{"scope":{"layer":"brain_tumor","tissue_scope":"unknown","species":"unknown","system":"unknown","evidence_level":"peer_reviewed","importance":"","importance_reason":""},"id":"VC0184","type":"paper","title":"AI and computational methods for reconstructing cell-state-specific gene regulatory networks from single-cell transcriptome and chromatin accessibility data in cancer: methods, applications, and perspectives","authors_or_org":"Wang J, Chen F.","date":"2026-10-20","year":"2026","venue":"Frontiers in Genetics","url":"https://doi.org/10.3389/fgene.2026.1982712","doi":"10.3389/fgene.2026.1982712","pmid":"","brain_tumor_focus":"partial","tags":["review","grn","scrna-seq","multi-omics","in-silico-perturbation","cancer"],"tag_slugs":["review","grn","scrna-seq","multi-omics","in-silico-perturbation","cancer"],"tag_labels":["Review","Gene regulatory network","scRNA-seq","Multi-omics","In-silico perturbation","Cancer"],"tag_labels_zh":["综述","基因调控网络","单细胞 RNA 测序","多组学","计算机模拟扰动","癌症"],"unmapped_tags":[],"one_line_summary_en":"Mini-review (Wang and Chen; Frontiers in Genetics 17:1982712, 2026) of AI and computational methods for reconstructing cell-state-specific gene regulatory networks in cancer from single-cell transcriptome and chromatin accessibility data. It distinguishes same-cell paired, matched-unpaired, imputed and single-modality designs, organizes methods by inference objective (motif/co-accessibility inference, deep integration and graph learning, dynamic and cell-state-specific modeling, virtual perturbation, with tools such as CellOracle, Pando, GenKI and scTenifoldKnk discussed in the full text), and discusses applications to EMT, treatment resistance, lineage plasticity and tumor subtyping along an evidence hierarchy from association to experimentally supported causal regulation. The glioma example (patient-derived glioma stem-like cells, citing Park et al. 2024) appears in the main text, not the abstract, and is described there as preclinical. The authors state clinical utility still requires prospective validation.","one_line_summary_zh":"小型综述（Wang 与 Chen；Frontiers in Genetics 17:1982712，2026）：梳理利用单细胞转录组与染色质可及性数据重建癌症中细胞状态特异基因调控网络（GRN）的 AI 与计算方法。文章区分同细胞配对、匹配未配对、插补与单模态设计，按推断目标归类方法（基序/共可及性推断、深度整合与图学习、动态与细胞状态特异建模、虚拟扰动；正文讨论了 CellOracle、Pando、GenKI、scTenifoldKnk 等工具），并沿“统计关联—方向性预测—计算机扰动—实验支持的因果调控”的证据层级讨论在 EMT、治疗抵抗、谱系可塑性与肿瘤分型中的应用。胶质瘤示例（患者来源胶质瘤干细胞样细胞，引用 Park 等 2024）出自正文而非摘要，正文称其仍属临床前结果。作者指出临床价值仍需前瞻性验证。","verified":"yes","verification_source":"Crossref (journal-article, Frontiers in Genetics vol 17, article 1982712, published 2026-10-20, CC BY 4.0, 2 authors, abstract) + Frontiers full-text page (tool names; glioma stem-like cell example citing Park et al. 2024 Sci Adv 10.1126/sciadv.adj7706) fetched 2026-10-21; no PMID yet","page_url":"https://gliostate.com/library/VC0184/","api_url":"https://gliostate.com/api/items/VC0184.json","sameAs":["https://doi.org/10.3389/fgene.2026.1982712"],"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/grn/","https://gliostate.com/library/tag/scrna-seq/","https://gliostate.com/library/tag/multi-omics/","https://gliostate.com/library/tag/in-silico-perturbation/","https://gliostate.com/library/tag/cancer/"]},"cited_by":[],"related":[{"id":"VC0072","title":"A method for in silico exploration of potential glioblastoma multiforme attractors using single-cell RNA sequencing.","shared_tags":["in-silico-perturbation","grn"],"page_url":"https://gliostate.com/library/VC0072/","api_url":"https://gliostate.com/api/items/VC0072.json"},{"id":"VC0139","title":"Engineered regulatory states resolve context-dependent genetics in macular degeneration","shared_tags":["multi-omics"],"page_url":"https://gliostate.com/library/VC0139/","api_url":"https://gliostate.com/api/items/VC0139.json"},{"id":"VC0141","title":"Acquired genetic and cell-state changes in IDH-mutant glioma progression","shared_tags":["multi-omics"],"page_url":"https://gliostate.com/library/VC0141/","api_url":"https://gliostate.com/api/items/VC0141.json"},{"id":"VC0062","title":"Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma.","shared_tags":["multi-omics"],"page_url":"https://gliostate.com/library/VC0062/","api_url":"https://gliostate.com/api/items/VC0062.json"},{"id":"VC0124","title":"AACR Annual Meeting 2027","shared_tags":["cancer"],"page_url":"https://gliostate.com/library/VC0124/","api_url":"https://gliostate.com/api/items/VC0124.json"},{"id":"VC0111","title":"Training transformers to solve cancer trial failure — Ron Alfa & Daniel Bear, Noetik","shared_tags":["cancer"],"page_url":"https://gliostate.com/library/VC0111/","api_url":"https://gliostate.com/api/items/VC0111.json"}]}