{"scope":{"layer":"general","tissue_scope":"unknown","species":"unknown","system":"unknown","evidence_level":"peer_reviewed","importance":"","importance_reason":""},"id":"VC0173","type":"paper","title":"Virtual cells: progress and challenges from mechanistic modeling to data-driven integration","authors_or_org":"Ma N, Wang Z, Yang X, Yang G, Yang R, Wang Z, Jiang Q, Chu Y.","date":"2026-10-18","year":"2026","venue":"Briefings in Bioinformatics","url":"https://doi.org/10.1093/bib/bbag562","doi":"10.1093/bib/bbag562","pmid":"42893561","brain_tumor_focus":"no","tags":["review","aivc","mechanistic","foundation-model","benchmark"],"tag_slugs":["review","aivc","mechanistic","foundation-model","benchmark"],"tag_labels":["Review","AI virtual cell","Mechanistic & agent-based model","Foundation model","Benchmark & validation"],"tag_labels_zh":["综述","AI 虚拟细胞","机制与基于主体的模型","基础模型","基准评测与验证"],"unmapped_tags":[],"one_line_summary_en":"Review (Ma et al.; senior authors Jiang Q and Chu Y; Briefings in Bioinformatics 2026) that uses state encoding, state transition and observation decoding as a common abstraction across mechanistic models, data-driven foundation and perturbation models, multimodal systems and spatial/multiscale simulation, and examines model boundaries, data foundations, resource disclosure, out-of-distribution evaluation, causal evidence and experimental validation. The authors note that independent benchmarks show parameter count and training-set size do not automatically yield robust out-of-distribution generalization.","one_line_summary_zh":"综述（Ma 等；资深作者 Jiang Q 与 Chu Y；Briefings in Bioinformatics 2026）：以“状态编码—状态转移—观测解码”作为统一抽象，梳理机理模型、数据驱动的基础模型与扰动模型、多模态系统及空间/多尺度模拟，并讨论模型边界、数据基础、资源公开、分布外评测、因果证据与实验验证。作者指出，独立基准显示参数量和训练集规模并不会自动带来稳健的分布外泛化。","verified":"yes","verification_source":"Crossref (journal-article, vol 27, article bbag562, published online 2026-10-18, issue date 2026-09, CC BY 4.0, 8 authors, abstract) + PubMed PMID 42893561 fetched 2026-10-20; full text not read","page_url":"https://gliostate.com/library/VC0173/","api_url":"https://gliostate.com/api/items/VC0173.json","sameAs":["https://doi.org/10.1093/bib/bbag562","https://pubmed.ncbi.nlm.nih.gov/42893561/"],"is_preprint":false,"facets":{"type":"https://gliostate.com/library/type/paper/","year":"https://gliostate.com/library/year/2026/","tags":["https://gliostate.com/library/tag/review/","https://gliostate.com/library/tag/aivc/","https://gliostate.com/library/tag/mechanistic/","https://gliostate.com/library/tag/foundation-model/","https://gliostate.com/library/tag/benchmark/"]},"cited_by":[],"related":[{"id":"VC0174","title":"Emerging frontiers for AI in single-cell genomics","shared_tags":["review","foundation-model","aivc"],"page_url":"https://gliostate.com/library/VC0174/","api_url":"https://gliostate.com/api/items/VC0174.json"},{"id":"VC0004","title":"AI-driven virtual cell models in preclinical research: technical pathways, validation mechanisms, and clinical translation potential.","shared_tags":["review","benchmark"],"page_url":"https://gliostate.com/library/VC0004/","api_url":"https://gliostate.com/api/items/VC0004.json"},{"id":"VC0171","title":"AI in Neuro-Oncology: An Overarching Review","shared_tags":["review"],"page_url":"https://gliostate.com/library/VC0171/","api_url":"https://gliostate.com/api/items/VC0171.json"},{"id":"VC0006","title":"Beyond the drug-centric view: Advancing AI virtual cell platforms for environmental perturbation modelling.","shared_tags":["review"],"page_url":"https://gliostate.com/library/VC0006/","api_url":"https://gliostate.com/api/items/VC0006.json"},{"id":"VC0084","title":"Current landscape of single-cell genomics in meningioma.","shared_tags":["review"],"page_url":"https://gliostate.com/library/VC0084/","api_url":"https://gliostate.com/api/items/VC0084.json"},{"id":"VC0007","title":"Can AI build a virtual cell? Scientists race to model life's smallest unit.","shared_tags":["review"],"page_url":"https://gliostate.com/library/VC0007/","api_url":"https://gliostate.com/api/items/VC0007.json"}]}