{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T12:20:30Z","timestamp":1788006030664,"version":"build-2784847793"},"reference-count":32,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"vor","delay-in-days":238,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biotechnology Notes"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1016\/j.biotno.2026.08.001","type":"journal-article","created":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T08:30:18Z","timestamp":1787992218000},"page":"177-182","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["From spatial maps to treatment decisions: a roadmap for integrating explainable AI with multi-omics to guide precision immunotherapy"],"prefix":"10.1016","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3502-7409","authenticated-orcid":false,"given":"Mamoudou","family":"Hamadou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.biotno.2026.08.001_bib1","doi-asserted-by":"crossref","first-page":"1615","DOI":"10.1007\/s13402-025-01108-y","article-title":"Fibroblasts in the tumor microenvironment: heterogeneity and dynamic interactions in tumor progression revealed by spatial transcriptomics","volume":"48","author":"Ji","year":"2025","journal-title":"Cell Oncol"},{"key":"10.1016\/j.biotno.2026.08.001_bib2","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1016\/j.cell.2020.07.005","article-title":"Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front","volume":"182","author":"Sch\u00fcrch","year":"2020","journal-title":"Cell"},{"key":"10.1016\/j.biotno.2026.08.001_bib3","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1038\/s41586-025-09478-x","article-title":"Spatial joint profiling of DNA methylome and transcriptome in tissues","volume":"646","author":"Lee","year":"2025","journal-title":"Nature"},{"key":"10.1016\/j.biotno.2026.08.001_bib4","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1038\/s41576-023-00580-2","article-title":"Methods and applications for single-cell and spatial multi-omics","volume":"24","author":"Vandereyken","year":"2023","journal-title":"Nat Rev Genet"},{"key":"10.1016\/j.biotno.2026.08.001_bib5","doi-asserted-by":"crossref","DOI":"10.1080\/19490976.2026.2644681","article-title":"Gut microbiota metabolic reprogramming drives the development of metabolic diseases in the host","volume":"18","author":"Wang","year":"2026","journal-title":"Gut Microbes"},{"key":"10.1016\/j.biotno.2026.08.001_bib6","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1038\/s41586-019-1836-5","article-title":"An intra-tumoral niche maintains and differentiates stem-like CD8 T cells","volume":"576","author":"Jansen","year":"2019","journal-title":"Nature"},{"key":"10.1016\/j.biotno.2026.08.001_bib7","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-024-53034-6","article-title":"Pre-operative stereotactic radiosurgery and peri-operative dexamethasone for resectable brain metastases: a two-arm pilot study evaluating clinical outcomes and immunological correlates","author":"Jansen","year":"2024","journal-title":"Nat Commun"},{"key":"10.1016\/j.biotno.2026.08.001_bib8","article-title":"Spatially resolved transcriptomics reveal the determinants of primary resistance to immunotherapy in NSCLC with mature tertiary lymphoid structures","volume":"6","author":"Peyraud","year":"2025","journal-title":"Cell Rep Med"},{"key":"10.1016\/j.biotno.2026.08.001_bib9","doi-asserted-by":"crossref","DOI":"10.3389\/fimmu.2025.1698088","article-title":"TGF-\u03b2\u2013driven T-cell exclusion in ovarian cancer: single-cell and spatial transcriptomic views of immune low-response states","volume":"16","author":"He","year":"2025","journal-title":"Front Immunol"},{"key":"10.1016\/j.biotno.2026.08.001_bib10","doi-asserted-by":"crossref","first-page":"6901","DOI":"10.3390\/ijms26146901","article-title":"Spatiotemporal heterogeneity of tumor glucose metabolism reprogramming: from single-cell mechanisms to precision interventions","volume":"26","author":"Chai","year":"2025","journal-title":"Int J Mol Sci"},{"key":"10.1016\/j.biotno.2026.08.001_bib11","doi-asserted-by":"crossref","first-page":"2646","DOI":"10.21037\/tlcr-2025-244","article-title":"Single-cell and spatial transcriptomics profile the interaction of SPP1+ macrophages and FAP+ fibroblasts in non-small cell lung cancer","volume":"14","author":"Xiao","year":"2025","journal-title":"Transl Lung Cancer Res"},{"key":"10.1016\/j.biotno.2026.08.001_bib12","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102470","article-title":"Explainable artificial intelligence (XAI) in deep learning-based medical image analysis","volume":"79","author":"van der Velden","year":"2022","journal-title":"Med Image Anal"},{"key":"10.1016\/j.biotno.2026.08.001_bib13","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1038\/s41551-020-00682-w","article-title":"Data-efficient and weakly supervised computational pathology on whole-slide images","volume":"5","author":"Lu","year":"2021","journal-title":"Nat Biomed Eng"},{"key":"10.1016\/j.biotno.2026.08.001_bib14","doi-asserted-by":"crossref","first-page":"e763","DOI":"10.1016\/S2589-7500(21)00180-1","article-title":"Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study","volume":"3","author":"Bilal","year":"2021","journal-title":"Lancet Digit Health"},{"key":"10.1016\/j.biotno.2026.08.001_bib15","first-page":"2547","article-title":"Benchmarking explainable AI methods for toxicophore detection and toxicity prediction","volume":"5","author":"Khasanova","year":"2026","journal-title":"Dig Dis"},{"key":"10.1016\/j.biotno.2026.08.001_bib16","series-title":"Proceeding of Advances in Neural Information Processing Systems 30","first-page":"30","article-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017"},{"key":"10.1016\/j.biotno.2026.08.001_bib17","series-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"1135","article-title":"Why should I trust you?","author":"Ribeiro","year":"2016"},{"key":"10.1016\/j.biotno.2026.08.001_bib18","series-title":"Proceedings of the 37th International Conference on Machine Learning","first-page":"5338","article-title":"Concept bottleneck models","author":"Pang","year":"2020"},{"key":"10.1016\/j.biotno.2026.08.001_bib19","doi-asserted-by":"crossref","first-page":"247","DOI":"10.3390\/foods14020247","article-title":"Application of convolutional neural networks and recurrent neural networks in food safety","volume":"14","author":"Ding","year":"2025","journal-title":"Foods"},{"key":"10.1016\/j.biotno.2026.08.001_bib20","series-title":"Proceedings of the 39th ACM\/SIGAPP Symposium on Applied Computing","first-page":"971","article-title":"Explainable artificial intelligence (XAI) approach for reinforcement learning systems","author":"Peixoto","year":"2024"},{"key":"10.1016\/j.biotno.2026.08.001_bib21","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/s43094-026-00954-3","article-title":"Navigation of drug discovery via artificial intelligence","volume":"12","author":"Mishra","year":"2026","journal-title":"Futur J Pharm\\ Sci"},{"key":"10.1016\/j.biotno.2026.08.001_bib22","doi-asserted-by":"crossref","DOI":"10.1007\/s11831-026-10517-7","article-title":"Advancement in metabolomics research via tools, databases, and packages: a cancer perspective","author":"Mishra","year":"2026","journal-title":"Arch Comput Methods Eng"},{"key":"10.1016\/j.biotno.2026.08.001_bib23","article-title":"From prediction to design: a machine learning model for de novo bioactive compounds and personalized biotherapeutics","author":"Mamoudou","year":"2026","journal-title":"Lett Drug Des Discov"},{"key":"10.1016\/j.biotno.2026.08.001_bib24","doi-asserted-by":"crossref","DOI":"10.1016\/j.afres.2025.101291","article-title":"AI-driven bioactive peptide discovery of next-generation metabolic biotherapeutics","author":"Mamoudou","year":"2025","journal-title":"Appl Food Res"},{"key":"10.1016\/j.biotno.2026.08.001_bib25","doi-asserted-by":"crossref","DOI":"10.1016\/j.cej.2025.161134","article-title":"AI-driven prediction of cardio-oncology biomarkers through protein corona analysis","volume":"509","author":"Guha","year":"2025","journal-title":"Chem Eng J"},{"key":"10.1016\/j.biotno.2026.08.001_bib26","author":"Mohammed"},{"key":"10.1016\/j.biotno.2026.08.001_bib27","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbae421","article-title":"Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology","volume":"25","author":"Mi","year":"2024","journal-title":"Brief Bioinform"},{"key":"10.1016\/j.biotno.2026.08.001_bib28","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1038\/s41591-025-04060-4","article-title":"AI-enabled virtual spatial proteomics from histopathology for interpretable biomarker discovery in lung cancer","volume":"32","author":"Li","year":"2026","journal-title":"Nat Med"},{"key":"10.1016\/j.biotno.2026.08.001_bib29","doi-asserted-by":"crossref","DOI":"10.1016\/j.curpro.2025.100051","article-title":"Compounds isolated from Rhabdophyllum arnoldianum: promising new molecules for cancer treatment through their inhibitory properties on tyrosine kinases and dihydrofolate reductase, revealed by molecular docking","volume":"22","author":"Hamadou","year":"2025","journal-title":"Curr Proteom"},{"key":"10.1016\/j.biotno.2026.08.001_bib30","series-title":"Advances in Organic Waste Conversion Through Industrial Biotechnology. Sustainable Environmental Waste Management Strategies","first-page":"443","article-title":"Microbial Fermentation Strategy for Converting Organic Waste to Value-Added Product","author":"Abubakar","year":"2026"},{"key":"10.1016\/j.biotno.2026.08.001_bib31","doi-asserted-by":"crossref","DOI":"10.1002\/psc.70001","article-title":"Regulatory guidelines for the analysis of therapeutic peptides and proteins","volume":"31","author":"Elsayed","year":"2025","journal-title":"J Pept Sci"},{"key":"10.1016\/j.biotno.2026.08.001_bib32","doi-asserted-by":"crossref","DOI":"10.1002\/wnan.70027","article-title":"Machine learning and artificial intelligence in nanomedicine","volume":"17","author":"Chou","year":"2025","journal-title":"WIREs Nanomed Nanobiotechnol"}],"container-title":["Biotechnology Notes"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2665906926000176?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2665906926000176?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T12:08:40Z","timestamp":1788005320000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2665906926000176"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":32,"alternative-id":["S2665906926000176"],"URL":"https:\/\/doi.org\/10.1016\/j.biotno.2026.08.001","relation":{},"ISSN":["2665-9069"],"issn-type":[{"value":"2665-9069","type":"print"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"From spatial maps to treatment decisions: a roadmap for integrating explainable AI with multi-omics to guide precision immunotherapy","name":"articletitle","label":"Article Title"},{"value":"Biotechnology Notes","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.biotno.2026.08.001","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.","name":"copyright","label":"Copyright"}]}}