When Biology Meets Medicine: A Perspective on Foundation Models
Artificial intelligence, and foundation models in particular, are transforming life sciences and medicine. This perspective reviews biological and medical foundation models across scales, highlighting key challenges in data availability, model evaluation, and architectural design.
Kunying Niu +3 more
wiley +1 more source
Functional profiling of the sequence stockpile: a protein pair-based assessment of in silico prediction tools. [PDF]
Prabakaran R, Bromberg Y.
europepmc +1 more source
AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley +1 more source
In Silico Prediction of Maize microRNA as a Xanthine Oxidase Inhibitor: A New Approach to Treating Hyperuricemia Patients. [PDF]
Joshi M, Khan MM.
europepmc +1 more source
AS‐pHopt: An Optimal pH Prediction Model Enhanced by Active Site of Enzymes
To address the low accuracy of enzyme optimal pH (pHopt) prediction, this study develops active site‐based pHopt (AS‐pHopt), a prediction model enhanced by active site information and pseudo‐label prediction. Integrating key structural and physicochemical features affecting enzyme pHopt, AS‐pHopt uses Evolutionary Scale Modeling (ESM)‐2 with active ...
Wenxiang Song +6 more
wiley +1 more source
Severe Darier's Disease by Mitochondrial DNA Insertion Causing Nonsense Mutations: In Silico Prediction of a Pathophysiological Mechanism to a Novel Mutation. [PDF]
Shintani H +9 more
europepmc +1 more source
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi +2 more
wiley +1 more source
In-silico prediction of coat protein structure of Indian citrus ringspot virus and their interactions with the Argonaut2/DCL4 proteins. [PDF]
Angira A +5 more
europepmc +1 more source
OxSpred, an eXtreme‐Gradient‐Boosting‐‐based supervised learning model, accurately annotates oxidative stress in innate immune cells at the single‐cell level, providing interpretable embeddings with significant biological relevance. This innovative tool revolutionizes the understanding of innate immune cell functions during inflammation and enhances ...
Po‐Yuan Chen, Tai‐Ming Ko
wiley +1 more source
UHPLC-QTOF-ESI-MS/MS, SNAP-MS Identification, In Silico Prediction of Pharmacokinetic Properties of Constituents from the Stem Bark of <i>Holarrhena floribunda</i> (G. Don) T. Durand and Schinz (Apocynaceae). [PDF]
Djila Possi FL +11 more
europepmc +1 more source

