Results 191 to 200 of about 125,055 (266)

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Advanced Intelligent Discovery, EarlyView.
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

Comparative Efficacy of SGLT2 Inhibitors in MASLD: Bayesian Network Meta-Analysis of CAP-LSM Outcomes and Time Effects. [PDF]

JGH Open
Gomez DP, Hababag WF, Ong-Ramos CC.
europepmc   +1 more source

Interpretable Machine Learning for Bandgap Prediction and Descriptor‐Guided Design Rules of Phosphates

Advanced Intelligent Discovery, EarlyView.
An explainable CatBoost model was trained to predict the bandgaps of 474 phosphate crystals based on composition and density descriptors. SHAP analysis identified two key variables—d‐electron‐count dispersion and atomic‐density dispersion—as the primary drivers of the model's predictions.
Wenhu Wang, Abudukadi Tudi, Ran An, Zhihua Yang   +3 more
wiley   +1 more source

Comparative effects of some pharmacological and non-pharmacological interventions on cognitive function in Alzheimer's disease: A Bayesian network meta-analysis. [PDF]

J Prev Alzheimers Dis
Zhao Y, Luo G, Huang C, Chen Z, Wei Y, Chen Q, Wang F, Gan Y, Yin X.   +8 more
europepmc   +1 more source

Large‐Scale Machine Learning to Screen for Small‐Molecule Senolytics

Advanced Intelligent Discovery, EarlyView.
A consistent workflow underpins all experiments in this study. A dedicated model‐selection dataset first identifies optimal hyperparameters for each algorithm. Models are then trained and rigorously evaluated on independent sets of molecules using the senolytic ratio SR. Comprehensive hyperparameter exploration across SMILES representations, task types,
Alexis Dougha, Jesus Gil, Pedro J. Ballester   +2 more
wiley   +1 more source

Study on the driving mechanism of cultivated land change in the urban-rural fringe with Bayesian network modeling. [PDF]

Sci Rep
Wang J, Zhu Z, Chen M, Zhang Y.
europepmc   +1 more source

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
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, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

Heterogeneous trajectories of intrinsic capacity and their predictors: a Bayesian network analysis of a longitudinal cohort in Chinese nursing homes. [PDF]

Front Public Health
Su H, Wang Y, Zhang Y, Qi X.
europepmc   +1 more source