{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T20:35:50Z","timestamp":1793133350742,"version":"4.5.0"},"reference-count":173,"publisher":"Elsevier BV","issue":"20","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:00:00Z","timestamp":1788998400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82192890"],"award-info":[{"award-number":["82192890"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["91959111"],"award-info":[{"award-number":["91959111"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82203318"],"award-info":[{"award-number":["82203318"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81822032"],"award-info":[{"award-number":["81822032"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32500508"],"award-info":[{"award-number":["32500508"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82192891"],"award-info":[{"award-number":["82192891"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81872027"],"award-info":[{"award-number":["81872027"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012458","name":"Chongqing Basic Science and Advanced Technology Research Program","doi-asserted-by":"publisher","award":["CSTB2024TIAD-KPX0028"],"award-info":[{"award-number":["CSTB2024TIAD-KPX0028"]}],"id":[{"id":"10.13039\/501100012458","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012458","name":"Chongqing Basic Science and Advanced Technology Research Program","doi-asserted-by":"publisher","award":["CSTB2023TIAD-STX0002"],"award-info":[{"award-number":["CSTB2023TIAD-STX0002"]}],"id":[{"id":"10.13039\/501100012458","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing Municipality","doi-asserted-by":"publisher","award":["CSTB2022NSCQ-MSX0880"],"award-info":[{"award-number":["CSTB2022NSCQ-MSX0880"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing Municipality","doi-asserted-by":"publisher","award":["CSTB2023NSCQ-LZX0156"],"award-info":[{"award-number":["CSTB2023NSCQ-LZX0156"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100015375","name":"The Affiliated Hospital of Southwest Medical University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100015375","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFC3402100"],"award-info":[{"award-number":["2023YFC3402100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFA1105300"],"award-info":[{"award-number":["2022YFA1105300"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Science Bulletin"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.scib.2026.09.016","type":"journal-article","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T23:32:59Z","timestamp":1788910379000},"page":"5349-5380","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"title":["Emerging advances in multimodal AI in cancer immunotherapy: from multi-scale data integration to clinical decision support"],"prefix":"10.1016","volume":"71","author":[{"given":"Wenjie","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeyu","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaijie","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuzhu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongying","family":"Hou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaotan","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xisong","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Miao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zongsheng","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiaoqiao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guohua","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiuwu","family":"Bian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.scib.2026.09.016_b0005","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1016\/S2468-1253(23)00147-4","article-title":"Immune checkpoint inhibitors in hepatocellular carcinoma: emerging challenges in clinical practice","volume":"8","author":"Pinter","year":"2023","journal-title":"Lancet Gastroenterol Hepatol"},{"key":"10.1016\/j.scib.2026.09.016_b0010","doi-asserted-by":"crossref","first-page":"e92","DOI":"10.1002\/mog2.92","article-title":"Immunotherapy for gastric cancer: advances and challenges","volume":"3","author":"Zhang","year":"2024","journal-title":"MedComm Oncol"},{"key":"10.1016\/j.scib.2026.09.016_b0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.eclinm.2024.102536","article-title":"Clinical spectrum and evolution of immune-checkpoint inhibitors toxicities over a decade-a worldwide perspective","volume":"70","author":"Gougis","year":"2024","journal-title":"EClinicalMedicine"},{"key":"10.1016\/j.scib.2026.09.016_b0020","article-title":"Stabilization of TGF-\u03b2 receptor 1 by a receptor-associated adaptor dictates feedback activation of the TGF-\u03b2 signaling pathway to maintain liver cancer stemness and drug resistance","volume":"11","author":"Liu","year":"2024","journal-title":"Adv Sci (Weinh)"},{"key":"10.1016\/j.scib.2026.09.016_b0025","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1038\/s41575-019-0126-x","article-title":"Immunotherapy in colorectal cancer: rationale, challenges and potential","volume":"16","author":"Ganesh","year":"2019","journal-title":"Nat Rev Gastroenterol Hepatol"},{"key":"10.1016\/j.scib.2026.09.016_b0030","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1158\/1078-0432.CCR-24-2436","article-title":"Intestinal subtype as a biomarker of response to neoadjuvant immunochemotherapy in locally advanced gastric adenocarcinoma: insights from a prospective phase II trial","volume":"31","author":"Wang","year":"2025","journal-title":"Clin Cancer Res"},{"key":"10.1016\/j.scib.2026.09.016_b0035","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.semcancer.2022.08.002","article-title":"Artificial intelligence-based prediction of clinical outcome in immunotherapy and targeted therapy of lung cancer","volume":"86","author":"Yin","year":"2022","journal-title":"Semin Cancer Biol"},{"key":"10.1016\/j.scib.2026.09.016_b0040","doi-asserted-by":"crossref","first-page":"e91","DOI":"10.1002\/mog2.91","article-title":"The application of the combination between artificial intelligence and endoscopy in gastrointestinal tumors","volume":"3","author":"Li","year":"2024","journal-title":"MedComm Oncol"},{"key":"10.1016\/j.scib.2026.09.016_b0045","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1186\/s13045-023-01456-y","article-title":"The artificial intelligence and machine learning in lung cancer immunotherapy","volume":"16","author":"Gao","year":"2023","journal-title":"J Hematol Oncol"},{"key":"10.1016\/j.scib.2026.09.016_b0050","doi-asserted-by":"crossref","DOI":"10.1136\/jitc-2022-005292","article-title":"Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy","volume":"10","author":"Dercle","year":"2022","journal-title":"J Immunother Cancer"},{"key":"10.1016\/j.scib.2026.09.016_b0055","doi-asserted-by":"crossref","DOI":"10.2147\/JMDH.S572089","article-title":"Artificial intelligence-enabled multi-omics for predicting immune checkpoint inhibitor response and resistance","volume":"19","author":"Wang","year":"2026","journal-title":"J Multidiscip Healthc"},{"key":"10.1016\/j.scib.2026.09.016_b0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.medp.2023.100002","article-title":"Targeting immunogenic cell death for cancer immunotherapy","volume":"1","author":"Zhang","year":"2024","journal-title":"Med Plus"},{"key":"10.1016\/j.scib.2026.09.016_b0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.canlet.2025.217589","article-title":"Neural-cancer crosstalk: reciprocal molecular circuits driving gastric tumorigenesis and emerging therapeutic opportunities","volume":"616","author":"Li","year":"2025","journal-title":"Cancer Lett"},{"key":"10.1016\/j.scib.2026.09.016_b0070","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1038\/s41392-025-02331-7","article-title":"Cancer cachexia: molecular basis and therapeutic advances","volume":"11","author":"Tan","year":"2026","journal-title":"Signal Transduct Target Ther"},{"key":"10.1016\/j.scib.2026.09.016_b0075","article-title":"Leveraging artificial intelligence and large language models for cancer immunotherapy","author":"Wu","year":"2026","journal-title":"Adv Sci (Weinh)"},{"key":"10.1016\/j.scib.2026.09.016_b0080","doi-asserted-by":"crossref","DOI":"10.1016\/j.canlet.2026.218257","article-title":"Programmable smart CAR-T design: a new paradigm in precision immunotherapy driven by logic gates, conditional activation, allogeneic strategies, and artificial intelligence","volume":"640","author":"Wen","year":"2026","journal-title":"Cancer Lett"},{"key":"10.1016\/j.scib.2026.09.016_b0085","article-title":"Helicobacter pylori-induced PPFIA4 orchestrates immune network-promoting gastritis and gastric bacterial colonization","volume":"136","author":"Wang","year":"2026","journal-title":"J Clin Invest"},{"key":"10.1016\/j.scib.2026.09.016_b0090","doi-asserted-by":"crossref","first-page":"2188","DOI":"10.1016\/j.immuni.2023.09.011","article-title":"The cancer-immunity cycle: Indication, genotype, and immunotype","volume":"56","author":"Mellman","year":"2023","journal-title":"Immunity"},{"key":"10.1016\/j.scib.2026.09.016_b0095","doi-asserted-by":"crossref","DOI":"10.3389\/fimmu.2023.1084887","article-title":"Towards a consensus definition of immune exclusion in cancer","volume":"14","author":"Tiwari","year":"2023","journal-title":"Front Immunol"},{"key":"10.1016\/j.scib.2026.09.016_b0100","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1038\/s41416-024-02887-1","article-title":"Cancer and treatment specific incidence rates of immune-related adverse events induced by immune checkpoint inhibitors: a systematic review","volume":"132","author":"Jayathilaka","year":"2025","journal-title":"Br J Cancer"},{"key":"10.1016\/j.scib.2026.09.016_b0105","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1056\/NEJMoa1709684","article-title":"Overall survival with combined nivolumab and ipilimumab in advanced melanoma","volume":"377","author":"Wolchok","year":"2017","journal-title":"N Engl J Med"},{"key":"10.1016\/j.scib.2026.09.016_b0110","doi-asserted-by":"crossref","DOI":"10.1016\/j.medp.2025.100087","article-title":"Transforming the landscape of cancer treatment with seven promising novel therapies: evolution and future perspectives","volume":"2","author":"Miao","year":"2025","journal-title":"Med Plus"},{"key":"10.1016\/j.scib.2026.09.016_b0115","doi-asserted-by":"crossref","first-page":"6753","DOI":"10.1038\/s41467-022-34275-9","article-title":"Deep learning-based image analysis predicts PD-L1 status from H&E-stained histopathology images in breast cancer","volume":"13","author":"Shamai","year":"2022","journal-title":"Nat Commun"},{"key":"10.1016\/j.scib.2026.09.016_b0120","doi-asserted-by":"crossref","DOI":"10.1136\/jitc-2023-008339","article-title":"Inflamed immune phenotype predicts favorable clinical outcomes of immune checkpoint inhibitor therapy across multiple cancer types","volume":"12","author":"Shen","year":"2024","journal-title":"J Immunother Cancer"},{"key":"10.1016\/j.scib.2026.09.016_b0125","first-page":"743","article-title":"A multi-omics-based serial deep learning approach to predict clinical outcomes of single-agent anti-PD-1\/PD-l1 immunotherapy in advanced stage non-small-cell lung cancer","volume":"13","author":"Yang","year":"2021","journal-title":"Am J Transl Res"},{"key":"10.1016\/j.scib.2026.09.016_b0130","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1001\/jamaoncol.2024.5356","article-title":"Deep learning model for predicting immunotherapy response in advanced non-small cell lung cancer","volume":"11","author":"Rakaee","year":"2025","journal-title":"JAMA Oncol"},{"key":"10.1016\/j.scib.2026.09.016_b0135","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1177\/2993091X251380150","article-title":"Development, validation, and silent trial of a predictive model for acute care use in cancer patients on immunotherapy","volume":"2","author":"Prizant","year":"2025","journal-title":"AI Precis Oncol"},{"key":"10.1016\/j.scib.2026.09.016_b0140","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1186\/s12911-021-01564-0","article-title":"Electronic patient-reported outcomes and machine learning in predicting immune-related adverse events of immune checkpoint inhibitor therapies","volume":"21","author":"Iivanainen","year":"2021","journal-title":"BMC Med Inform Decis Mak"},{"key":"10.1016\/j.scib.2026.09.016_b0145","doi-asserted-by":"crossref","first-page":"3703","DOI":"10.1038\/s41467-022-31535-6","article-title":"Network-based machine learning approach to predict immunotherapy response in cancer patients","volume":"13","author":"Kong","year":"2022","journal-title":"Nat Commun"},{"key":"10.1016\/j.scib.2026.09.016_b0150","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1007\/s10238-024-01324-0","article-title":"Multi-omics and artificial intelligence predict clinical outcomes of immunotherapy in non-small cell lung cancer patients","volume":"24","author":"Mei","year":"2024","journal-title":"Clin Exp Med"},{"key":"10.1016\/j.scib.2026.09.016_b0155","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1038\/s41592-018-0229-2","article-title":"Deep generative modeling for single-cell transcriptomics","volume":"15","author":"Lopez","year":"2018","journal-title":"Nat Methods"},{"key":"10.1016\/j.scib.2026.09.016_b0160","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1038\/s41592-021-01336-8","article-title":"Benchmarking atlas-level data integration in single-cell genomics","volume":"19","author":"Luecken","year":"2022","journal-title":"Nat Methods"},{"key":"10.1016\/j.scib.2026.09.016_b0165","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1038\/s41592-020-01050-x","article-title":"Joint probabilistic modeling of single-cell multi-omic data with totalvi","volume":"18","author":"Gayoso","year":"2021","journal-title":"Nat Methods"},{"key":"10.1016\/j.scib.2026.09.016_b0170","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1038\/s41592-019-0494-8","article-title":"Scgen predicts single-cell perturbation responses","volume":"16","author":"Lotfollahi","year":"2019","journal-title":"Nat Methods"},{"key":"10.1016\/j.scib.2026.09.016_b0175","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1038\/s42003-024-06380-6","article-title":"Heterotcr: a heterogeneous graph neural network-based method for predicting peptide-TCR interaction","volume":"7","author":"Yu","year":"2024","journal-title":"Commun Biol"},{"key":"10.1016\/j.scib.2026.09.016_b0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107137","article-title":"Deciphering ligand-receptor-mediated intercellular communication based on ensemble deep learning and the joint scoring strategy from single-cell transcriptomic data","volume":"163","author":"Peng","year":"2023","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.scib.2026.09.016_b0185","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1021\/acsomega.3c05863","article-title":"PLM-GAN: a large-scale protein loop modeling using pix2pix GAN","volume":"9","author":"Khalaf","year":"2024","journal-title":"ACS Omega"},{"key":"10.1016\/j.scib.2026.09.016_b0190","unstructured":"Kataria T, Knudsen B, Elhabian SY. Implicitstainer: data-efficient medical image translation for virtual antibody-based tissue staining using local implicit functions. arXiv:2505.09831."},{"key":"10.1016\/j.scib.2026.09.016_b0195","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1093\/bib\/bbad534","article-title":"Interpretable feature extraction and dimensionality reduction in ESM2 for protein localization prediction","volume":"25","author":"Luo","year":"2024","journal-title":"Brief Bioinform"},{"key":"10.1016\/j.scib.2026.09.016_b0200","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1016\/j.cels.2023.10.002","article-title":"Progen2: exploring the boundaries of protein language models","volume":"14","author":"Nijkamp","year":"2023","journal-title":"Cell Syst"},{"key":"10.1016\/j.scib.2026.09.016_b0205","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1038\/s42256-025-01002-0","article-title":"A unified deep framework for peptide\u2013major histocompatibility complex\u2013T cell receptor binding prediction","volume":"7","author":"Zhao","year":"2025","journal-title":"Nat Machine Intell"},{"key":"10.1016\/j.scib.2026.09.016_b0210","article-title":"Abimmpred: an immunogenicity prediction method for therapeutic antibodies using antiberty-based sequence features","volume":"19","author":"Wang","year":"2024","journal-title":"PLoS One"},{"key":"10.1016\/j.scib.2026.09.016_b0215","doi-asserted-by":"crossref","first-page":"2389","DOI":"10.1038\/s41467-023-38063-x","article-title":"Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies","volume":"14","author":"Ruffolo","year":"2023","journal-title":"Nat Commun"},{"key":"10.1016\/j.scib.2026.09.016_b0220","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1016\/j.cels.2023.10.001","article-title":"IGLM: infilling language modeling for antibody sequence design","volume":"14","author":"Shuai","year":"2023","journal-title":"Cell Syst"},{"key":"10.1016\/j.scib.2026.09.016_b0225","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1038\/s41586-023-06415-8","article-title":"De novo design of protein structure and function with rfdiffusion","volume":"620","author":"Watson","year":"2023","journal-title":"Nature"},{"key":"10.1016\/j.scib.2026.09.016_b0230","doi-asserted-by":"crossref","DOI":"10.1126\/science.adl2528","article-title":"Generalized biomolecular modeling and design with RosettaFold all-atom","volume":"384","author":"Krishna","year":"2024","journal-title":"Science"},{"key":"10.1016\/j.scib.2026.09.016_b0235","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1038\/s43018-022-00416-8","article-title":"Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(l)1 blockade in patients with non-small cell lung cancer","volume":"3","author":"Vanguri","year":"2022","journal-title":"Nat Cancer"},{"key":"10.1016\/j.scib.2026.09.016_b0240","unstructured":"Radford A, Kim JW, Hallacy C, et al. Learning transferable visual models from natural language supervision. In: Proceedings of the 38th international conference on machine learning. 2021;139:8748\u20138763."},{"key":"10.1016\/j.scib.2026.09.016_b0245","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","article-title":"Learning to exploit temporal structure for biomedical vision-language processing","author":"Bannur","year":"2023"},{"key":"10.1016\/j.scib.2026.09.016_b0250","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbac409","article-title":"BioGPT: generative pre-trained transformer for biomedical text generation and mining","volume":"23","author":"Luo","year":"2022","journal-title":"Brief Bioinform"},{"key":"10.1016\/j.scib.2026.09.016_b0255","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1016\/j.gpb.2023.03.004","article-title":"AB-Gen: antibody library design with generative pre-trained transformer and deep reinforcement learning","volume":"21","author":"Xu","year":"2023","journal-title":"Genom Proteom Bioinf"},{"key":"10.1016\/j.scib.2026.09.016_b0260","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1186\/s12859-022-04582-5","article-title":"Boosting tissue-specific prediction of active cis-regulatory regions through deep learning and Bayesian optimization techniques","volume":"23","author":"Cappelletti","year":"2022","journal-title":"BMC Bioinf"},{"key":"10.1016\/j.scib.2026.09.016_b0265","doi-asserted-by":"crossref","first-page":"2555","DOI":"10.3390\/diagnostics14121213","article-title":"Few-shot learning for medical image segmentation using 3D U-Net and model-agnostic meta-learning (MAML)","volume":"14","author":"Alsaleh","year":"2024","journal-title":"Diagnostics"},{"key":"10.1016\/j.scib.2026.09.016_b0270","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1038\/s43018-020-00169-2","article-title":"Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients","volume":"2","author":"Ma","year":"2021","journal-title":"Nat Cancer"},{"key":"10.1016\/j.scib.2026.09.016_b0275","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1038\/s42256-026-01236-6","article-title":"Reusability report: meta-learning for antigen-specific T cell receptor binder identification","volume":"8","author":"Xu","year":"2026","journal-title":"Nat Mach Intell"},{"key":"10.1016\/j.scib.2026.09.016_b0280","article-title":"Meta learning for mutant HLA class I epitope immunogenicity prediction to accelerate cancer clinical immunotherapy","volume":"26","author":"Xu","year":"2025","journal-title":"Brief Bioinform"},{"key":"10.1016\/j.scib.2026.09.016_b0285","doi-asserted-by":"crossref","first-page":"7045","DOI":"10.1016\/j.cell.2024.11.015","article-title":"How to build the virtual cell with artificial intelligence: priorities and opportunities","volume":"187","author":"Bunne","year":"2024","journal-title":"Cell"},{"key":"10.1016\/j.scib.2026.09.016_b0290","doi-asserted-by":"crossref","unstructured":"Rizvi S, Levine D, Patel A, et al. Scaling large language models for next-generation single-cell analysis. bioRxiv 2025;2025.04.14.648850.","DOI":"10.1101\/2025.04.14.648850"},{"key":"10.1016\/j.scib.2026.09.016_b0295","doi-asserted-by":"crossref","unstructured":"Adduri AK, Gautam D, Bevilacqua B, et al. Predicting cellular responses to perturbation across diverse contexts with State. bioRxiv 2025;2025.06.26.661135.","DOI":"10.1101\/2025.06.26.661135"},{"key":"10.1016\/j.scib.2026.09.016_b0300","doi-asserted-by":"crossref","DOI":"10.1016\/j.medp.2024.100027","article-title":"Health research in the era of artificial intelligence: advances in gene-editing study","volume":"1","author":"Dai","year":"2024","journal-title":"Med Plus"},{"key":"10.1016\/j.scib.2026.09.016_b0305","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1093\/bioinformatics\/btt755","article-title":"MSIsensor: microsatellite instability detection using paired tumor-normal sequence data","volume":"30","author":"Niu","year":"2014","journal-title":"Bioinformatics"},{"key":"10.1016\/j.scib.2026.09.016_b0310","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.gpb.2020.02.001","article-title":"MSIsensor-pro: fast, accurate, and matched-normal-sample-free detection of microsatellite instability","volume":"18","author":"Jia","year":"2020","journal-title":"Genom Proteom Bioinform"},{"key":"10.1016\/j.scib.2026.09.016_b0315","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaa402","article-title":"MSIsensor-ct: microsatellite instability detection using cfDNA sequencing data","volume":"22","author":"Han","year":"2021","journal-title":"Brief Bioinform"},{"key":"10.1016\/j.scib.2026.09.016_b0320","doi-asserted-by":"crossref","DOI":"10.1093\/gpbjnl\/qzae004","article-title":"MSIsensor-RNA: microsatellite instability detection for bulk and single-cell gene expression data","volume":"22","author":"Jia","year":"2024","journal-title":"Genom Proteom Bioinform"},{"key":"10.1016\/j.scib.2026.09.016_b0325","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1038\/s41467-024-54970-z","article-title":"A deep multiple instance learning framework improves microsatellite instability detection from tumor next generation sequencing","volume":"16","author":"Ziegler","year":"2025","journal-title":"Nat Commun"},{"key":"10.1016\/j.scib.2026.09.016_b0330","article-title":"TMBserval: a statistical explainable learning model reveals weighted tumor mutation burden better categorizing therapeutic benefits","volume":"14","author":"Wang","year":"2023","journal-title":"Front Immunol"},{"key":"10.1016\/j.scib.2026.09.016_b0335","doi-asserted-by":"crossref","first-page":"7557","DOI":"10.1021\/acs.jcim.3c01293","article-title":"GGNpTCR: a generative graph structure neural network for predicting immunogenic peptides for T-cell immune response","volume":"63","author":"Zhao","year":"2023","journal-title":"J Chem Inf Model"},{"key":"10.1016\/j.scib.2026.09.016_b0340","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.ejca.2022.05.021","article-title":"Human leucocyte antigen genotype association with the development of immune-related adverse events in patients with non-small cell lung cancer treated with single agent immunotherapy","volume":"172","author":"Abed","year":"2022","journal-title":"Eur J Cancer"},{"key":"10.1016\/j.scib.2026.09.016_b0345","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1038\/s41389-023-00482-2","article-title":"Deep learning identifies a T-cell exhaustion-dependent transcriptional signature for predicting clinical outcomes and response to immune checkpoint blockade","volume":"12","author":"Zhang","year":"2023","journal-title":"Oncogenesis"},{"key":"10.1016\/j.scib.2026.09.016_b0350","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1038\/s41598-023-51108-x","article-title":"Immune, metabolic landscapes of prognostic signatures for lung adenocarcinoma based on a novel deep learning framework","volume":"14","author":"Qin","year":"2024","journal-title":"Sci Rep"},{"key":"10.1016\/j.scib.2026.09.016_b0355","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1186\/s40425-017-0272-z","article-title":"Whole-blood RNA transcript-based models can predict clinical response in two large independent clinical studies of patients with advanced melanoma treated with the checkpoint inhibitor, tremelimumab","volume":"5","author":"Friedlander","year":"2017","journal-title":"J Immunother Cancer"},{"key":"10.1016\/j.scib.2026.09.016_b0360","doi-asserted-by":"crossref","DOI":"10.1016\/j.medp.2024.100043","article-title":"Artificial intelligence-driven health research innovations: protein sciences","volume":"1","author":"Liu","year":"2024","journal-title":"Med Plus"},{"key":"10.1016\/j.scib.2026.09.016_b0365","doi-asserted-by":"crossref","first-page":"3116","DOI":"10.3390\/cancers13133116","article-title":"Plasma proteomic analysis in non-small cell lung cancer patients treated with PD-1\/PD-L1 blockade","volume":"13","author":"Eltahir","year":"2021","journal-title":"Cancers"},{"key":"10.1016\/j.scib.2026.09.016_b0370","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1038\/s42003-023-04927-7","article-title":"ImmuneBuilder: deep-learning models for predicting the structures of immune proteins","volume":"6","author":"Abanades","year":"2023","journal-title":"Commun Biol"},{"key":"10.1016\/j.scib.2026.09.016_b0375","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaf321","article-title":"pMHChat, characterizing the interactions between major histocompatibility complex class II molecules and peptides with large language models and deep hypergraph learning","volume":"26","author":"Ma","year":"2025","journal-title":"Brief Bioinform"},{"key":"10.1016\/j.scib.2026.09.016_b0380","article-title":"Identification of protein biomarker candidates associated with organ-specific immune-related toxicity and response to management by plasma proteomics","volume":"4","author":"Kverneland","year":"2025","journal-title":"BMJ Oncol"},{"key":"10.1016\/j.scib.2026.09.016_b0385","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1016\/S2213-2600(18)30284-4","article-title":"Epigenetic prediction of response to anti-PD-1 treatment in non-small-cell lung cancer: a multicentre, retrospective analysis","volume":"6","author":"Duruisseaux","year":"2018","journal-title":"Lancet Respir Med"},{"key":"10.1016\/j.scib.2026.09.016_b0390","doi-asserted-by":"crossref","DOI":"10.3389\/fgene.2021.676449","article-title":"MeImmS: predict clinical benefit of anti-PD-1\/PD-L1 treatments based on DNA methylation in non-small cell lung cancer","volume":"12","author":"Shang","year":"2021","journal-title":"Front Genet"},{"key":"10.1016\/j.scib.2026.09.016_b0395","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1038\/cr.2017.121","article-title":"5-hydroxymethylcytosine signatures in circulating cell-free DNA as diagnostic biomarkers for human cancers","volume":"27","author":"Li","year":"2017","journal-title":"Cell Res"},{"key":"10.1016\/j.scib.2026.09.016_b0400","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.ejca.2021.03.005","article-title":"Applications of single-cell and bulk RNA sequencing in onco-immunology","volume":"149","author":"Kuksin","year":"2021","journal-title":"Eur J Cancer"},{"key":"10.1016\/j.scib.2026.09.016_b0405","article-title":"Exploring the immune escape mechanisms in gastric cancer patients based on the deep AI algorithms and single-cell sequencing analysis","volume":"28","author":"Chen","year":"2024","journal-title":"J Cell Mol Med"},{"key":"10.1016\/j.scib.2026.09.016_b0410","doi-asserted-by":"crossref","DOI":"10.1016\/j.tranon.2025.102459","article-title":"Machine learning-guided single-cell multiomics uncovers GDF15-driven immunosuppressive niches in NSCLC: a translational framework for overcoming anti-PD-1 resistance","volume":"59","author":"Zhang","year":"2025","journal-title":"Transl Oncol"},{"key":"10.1016\/j.scib.2026.09.016_b0415","doi-asserted-by":"crossref","DOI":"10.3389\/fgene.2021.806457","article-title":"Machine learning of single cell transcriptomic data from anti-PD-1 responders and non-responders reveals distinct resistance mechanisms in skin cancers and PDAC","volume":"12","author":"Liu","year":"2022","journal-title":"Front Genet"},{"key":"10.1016\/j.scib.2026.09.016_b0420","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1038\/s41698-025-00883-z","article-title":"Uncovering gene and cellular signatures of immune checkpoint response via machine learning and single-cell RNA-seq","volume":"9","author":"Pinhasi","year":"2025","journal-title":"NPJ Precis Oncol"},{"key":"10.1016\/j.scib.2026.09.016_b0425","doi-asserted-by":"crossref","DOI":"10.1136\/jitc-2025-011636","article-title":"Single-cell RNA sequencing of baseline PBMCs predicts ICI efficacy and irAE severity in patients with NSCLC","volume":"13","author":"Kim","year":"2025","journal-title":"J Immunother Cancer"},{"key":"10.1016\/j.scib.2026.09.016_b0430","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1186\/s12967-024-06007-8","article-title":"Spatial transcriptome reveals histology-correlated immune signature learnt by deep learning attention mechanism on H&E-stained images for ovarian cancer prognosis","volume":"23","author":"Ng","year":"2025","journal-title":"J Transl Med"},{"key":"10.1016\/j.scib.2026.09.016_b0435","doi-asserted-by":"crossref","first-page":"1292","DOI":"10.1038\/s42256-025-01074-y","article-title":"Histopathology-based protein multiplex generation using deep learning","volume":"7","author":"Andani","year":"2025","journal-title":"Nat Mach Intell"},{"key":"10.1016\/j.scib.2026.09.016_b0440","doi-asserted-by":"crossref","DOI":"10.1016\/j.modpat.2024.100652","article-title":"Spatial architecture of single-cell and vasculature in tumor microenvironment predicts clinical outcomes in triple-negative breast cancer","volume":"38","author":"Mi","year":"2025","journal-title":"Mod Pathol"},{"key":"10.1016\/j.scib.2026.09.016_b0445","doi-asserted-by":"crossref","first-page":"3063","DOI":"10.1038\/s41467-024-46764-0","article-title":"Teacher-student collaborated multiple instance learning for pan-cancer PDL1 expression prediction from histopathology slides","volume":"15","author":"Jin","year":"2024","journal-title":"Nat Commun"},{"key":"10.1016\/j.scib.2026.09.016_b0450","doi-asserted-by":"crossref","DOI":"10.7717\/peerj.19201","article-title":"Multiple instance learning-based prediction of programmed death-ligand 1 (PD-L1) expression from hematoxylin and eosin (H&E)-stained histopathological images in breast cancer","volume":"13","author":"Da","year":"2025","journal-title":"PeerJ"},{"key":"10.1016\/j.scib.2026.09.016_b0455","doi-asserted-by":"crossref","first-page":"11080","DOI":"10.7150\/thno.49864","article-title":"Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in colorectal cancer","volume":"10","author":"Cao","year":"2020","journal-title":"Theranostics"},{"key":"10.1016\/j.scib.2026.09.016_b0460","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1038\/s41698-025-01094-2","article-title":"Deepath-MSI: a clinic-ready deep learning model for microsatellite instability detection in colorectal cancer using whole-slide imaging","volume":"9","author":"Feng","year":"2025","journal-title":"npj Precis Oncol"},{"key":"10.1016\/j.scib.2026.09.016_b0465","doi-asserted-by":"crossref","DOI":"10.1016\/j.jpi.2022.100105","article-title":"Spatial heterogeneity and organization of tumor mutation burden with immune infiltrates within tumors based on whole slide images correlated with patient survival in bladder cancer","volume":"13","author":"Xu","year":"2022","journal-title":"J Pathol Inform"},{"key":"10.1016\/j.scib.2026.09.016_b0470","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1038\/s41698-024-00766-9","article-title":"Deep learning for endometrial cancer subtyping and predicting tumor mutational burden from histopathological slides","volume":"8","author":"Wang","year":"2024","journal-title":"npj Precis Oncol"},{"key":"10.1016\/j.scib.2026.09.016_b0475","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103372","article-title":"Ensemble transformer-based multiple instance learning to predict pathological subtypes and tumor mutational burden from histopathological whole slide images of endometrial and colorectal cancer","volume":"99","author":"Wang","year":"2025","journal-title":"Med Image Anal"},{"key":"10.1016\/j.scib.2026.09.016_b0480","doi-asserted-by":"crossref","DOI":"10.1016\/j.modpat.2023.100208","article-title":"Outcome-supervised deep learning on pathologic whole slide images for survival prediction of immunotherapy in patients with non-small cell lung cancer","volume":"36","author":"Li","year":"2023","journal-title":"Mod Pathol"},{"key":"10.1016\/j.scib.2026.09.016_b0485","doi-asserted-by":"crossref","DOI":"10.1136\/jitc-2024-008927","article-title":"Development and interpretation of a pathomics-driven ensemble model for predicting the response to immunotherapy in gastric cancer","volume":"12","author":"Han","year":"2024","journal-title":"J Immunother Cancer"},{"key":"10.1016\/j.scib.2026.09.016_b0490","doi-asserted-by":"crossref","first-page":"1833","DOI":"10.1038\/s41416-024-02856-8","article-title":"Deep learning analysis of histopathological images predicts immunotherapy prognosis and reveals tumour microenvironment features in non-small cell lung cancer","volume":"131","author":"Wang","year":"2024","journal-title":"Br J Cancer"},{"key":"10.1016\/j.scib.2026.09.016_b0495","doi-asserted-by":"crossref","DOI":"10.1136\/jitc-2020-002118","article-title":"Non-invasive measurement of PD-L1 status and prediction of immunotherapy response using deep learning of PET\/CT images","volume":"9","author":"Mu","year":"2021","journal-title":"J Immunother Cancer"},{"key":"10.1016\/j.scib.2026.09.016_b0500","doi-asserted-by":"crossref","DOI":"10.1136\/jitc-2020-000550","article-title":"Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker","volume":"8","author":"He","year":"2020","journal-title":"J Immunother Cancer"},{"key":"10.1016\/j.scib.2026.09.016_b0505","article-title":"Voxel-level radiomics and deep learning for predicting pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant immunotherapy and chemotherapy","volume":"13","author":"Zhang","year":"2025","journal-title":"J Immunother Cancer"},{"key":"10.1016\/j.scib.2026.09.016_b0510","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1007\/s10637-017-0524-2","article-title":"Radiomics to predict immunotherapy-induced pneumonitis: proof of concept","volume":"36","author":"Colen","year":"2018","journal-title":"Invest New Drugs"},{"key":"10.1016\/j.scib.2026.09.016_b0515","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.1016\/j.acra.2024.09.053","article-title":"Radiomics biomarkers to predict checkpoint inhibitor pneumonitis in non-small cell lung cancer","volume":"32","author":"Du","year":"2025","journal-title":"Acad Radiol"},{"key":"10.1016\/j.scib.2026.09.016_b0520","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1186\/s12967-024-06057-y","article-title":"Habitat radiomics analysis for progression free survival and immune-related adverse reaction prediction in non-small cell lung cancer treated by immunotherapy","volume":"23","author":"Wu","year":"2025","journal-title":"J Transl Med"},{"key":"10.1016\/j.scib.2026.09.016_b0525","doi-asserted-by":"crossref","DOI":"10.1136\/esmoopen-2020-000910","article-title":"Machine learning: an approach to preoperatively predict PD-1\/PD-L1 expression and outcome in intrahepatic cholangiocarcinoma using MRI biomarkers","volume":"5","author":"Zhang","year":"2020","journal-title":"ESMO Open"},{"key":"10.1016\/j.scib.2026.09.016_b0530","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1007\/s11060-023-04367-7","article-title":"Radiomics for the non-invasive prediction of PD-L1 expression in patients with brain metastases secondary to non-small cell lung cancer","volume":"163","author":"Mei\u00dfner","year":"2023","journal-title":"J Neurooncol"},{"key":"10.1016\/j.scib.2026.09.016_b0535","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1186\/s40644-025-00837-5","article-title":"Development and validation of MRI-derived deep learning score for non-invasive prediction of PD-L1 expression and prognostic stratification in head and neck squamous cell carcinoma","volume":"25","author":"Ding","year":"2025","journal-title":"Cancer Imaging"},{"key":"10.1016\/j.scib.2026.09.016_b0540","doi-asserted-by":"crossref","first-page":"3492","DOI":"10.3390\/cancers14143492","article-title":"A radiomics-based machine learning model for prediction of tumor mutational burden in lower-grade gliomas","volume":"14","author":"Lam","year":"2022","journal-title":"Cancers"},{"key":"10.1016\/j.scib.2026.09.016_b0545","doi-asserted-by":"crossref","first-page":"3975","DOI":"10.1016\/j.acra.2025.02.009","article-title":"Multi-parametric MRI habitat radiomics based on interpretable machine learning for preoperative assessment of microsatellite instability in rectal cancer","volume":"32","author":"Wang","year":"2025","journal-title":"Acad Radiol"},{"key":"10.1016\/j.scib.2026.09.016_b0550","doi-asserted-by":"crossref","first-page":"4310","DOI":"10.1097\/JS9.0000000000001335","article-title":"Multiparametric MRI subregion radiomics for preoperative assessment of high-risk subregions in microsatellite instability of rectal cancer patients: a multicenter study","volume":"110","author":"Cai","year":"2024","journal-title":"Int J Surg"},{"key":"10.1016\/j.scib.2026.09.016_b0555","doi-asserted-by":"crossref","first-page":"11760","DOI":"10.1038\/s41598-024-62584-0","article-title":"A combinatorial MRI sequence-based radiomics model for preoperative prediction of microsatellite instability status in rectal cancer","volume":"14","author":"Xing","year":"2024","journal-title":"Sci Rep"},{"key":"10.1016\/j.scib.2026.09.016_b0560","doi-asserted-by":"crossref","DOI":"10.2214\/AJR.25.33178","article-title":"Baseline and early treatment MRI model for predicting complete pathologic response to neoadjuvant chemoimmunotherapy in patients with triple-negative breast cancer","volume":"225","author":"Liu","year":"2025","journal-title":"AJR Am J Roentgenol"},{"key":"10.1016\/j.scib.2026.09.016_b0565","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1038\/s41467-024-55659-z","article-title":"Multiparametric MRI along with machine learning predicts prognosis and treatment response in pediatric low-grade glioma","volume":"16","author":"Fathi Kazerooni","year":"2025","journal-title":"Nat Commun"},{"key":"10.1016\/j.scib.2026.09.016_b0570","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s43856-024-00634-4","article-title":"A convolutional attention model for predicting response to chemo-immunotherapy from ultrasound elastography in mouse tumor models","volume":"4","author":"Voutouri","year":"2024","journal-title":"Commun Med"},{"key":"10.1016\/j.scib.2026.09.016_b0575","article-title":"Multimodal deep learning for predicting PD-L1 biomarker and clinical immunotherapy outcomes of esophageal cancer","volume":"16","author":"Liu","year":"2025","journal-title":"Front Immunol"},{"key":"10.1016\/j.scib.2026.09.016_b0580","doi-asserted-by":"crossref","DOI":"10.3389\/fimmu.2025.1530279","article-title":"A preoperative predictive model based on multi-modal features to predict pathological complete response after neoadjuvant chemoimmunotherapy in esophageal cancer patients","volume":"16","author":"Qi","year":"2025","journal-title":"Front Immunol"},{"key":"10.1016\/j.scib.2026.09.016_b0585","doi-asserted-by":"crossref","DOI":"10.1016\/j.radonc.2023.109793","article-title":"Cancer immunotherapy response prediction from multi-modal clinical and image data using semi-supervised deep learning","volume":"186","author":"Wang","year":"2023","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.scib.2026.09.016_b0590","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1038\/s41392-024-01932-y","article-title":"Predicting gastric cancer response to anti-HER2 therapy or anti-HER2 combined immunotherapy based on multi-modal data","volume":"9","author":"Chen","year":"2024","journal-title":"Signal Transduct Target Ther"},{"key":"10.1016\/j.scib.2026.09.016_b0595","doi-asserted-by":"crossref","first-page":"3342","DOI":"10.1097\/JS9.0000000000002322","article-title":"A deep learning-based clinical-radiomics model predicting the treatment response of immune checkpoint inhibitors (ICIs)-based conversion therapy in potentially convertible hepatocelluar carcinoma patients: a tumor marker prognostic study","volume":"111","author":"Lin","year":"2025","journal-title":"Int J Surg"},{"key":"10.1016\/j.scib.2026.09.016_b0600","doi-asserted-by":"crossref","DOI":"10.1016\/j.ebiom.2023.104644","article-title":"Deep learning-based scoring of tumour-infiltrating lymphocytes is prognostic in primary melanoma and predictive to PD-1 checkpoint inhibition in melanoma metastases","volume":"93","author":"Chatziioannou","year":"2023","journal-title":"EBioMedicine"},{"key":"10.1016\/j.scib.2026.09.016_b0605","article-title":"AI-driven multimodal algorithm predicts immunotherapy and targeted therapy outcomes in clear cell renal cell carcinoma","volume":"6","author":"Stupichev","year":"2025","journal-title":"Cell Rep Med"},{"key":"10.1016\/j.scib.2026.09.016_b0610","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1038\/s41467-025-55847-5","article-title":"Integration of clinical, pathological, radiological, and transcriptomic data improves prediction for first-line immunotherapy outcome in metastatic non-small cell lung cancer","volume":"16","author":"Captier","year":"2025","journal-title":"Nat Commun"},{"key":"10.1016\/j.scib.2026.09.016_b0615","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1186\/s13244-024-01784-8","article-title":"CT-based multimodal deep learning for non-invasive overall survival prediction in advanced hepatocellular carcinoma patients treated with immunotherapy","volume":"15","author":"Xia","year":"2024","journal-title":"Insights Imaging"},{"key":"10.1016\/j.scib.2026.09.016_b0620","doi-asserted-by":"crossref","first-page":"3397","DOI":"10.1016\/j.acra.2025.01.046","article-title":"CT-based deep learning predicts prognosis in esophageal squamous cell cancer patients receiving immunotherapy combined with chemotherapy","volume":"32","author":"Huang","year":"2025","journal-title":"Acad Radiol"},{"key":"10.1016\/j.scib.2026.09.016_b0625","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1093\/jamia\/ocaf074","article-title":"Multimodal integration of longitudinal noninvasive diagnostics for survival prediction in immunotherapy using deep learning","volume":"32","author":"Yeghaian","year":"2025","journal-title":"J Am Med Inform Assoc"},{"key":"10.1016\/j.scib.2026.09.016_b0630","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1200\/EDBK_200643","article-title":"Patterns of response and progression to immunotherapy","volume":"38","author":"Borcoman","year":"2018","journal-title":"Am Soc Clin Oncol Educ Book"},{"key":"10.1016\/j.scib.2026.09.016_b0635","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.3348\/kjr.2022.0225","article-title":"Evolution of radiological treatment response assessments for cancer immunotherapy: From iRECIST to radiomics and artificial intelligence","volume":"23","author":"Kim","year":"2022","journal-title":"Korean J Radiol"},{"key":"10.1016\/j.scib.2026.09.016_b0640","doi-asserted-by":"crossref","first-page":"e143","DOI":"10.1016\/S1470-2045(17)30074-8","article-title":"iRECIST: guidelines for response criteria for use in trials testing immunotherapeutics","volume":"18","author":"Seymour","year":"2017","journal-title":"Lancet Oncol"},{"key":"10.1016\/j.scib.2026.09.016_b0645","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.ejca.2016.03.082","article-title":"RECIST 1.1\u2014standardisation and disease-specific adaptations: perspectives from the RECIST Working Group","volume":"62","author":"Schwartz","year":"2016","journal-title":"Eur J Cancer"},{"key":"10.1016\/j.scib.2026.09.016_b0650","doi-asserted-by":"crossref","first-page":"808","DOI":"10.3390\/cancers18050808","article-title":"Artificial intelligence for RECIST-based radiologic treatment response assessment in solid tumors: a systematic review of imaging- and report-derived approaches","volume":"18","author":"Leszczy\u0144ska","year":"2026","journal-title":"Cancers"},{"key":"10.1016\/j.scib.2026.09.016_b0655","doi-asserted-by":"crossref","first-page":"4414","DOI":"10.1158\/1078-0432.CCR-20-0020","article-title":"Radiomics, tumor volume, and blood biomarkers for early prediction of pseudoprogression in patients with metastatic melanoma treated with immune checkpoint inhibition","volume":"26","author":"Basler","year":"2020","journal-title":"Clin Cancer Res"},{"key":"10.1016\/j.scib.2026.09.016_b0660","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2021.729371","article-title":"CT-based peritumoral and intratumoral radiomics as pretreatment predictors of atypical responses to immune checkpoint inhibitor across tumor types: a preliminary multicenter study","volume":"11","author":"He","year":"2021","journal-title":"Front Oncol"},{"key":"10.1016\/j.scib.2026.09.016_b0665","doi-asserted-by":"crossref","DOI":"10.1136\/jitc-2020-001343","article-title":"Novel, non-invasive imaging approach to identify patients with advanced non-small cell lung cancer at risk of hyperprogressive disease with immune checkpoint blockade","volume":"8","author":"Vaidya","year":"2020","journal-title":"J Immunother Cancer"},{"key":"10.1016\/j.scib.2026.09.016_b0670","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.lungcan.2019.01.010","article-title":"Novel clinical and radiomic predictors of rapid disease progression phenotypes among lung cancer patients treated with immunotherapy: an early report","volume":"129","author":"Tunali","year":"2019","journal-title":"Lung Cancer"},{"key":"10.1016\/j.scib.2026.09.016_b0675","doi-asserted-by":"crossref","first-page":"1524","DOI":"10.3389\/fonc.2020.01524","article-title":"Pilot study of CT-based radiomics model for early evaluation of response to immunotherapy in patients with metastatic melanoma","volume":"10","author":"Wang","year":"2020","journal-title":"Front Oncol"},{"key":"10.1016\/j.scib.2026.09.016_b0680","doi-asserted-by":"crossref","DOI":"10.3389\/fimmu.2024.1492399","article-title":"Multimodal data deep learning method for predicting symptomatic pneumonitis caused by lung cancer radiotherapy combined with immunotherapy","volume":"15","author":"Yang","year":"2025","journal-title":"Front Immunol"},{"key":"10.1016\/j.scib.2026.09.016_b0685","article-title":"Elucidation of the application of blood test biomarkers to predict immune-related adverse events in atezolizumab-treated NSCLC patients using machine learning methods","volume":"13","author":"Zhou","year":"2022","journal-title":"Front Immunol"},{"key":"10.1016\/j.scib.2026.09.016_b0690","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1186\/s43556-025-00291-6","article-title":"Liquid biopsy using plasma proteomics in predicting efficacy and tolerance of PD-1\/PD-L1 blockades in NSCLC: a prospective exploratory study","volume":"6","author":"Gao","year":"2025","journal-title":"Mol Biomed"},{"key":"10.1016\/j.scib.2026.09.016_b0695","doi-asserted-by":"crossref","DOI":"10.1136\/jitc-2023-008599","article-title":"Use of artificial intelligence chatbots in clinical management of immune-related adverse events","volume":"12","author":"Burnette","year":"2024","journal-title":"J Immunother Cancer"},{"key":"10.1016\/j.scib.2026.09.016_b0700","doi-asserted-by":"crossref","first-page":"4134","DOI":"10.1200\/JCO.24.00326","article-title":"Enhancing precision in detecting severe immune-related adverse events: comparative analysis of large language models and International Classification of Disease codes in patient records","volume":"42","author":"Sun","year":"2024","journal-title":"J Clin Oncol"},{"key":"10.1016\/j.scib.2026.09.016_b0705","doi-asserted-by":"crossref","DOI":"10.1136\/jitc-2023-006833","article-title":"PEOPLE (NTC03447678), a phase II trial to test pembrolizumab as first-line treatment in patients with advanced NSCLC with PD-L1 <50%: a multiomics analysis","volume":"11","author":"Lo Russo","year":"2023","journal-title":"J Immunother Cancer"},{"key":"10.1016\/j.scib.2026.09.016_b0710","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2025.111948","article-title":"Prediction of treatment response and outcome of transarterial chemoembolization in patients with hepatocellular carcinoma using artificial intelligence: a systematic review of efficacy","volume":"184","author":"Keshavarz","year":"2025","journal-title":"Eur J Radiol"},{"key":"10.1016\/j.scib.2026.09.016_b0715","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1186\/s12880-021-00587-3","article-title":"Integrating No.3 lymph nodes and primary tumor radiomics to predict lymph node metastasis in T1-2 gastric cancer","volume":"21","author":"Wang","year":"2021","journal-title":"BMC Med Imaging"},{"key":"10.1016\/j.scib.2026.09.016_b0720","doi-asserted-by":"crossref","first-page":"1753","DOI":"10.3390\/biomedicines12081753","article-title":"Applications of multimodal artificial intelligence in non-Hodgkin lymphoma B cells","volume":"12","author":"Isavand","year":"2024","journal-title":"Biomedicines"},{"key":"10.1016\/j.scib.2026.09.016_b0725","doi-asserted-by":"crossref","first-page":"2376","DOI":"10.1158\/0008-5472.CAN-24-2553","article-title":"Leveraging artificial intelligence for neoantigen prediction","volume":"85","author":"Zeng","year":"2025","journal-title":"Cancer Res"},{"key":"10.1016\/j.scib.2026.09.016_b0730","doi-asserted-by":"crossref","first-page":"3355","DOI":"10.1016\/j.apsb.2025.05.009","article-title":"Artificial intelligence and anti-cancer drugs\u2019 response","volume":"15","author":"Long","year":"2025","journal-title":"Acta Pharm Sin B"},{"key":"10.1016\/j.scib.2026.09.016_b0735","doi-asserted-by":"crossref","first-page":"1344","DOI":"10.1002\/cac2.70053","article-title":"Harnessing chimeric antigen receptor macrophages against solid tumors","volume":"45","author":"Wang","year":"2025","journal-title":"Cancer Commun (Lond)"},{"key":"10.1016\/j.scib.2026.09.016_b0740","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1158\/2643-3230.BCD-23-0240","article-title":"Advancing CAR T-cell therapies with artificial intelligence: Opportunities and challenges","volume":"6","author":"Luciani","year":"2025","journal-title":"Blood Cancer Discov"},{"key":"10.1016\/j.scib.2026.09.016_b0745","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaf268","article-title":"scATD: A high-throughput and interpretable framework for single-cell cancer drug resistance prediction and biomarker identification","volume":"26","author":"Zhou","year":"2025","journal-title":"Brief Bioinform"},{"key":"10.1016\/j.scib.2026.09.016_b0750","article-title":"Large language models transform biological research: From architecture to utilization","volume":"68","author":"Wang","year":"2025","journal-title":"Sci China Inf Sci"},{"key":"10.1016\/j.scib.2026.09.016_b0755","doi-asserted-by":"crossref","DOI":"10.1016\/j.medp.2024.100030","article-title":"Large language models illuminate a progressive pathway to artificial intelligent healthcare assistant","volume":"1","author":"Yuan","year":"2024","journal-title":"Med Plus"},{"key":"10.1016\/j.scib.2026.09.016_b0760","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1053\/j.gastro.2025.01.253","article-title":"Artificial intelligence applications in image-based diagnosis of early esophageal and gastric neoplasms","volume":"169","author":"Ebigbo","year":"2025","journal-title":"Gastroenterology"},{"key":"10.1016\/j.scib.2026.09.016_b0765","doi-asserted-by":"crossref","DOI":"10.1016\/j.modpat.2024.100609","article-title":"Regulatory aspects of artificial intelligence and machine learning","volume":"37","author":"Pantanowitz","year":"2024","journal-title":"Mod Pathol"},{"key":"10.1016\/j.scib.2026.09.016_b0770","doi-asserted-by":"crossref","first-page":"904","DOI":"10.1186\/s12982-025-01277-z","article-title":"Legal, ethical, and policy challenges of artificial intelligence translation tools in healthcare","volume":"22","author":"van Kolfschooten","year":"2025","journal-title":"Discov Public Health"},{"key":"10.1016\/j.scib.2026.09.016_b0775","doi-asserted-by":"crossref","first-page":"77","DOI":"10.14797\/mdcvj.1290","article-title":"ChatGPT and artificial intelligence in hospital level research: Potential, precautions, and prospects","volume":"19","author":"Arshad","year":"2023","journal-title":"Methodist Debakey Cardiovasc J"},{"key":"10.1016\/j.scib.2026.09.016_b0780","doi-asserted-by":"crossref","DOI":"10.1016\/j.medp.2024.100042","article-title":"Translating \u201cAI for omics\u201d into precision therapy","volume":"1","author":"Liu","year":"2024","journal-title":"Med Plus"},{"key":"10.1016\/j.scib.2026.09.016_b0785","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/s41746-025-02198-6","article-title":"AI-driven virtual cell models in preclinical research: Technical pathways, validation mechanisms, and clinical translation potential","volume":"9","author":"Ma","year":"2026","journal-title":"npj Digit Med"},{"key":"10.1016\/j.scib.2026.09.016_b0790","first-page":"27299","article-title":"Cell2Sentence: teaching large language models the language of biology","volume":"235","author":"Levine","year":"2024","journal-title":"Proc Mach Learn Res"},{"key":"10.1016\/j.scib.2026.09.016_b0795","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/j.cell.2025.11.016","article-title":"Multimodal AI generates virtual population for tumor microenvironment modeling","volume":"189","author":"Valanarasu","year":"2026","journal-title":"Cell"},{"key":"10.1016\/j.scib.2026.09.016_b0800","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbag104","article-title":"Artificial intelligence-enabled multi-scale virtual cell: perspective, challenges, and opportunities","volume":"27","author":"Jiang","year":"2026","journal-title":"Brief Bioinform"},{"key":"10.1016\/j.scib.2026.09.016_b0805","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1038\/s41422-025-01101-y","article-title":"Grow AI virtual cells: three data pillars and closed-loop learning","volume":"35","author":"Qian","year":"2025","journal-title":"Cell Res"},{"key":"10.1016\/j.scib.2026.09.016_b0810","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1080\/17460441.2023.2273839","article-title":"Generative artificial intelligence empowers digital twins in drug discovery and clinical trials","volume":"19","author":"Bordukova","year":"2024","journal-title":"Expert Opin Drug Discov"},{"key":"10.1016\/j.scib.2026.09.016_b0815","first-page":"19","article-title":"Unresolved questions in the application of artificial intelligence virtual cells for cancer research","volume":"12","author":"Ardila","year":"2025","journal-title":"Mil Med Res"},{"key":"10.1016\/j.scib.2026.09.016_b0820","doi-asserted-by":"crossref","first-page":"3370","DOI":"10.1016\/j.cell.2025.06.008","article-title":"Virtual Cell Challenge: toward a Turing test for the virtual cell","volume":"188","author":"Roohani","year":"2025","journal-title":"Cell"},{"key":"10.1016\/j.scib.2026.09.016_b0825","doi-asserted-by":"crossref","first-page":"3","DOI":"10.4274\/balkanmedj.galenos.2022.2022-11-51","article-title":"Must-have qualities of clinical research on artificial intelligence and machine learning","volume":"40","author":"Ko\u00e7ak","year":"2023","journal-title":"Balkan Med J"},{"key":"10.1016\/j.scib.2026.09.016_b0830","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106848","article-title":"Privacy-preserving artificial intelligence in healthcare: techniques and applications","volume":"158","author":"Khalid","year":"2023","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.scib.2026.09.016_b0835","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.tips.2022.12.005","article-title":"Computational and artificial intelligence-based methods for antibody development","volume":"44","author":"Kim","year":"2023","journal-title":"Trends Pharmacol Sci"},{"key":"10.1016\/j.scib.2026.09.016_b0840","doi-asserted-by":"crossref","DOI":"10.1136\/jitc-2024-009063","article-title":"Challenges and opportunities in cancer immunotherapy: a Society for Immunotherapy of Cancer (SITC) strategic vision","volume":"12","author":"Emens","year":"2024","journal-title":"J Immunother Cancer"},{"key":"10.1016\/j.scib.2026.09.016_b0845","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1056\/NEJMra2302038","article-title":"Artificial intelligence and machine learning in clinical medicine, 2023","volume":"388","author":"Haug","year":"2023","journal-title":"N Engl J Med"},{"key":"10.1016\/j.scib.2026.09.016_b0850","article-title":"Medical digital twins: enabling precision medicine and medical artificial intelligence","volume":"7","author":"Sad\u00e9e","year":"2025","journal-title":"Lancet Digit Health"},{"key":"10.1016\/j.scib.2026.09.016_b0855","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1158\/2326-6066.CIR-24-1270","article-title":"Artificial intelligence can predict personalized immunotherapy outcomes in cancer","volume":"13","author":"Huang","year":"2025","journal-title":"Cancer Immunol Res"},{"key":"10.1016\/j.scib.2026.09.016_b0860","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1016\/j.ccell.2025.03.018","article-title":"New horizons at the interface of artificial intelligence and translational cancer research","volume":"43","author":"Yates","year":"2025","journal-title":"Cancer Cell"},{"key":"10.1016\/j.scib.2026.09.016_b0865","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1186\/s12943-025-02400-z","article-title":"Overcoming resistance to anti-PD-L1 immunotherapy: mechanisms, combination strategies, and future directions","volume":"24","author":"Mandal","year":"2025","journal-title":"Mol Cancer"}],"container-title":["Science Bulletin"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2095927326010613?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2095927326010613?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T20:26:27Z","timestamp":1793132787000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2095927326010613"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":173,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2026,10]]}},"alternative-id":["S2095927326010613"],"URL":"https:\/\/doi.org\/10.1016\/j.scib.2026.09.016","relation":{},"ISSN":["2095-9273"],"issn-type":[{"value":"2095-9273","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Emerging advances in multimodal AI in cancer immunotherapy: from multi-scale data integration to clinical decision support","name":"articletitle","label":"Article Title"},{"value":"Science Bulletin","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.scib.2026.09.016","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V. and Science China Press.","name":"copyright","label":"Copyright"}]}}