paper✓ VerifiedVC0173
Ma N, Wang Z, Yang X, Yang G, Yang R, Wang Z, Jiang Q, Chu Y. · Briefings in Bioinformatics · 2026-10-18
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.
DOIPubMed
paper✓ VerifiedVC0175
Ni J, Zhang X, Li M, Cai Y, Xie Z, Liu Y. · Computational Biology and Chemistry · 2026-10-09
Ni et al. (Southeast University; Computational Biology and Chemistry 2026) compare STRING/Gene Ontology graph priors with dense, self-edge-only and degree-rewired supports in one fixed graph-attention backbone on four public Perturb-seq datasets (Adamson, Norman, Replogle K562 and RPE1), with GEARS, scGPT and Geneformer evaluated on the same held-out guide conditions. At 200 highly variable genes, STRING-GO improved Pearson r by +0.016 (95% interval +0.010 to +0.022) over dense support; the effect fell to +0.009 at 1000 genes. Of 36 matched contrasts with external models, 32 were within a ±0.030 envelope and four favoured the external model. The authors state the main comparison was formulated during peer-review revision and that the modest gain does not by itself establish biological relevance.
DOIPubMed
paperBrain tumor✓ VerifiedVC0171
Pitarch-Abaigar C, Lohmann P, Aloy P, Al-Shahrour F, Arnaout O, Bi WL, Galldiks N, Huse JT, Karschnia P, Nasrallah MP, Patel A, Rahman R, Sahm F, Smith TR, Tonn JC, Umeton R, Valiente M, Zarella M, Huang RY, Bakas S. · Neuro-Oncology · 2026-10-13
Review by the RANO (Response Assessment in Neuro-Oncology) working groups (Pitarch-Abaigar et al.; senior authors Huang RY and Bakas S; Neuro-Oncology 2026) covering AI across six domains: neuroimaging, neuropathology, laboratory medicine, neurosurgery, drug discovery and clinical trials. The authors place imaging segmentation and quantification, AI-assisted image reconstruction, DNA methylation-based classification and workflow optimization closest to clinical use, while radiogenomics, WSI-based molecular prediction, AI-guided surgical targeting, liquid biopsy interpretation, AI-guided drug discovery, digital twins and in silico trials still require stronger prospective validation.
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paper✓ VerifiedVC0006
Ukaegbu D, Curean V, Bender A, Maertens A. · ALTEX · 2026-09-17
Perspective on AI virtual cell platforms for environmental perturbation modelling.
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paperBrain tumor✓ VerifiedVC0084
Ellenbogen Y, Landry AP, Yefet LS, Ajisebutu A, Gui C, Persaud P, Kaloti R, Nassiri F. · Neuro-oncology advances · 2026-02-27
Review: current landscape of single-cell genomics in meningioma.
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paper✓ VerifiedVC0004
Ma C, Zhang H, Rao Y, Jiang X, Liu B, Sun Z, Song Z, Gao Y, Cui Y, Liu X, Li Z. · NPJ digital medicine · 2025-12-11
Review of AI virtual cell models in preclinical research: technical pathways and validation.
DOIPubMed