Results 201 to 210 of about 67,103 (261)
Explaining Person-by-Item Responses using Person- and Item-Level Predictors via Random Forests and Interpretable Machine Learning in Explanatory Item Response Models. [PDF]
Cho SJ, Amanda G, Salas J, Mueller S.
europepmc +1 more source
Integrated clinical and mechanistic analyses identify GALNT7 as a ferroptosis‐suppressive regulator associated with immunotherapy resistance in non‐small cell lung cancer. GALNT7 depletion promotes lipid peroxidation, mitochondrial dysfunction, and ferroptosis, enhances CD8+ T‐cell activation and IFN‐γ production, and sensitizes tumors to PD‐1 blockade,
Jiadi Gan +11 more
wiley +1 more source
pyRforest: a comprehensive R package for genomic data analysis featuring scikit-learn Random Forests in R. [PDF]
Kolisnik T +4 more
europepmc +1 more source
Upon JEV infection, ZNF33B recruits METTL14 to stabilize the METTL3‐METTL14 m6A methyltransferase complex, leading to increased m6A modification of host transcripts, including Trim25 mRNA. ZNF33B selectively binds m6A‐modified sites on Trim25 mRNA and accelerates its decay, resulting in reduced TRIM25 protein abundance.
Jian Du +9 more
wiley +1 more source
CauFinder: Steering Cell‐State and Phenotype Transitions by Causal Disentanglement Learning
CauFinder combines causal disentanglement modeling and network control to prioritize causal drivers of cell‐state transitions from observational transcriptomic data. The framework separates transition‐relevant signals from spurious associations, nominates intervention targets across biological and disease contexts, and identifies DAAM1 as an actionable
Chengming Zhang +11 more
wiley +1 more source
Random forests for survival data: which methods work best and under what conditions? [PDF]
Berkowitz M, Altman RM, Loughin TM.
europepmc +1 more source
Neuron‐derived MIF binds VCAM1 on gastric cancer cells and activates ERK/STAT3 signaling, leading to CXCL8 transcription and secretion. Tumor‐derived CXCL8 subsequently stimulates neuronal CXCR2 to enhance MIF production, establishing a self‐amplifying MIF–VCAM1–CXCL8 positive‐feedback loop that promotes perineural invasion, tumor progression, and ...
Xunjun Li +13 more
wiley +1 more source
Improving dengue fever predictions in Taiwan based on feature selection and random forests. [PDF]
Kuo CY, Yang WW, Su EC.
europepmc +1 more source
Dual‐Module Near‐Infrared Fluorophores Discovery System via Knowledge Transfer
This study presents a dual‐module deep learning system for the design of near‐infrared (NIR) fluorophores. A large molecular library is generated and analyzed, leading to the suggestions of promising candidates. The effectiveness of the system is further validated through the synthesis, characterization, and in vivo imaging, demonstrating its potential
Yixin Zhu +7 more
wiley +1 more source

