{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T15:43:12Z","timestamp":1776094992492,"version":"3.50.1"},"reference-count":62,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T00:00:00Z","timestamp":1749686400000},"content-version":"vor","delay-in-days":42,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272094"],"award-info":[{"award-number":["62272094"]}],"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":["62225109"],"award-info":[{"award-number":["62225109"]}],"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":["62450112"],"award-info":[{"award-number":["62450112"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Transfer learning has been widely applied to drug sensitivity prediction based on single-cell RNA sequencing, leveraging knowledge from large datasets of cancer cell lines or other sources to improve the prediction of drug responses. However, previous studies require model fine-tuning for different patient single-cell datasets, limiting their ability to meet the clinical need for high-throughput rapid prediction. In this research, we introduce single-cell Adaptive Transfer and Distillation model (scATD), a transfer learning framework leveraging large language models for high-throughput drug sensitivity prediction. Based on different large language models (scFoundation and Geneformer) and transfer strategies, scATD includes three distinct sub-models: scATD-sf, scATD-gf, and scATD-sf-dist. scATD-sf and scATD-gf employs an important bidirectional style transfer to enable predictions for new patients without model parameter training. Additionally, scATD-sf-dist uses knowledge distillation from large models to enhance prediction performance, improve efficiency, and reduce resource requirements. Benchmarking across more diverse datasets demonstrates scATD\u2019s superior accuracy, generalization and efficiency. Besides, by rigorously selecting reference background samples for feature attribution algorithms, scATD also provides more meaningful insights into the relationship between gene expression and drug resistance mechanisms. Making scATD more interpretability for addressing critical challenges in precision oncology.<\/jats:p>","DOI":"10.1093\/bib\/bbaf268","type":"journal-article","created":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T05:32:28Z","timestamp":1749706348000},"source":"Crossref","is-referenced-by-count":5,"title":["scATD: a high-throughput and interpretable framework for single-cell cancer drug resistance prediction and biomarker identification"],"prefix":"10.1093","volume":"26","author":[{"given":"Murong","family":"Zhou","sequence":"first","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University , Harbin 150040 ,","place":["China"]},{"name":"College of Life Science, Northeast Forestry University , Harbin 150040 ,","place":["China"]}]},{"given":"Zeyu","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University , Harbin 150040 ,","place":["China"]}]},{"given":"Yu-Hang","family":"Yin","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University , Harbin 150040 ,","place":["China"]},{"name":"College of Life Science, Northeast Forestry University , Harbin 150040 ,","place":["China"]}]},{"given":"Qiaoming","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Henan University , Zhengzhou 450000 ,","place":["China"]}]},{"given":"Guohua","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University , Harbin 150040 ,","place":["China"]}]},{"given":"Yuming","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University , Harbin 150040 ,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2025,6,12]]},"reference":[{"key":"2025061201322366300_ref1","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1016\/j.cell.2017.01.018","article-title":"Clonal heterogeneity and tumor evolution: Past, present, and the future","volume":"168","author":"McGranahan","year":"2017","journal-title":"Cell"},{"key":"2025061201322366300_ref2","doi-asserted-by":"publisher","first-page":"e694","DOI":"10.1002\/ctm2.694","article-title":"Single-cell RNA sequencing technologies and applications: A brief overview","volume":"12","author":"Jovic","year":"2022","journal-title":"Clin Transl Med"},{"key":"2025061201322366300_ref3","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1038\/s41573-023-00688-4","article-title":"Applications of single-cell RNA sequencing in drug discovery and development","volume":"22","author":"Van de Sande","year":"2023","journal-title":"Nat Rev Drug Discov"},{"key":"2025061201322366300_ref4","doi-asserted-by":"publisher","first-page":"e2204113","DOI":"10.1002\/advs.202204113","article-title":"Enabling single-cell drug response annotations from bulk RNA-Seq using SCAD","volume":"10","author":"Zheng","year":"2023","journal-title":"Adv Sci (Weinh)"},{"key":"2025061201322366300_ref5","doi-asserted-by":"publisher","first-page":"6494","DOI":"10.1038\/s41467-022-34277-7","article-title":"Deep transfer learning of cancer drug responses by integrating bulk and single-cell RNA-seq data","volume":"13","author":"Chen","year":"2022","journal-title":"Nat Commun"},{"key":"2025061201322366300_ref6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/JBHI.2025.3553126","article-title":"Predicting single-cell drug sensitivity by adaptive weighted feature for adversarial multi-source domain adaptation","volume":"240305260","author":"Duan","year":"2024","journal-title":"arXiv preprint arXiv"},{"key":"2025061201322366300_ref7","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1126\/science.ade2574","article-title":"Evolutionary-scale prediction of atomic-level protein structure with a language model","volume":"379","author":"Lin","year":"2023","journal-title":"Science"},{"key":"2025061201322366300_ref8","article-title":"Large language models in bioinformatics: Applications and perspectives","author":"Liu","year":"2024","journal-title":"ArXiv"},{"key":"2025061201322366300_ref9","doi-asserted-by":"publisher","first-page":"852","DOI":"10.1038\/s42256-022-00534-z","article-title":"scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data","volume":"4","author":"Yang","year":"2022","journal-title":"Nat Mach Intell"},{"key":"2025061201322366300_ref10","doi-asserted-by":"publisher","first-page":"1481","DOI":"10.1038\/s41592-024-02305-7","article-title":"Large-scale foundation model on single-cell transcriptomics","volume":"21","author":"Hao","year":"2024","journal-title":"Nat Methods"},{"key":"2025061201322366300_ref11","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1038\/s41586-023-06139-9","article-title":"Transfer learning enables predictions in network biology","volume":"618","author":"Theodoris","year":"2023","journal-title":"Nature"},{"key":"2025061201322366300_ref12","doi-asserted-by":"publisher","first-page":"1430","DOI":"10.1038\/s41592-024-02353-z","article-title":"Transformers in single-cell omics: A review and new perspectives","volume":"21","author":"Szalata","year":"2024","journal-title":"Nat Methods"},{"key":"2025061201322366300_ref13","doi-asserted-by":"publisher","first-page":"2405861","DOI":"10.1002\/advs.202405861","article-title":"DrugFormer: Graph-enhanced language model to predict drug sensitivity","volume":"11","author":"Liu","year":"2024","journal-title":"Adv Sci"},{"key":"2025061201322366300_ref14","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat Mach Intell"},{"key":"2025061201322366300_ref15","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/s41551-018-0304-0","article-title":"Explainable machine-learning predictions for the prevention of hypoxaemia during surgery","volume":"2","author":"Lundberg","year":"2018","journal-title":"Nat Biomed Eng"},{"key":"2025061201322366300_ref16","doi-asserted-by":"publisher","first-page":"1454","DOI":"10.1038\/s41592-024-02359-7","article-title":"Applying interpretable machine learning in computational biology-pitfalls, recommendations and opportunities for new developments","volume":"21","author":"Chen","year":"2024","journal-title":"Nat Methods"},{"key":"2025061201322366300_ref17","doi-asserted-by":"publisher","first-page":"bbad534","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":"2025061201322366300_ref18","volume-title":"Proceedings of the IEEE International Conference on Computer Vision"},{"key":"2025061201322366300_ref19","article-title":"Multi-level distillation of semantic knowledge for pre-training multilingual language model","author":"Mingqi","year":"2022","journal-title":"Emperical Methods Nat Lang Process"},{"key":"2025061201322366300_ref20","volume-title":"Proceedings of the 34th International Conference on Machine Learning"},{"key":"2025061201322366300_ref21","doi-asserted-by":"publisher","DOI":"10.1093\/database\/baz046","article-title":"PanglaoDB: A web server for exploration of mouse and human single-cell RNA sequencing data","volume":"2019","author":"Franzen","journal-title":"Database (Oxford)"},{"key":"2025061201322366300_ref22","doi-asserted-by":"publisher","first-page":"D955","DOI":"10.1093\/nar\/gks1111","article-title":"Genomics of drug sensitivity in cancer (GDSC): A resource for therapeutic biomarker discovery in cancer cells","volume":"41","author":"Yang","year":"2012","journal-title":"Nucleic Acids Res"},{"key":"2025061201322366300_ref23","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1038\/nature11003","article-title":"The cancer cell line Encyclopedia enables predictive modelling of anticancer drug sensitivity","volume":"483","author":"Barretina","year":"2012","journal-title":"Nature"},{"key":"2025061201322366300_ref24","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1093\/nar\/30.1.207","article-title":"Gene expression omnibus: NCBI gene expression and hybridization array data repository","volume":"30","author":"Edgar","year":"2002","journal-title":"Nucleic Acids Res"},{"key":"2025061201322366300_ref25","doi-asserted-by":"publisher","first-page":"100933","DOI":"10.1016\/j.patter.2024.100933","article-title":"TCGA-reports: A machine-readable pathology report resource for benchmarking text-based AI models","volume":"5","author":"Kefeli","year":"2024","journal-title":"Patterns"},{"key":"2025061201322366300_ref26","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1016\/j.cell.2018.02.052","article-title":"An integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics","volume":"173","author":"Liu","year":"2018","journal-title":"Cell"},{"key":"2025061201322366300_ref27","first-page":"19667","article-title":"NVAE: A deep hierarchical variational autoencoder","volume":"33","author":"Vahdat","year":"2020","journal-title":"Adv Neural Inf Process Syst"},{"key":"2025061201322366300_ref28","first-page":"36","article-title":"xTrimoGene: An efficient and scalable representation learner for single-cell RNA-seq data","author":"Gong","year":"2024","journal-title":"Adv Neural Inform Process Syst"},{"key":"2025061201322366300_ref29","volume-title":"Proceedings of the North American Chapter of the Association for Computational Linguistics, F, 2019","author":"Devlin"},{"key":"2025061201322366300_ref30","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.neunet.2017.12.012","article-title":"Sigmoid-weighted linear units for neural network function approximation in reinforcement learning","volume":"107","author":"Elfwing","year":"2018","journal-title":"Neural Netw"},{"key":"2025061201322366300_ref31","volume-title":"Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition; 2016 Jun 27\u201330; Las Vegas, NV, USA"},{"key":"2025061201322366300_ref32","doi-asserted-by":"publisher","first-page":"5983","DOI":"10.1038\/s41467-024-50194-3","article-title":"OmicVerse: A framework for bridging and deepening insights across bulk and single-cell sequencing","volume":"15","author":"Zeng","year":"2024","journal-title":"Nat Commun"},{"key":"2025061201322366300_ref33","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD \u201919); 2019 Aug 4\u20138; Anchorage, AK, USA"},{"key":"2025061201322366300_ref34","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J Artif Intell Res"},{"key":"2025061201322366300_ref35","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","article-title":"Silhouettes: A graphical aid to the interpretation and validation of cluster analysis","volume":"20","author":"Rousseeuw","year":"1987","journal-title":"J Comput Appl Math"},{"key":"2025061201322366300_ref36","article-title":"Towards better understanding of gradient-based attribution methods for deep neural networks","volume":"171106104","author":"Ancona","year":"2017","journal-title":"arXiv preprint arXiv"},{"key":"2025061201322366300_ref37","doi-asserted-by":"publisher","first-page":"1914","DOI":"10.1038\/s41467-022-29443-w","article-title":"Learning meaningful representations of protein sequences","volume":"13","author":"Detlefsen","year":"2022","journal-title":"Nat Commun"},{"key":"2025061201322366300_ref38","article-title":"In-context language learning: Arhitectures and algorithms","volume":"240112973","author":"Aky\u00fcrek","year":"2024","journal-title":"arXiv preprint arXiv"},{"key":"2025061201322366300_ref39","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1016\/j.cell.2016.06.017","article-title":"A landscape of pharmacogenomic interactions in cancer","volume":"166","author":"Iorio","year":"2016","journal-title":"Cell"},{"key":"2025061201322366300_ref40","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.ejca.2016.03.081","article-title":"RECIST 1.1\u2014Update and clarification: From the RECIST committee","volume":"62","author":"Schwartz","year":"2016","journal-title":"Eur J Cancer"},{"key":"2025061201322366300_ref41","doi-asserted-by":"publisher","first-page":"e2201501","DOI":"10.1002\/advs.202201501","article-title":"Interpretable machine learning models to predict the resistance of breast cancer patients to doxorubicin from their microRNA profiles","volume":"9","author":"Ogunleye","year":"2022","journal-title":"Adv Sci"},{"key":"2025061201322366300_ref42","doi-asserted-by":"publisher","first-page":"0108","DOI":"10.34133\/hds.0108","article-title":"Large-scale machine learning analysis reveals DNA methylation and gene expression response signatures for gemcitabine-treated pancreatic cancer","volume":"4","author":"Ogunleye","year":"2024","journal-title":"Health Data Sci"},{"key":"2025061201322366300_ref43","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1186\/s13073-021-01000-y","article-title":"Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures","volume":"13","author":"Suphavilai","year":"2021","journal-title":"Genome Med"},{"key":"2025061201322366300_ref44","doi-asserted-by":"publisher","first-page":"865","DOI":"10.1016\/j.ccell.2022.07.004","article-title":"Pan-cancer integrative histology-genomic analysis via multimodal deep learning","volume":"40","author":"Chen","year":"2022","journal-title":"Cancer Cell"},{"key":"2025061201322366300_ref45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/6567916","article-title":"Prognostic risk signature and comprehensive analyses of endoplasmic reticulum stress-related genes in lung adenocarcinoma","volume":"2022","author":"Yang","year":"2022","journal-title":"J Immunol Res"},{"key":"2025061201322366300_ref46","doi-asserted-by":"publisher","first-page":"3286","DOI":"10.1080\/21691401.2019.1648283","article-title":"MiR-629-3p-induced downregulation of SFTPC promotes cell proliferation and predicts poor survival in lung adenocarcinoma","volume":"47","author":"Li","year":"2019","journal-title":"Artif Cells Nanomed Biotechnol"},{"key":"2025061201322366300_ref47","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1038\/s41698-024-00682-y","article-title":"Amivantamab efficacy in wild-type EGFR NSCLC tumors correlates with levels of ligand expression","volume":"8","author":"Rivera-Soto","year":"2024","journal-title":"NPJ Precision Oncol"},{"key":"2025061201322366300_ref48","doi-asserted-by":"publisher","first-page":"1582","DOI":"10.1002\/jcsm.12985","article-title":"Inhibition of epidermal growth factor receptor suppresses parathyroid hormone-related protein expression in tumours and ameliorates cancer-associated cachexia","volume":"13","author":"Weber","year":"2022","journal-title":"J Cachexia Sarcopenia Muscle"},{"key":"2025061201322366300_ref49","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1038\/s41419-024-06945-7","article-title":"PD-L1 induces autophagy and primary resistance to EGFR\u2013TKIs in EGFR-mutant lung adenocarcinoma via the MAPK signaling pathway","volume":"15","author":"Li","year":"2024","journal-title":"Cell Death Dis"},{"key":"2025061201322366300_ref50","doi-asserted-by":"publisher","first-page":"999","DOI":"10.1038\/s41419-020-03198-y","article-title":"Novel lncRNA UPLA1 mediates tumorigenesis and prognosis in lung adenocarcinoma","volume":"11","author":"Han","year":"2020","journal-title":"Cell Death Dis"},{"key":"2025061201322366300_ref51","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1186\/s13059-024-03293-9","article-title":"TMO-net: An explainable pretrained multi-omics model for multi-task learning in oncology","volume":"25","author":"Wang","year":"2024","journal-title":"Genome Biol"},{"key":"2025061201322366300_ref52","doi-asserted-by":"publisher","first-page":"bbae076","DOI":"10.1093\/bib\/bbae076","article-title":"Cracking the black box of deep sequence-based protein\u2013protein interaction prediction","volume":"25","author":"Bernett","year":"2024","journal-title":"Brief Bioinform"},{"key":"2025061201322366300_ref53","doi-asserted-by":"publisher","first-page":"i911","DOI":"10.1093\/bioinformatics\/btaa822","article-title":"DeepCDR: A hybrid graph convolutional network for predicting cancer drug response","volume":"36","author":"Liu","year":"2020","journal-title":"Bioinformatics"},{"key":"2025061201322366300_ref54","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1038\/s41586-023-05905-z","article-title":"Computational approaches streamlining drug discovery","volume":"616","author":"Sadybekov","year":"2023","journal-title":"Nature"},{"key":"2025061201322366300_ref55","doi-asserted-by":"publisher","first-page":"5989","DOI":"10.1038\/s41467-024-50150-1","article-title":"scSemiProfiler: Advancing large-scale single-cell studies through semi-profiling with deep generative models and active learning","volume":"15","author":"Wang","year":"2024","journal-title":"Nat Commun"},{"key":"2025061201322366300_ref56","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1038\/s41586-021-04278-5","article-title":"Multi-omic machine learning predictor of breast cancer therapy response","volume":"601","author":"Sammut","year":"2022","journal-title":"Nature"},{"key":"2025061201322366300_ref57","volume":"30","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2025061201322366300_ref58","doi-asserted-by":"publisher","first-page":"950835","DOI":"10.3389\/fonc.2022.950835","article-title":"The integrative bioinformatics approaches to predict the xanthohumol as anti-breast cancer molecule: Targeting cancer cells signaling PI3K and AKT kinase pathway","volume":"12","author":"Gupta","year":"2022","journal-title":"Front Oncol"},{"key":"2025061201322366300_ref59","doi-asserted-by":"publisher","first-page":"7214","DOI":"10.7150\/jca.63517","article-title":"Decipher the helicobacter pylori protein targeting in the nucleus of host cell and their implications in gallbladder cancer: An insilico approach","volume":"12","author":"Wang","year":"2021","journal-title":"J Cancer"},{"key":"2025061201322366300_ref60","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1080\/10408363.2024.2322565","article-title":"Implication of calcium supplementations in health and diseases with special focus on colorectal cancer","volume":"61","author":"Khan","year":"2024","journal-title":"Crit Rev Clin Lab Sci"},{"key":"2025061201322366300_ref61","doi-asserted-by":"publisher","first-page":"e0148530","DOI":"10.1371\/journal.pone.0148530","article-title":"Systems biology approaches for the prediction of possible role of chlamydia pneumoniae proteins in the etiology of lung cancer","volume":"11","author":"Khan","year":"2016","journal-title":"PloS One"},{"key":"2025061201322366300_ref62","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1038\/s42256-020-0197-y","article-title":"Causal inference and counterfactual prediction in machine learning for actionable healthcare","volume":"2","author":"Prosperi","year":"2020","journal-title":"Nat Mach Intell"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/3\/bbaf268\/63473663\/bbaf268.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/3\/bbaf268\/63473663\/bbaf268.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T05:32:32Z","timestamp":1749706352000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbaf268\/8160682"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,1]]},"references-count":62,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,5,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbaf268","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,5]]},"published":{"date-parts":[[2025,5,1]]},"article-number":"bbaf268"}}