Results 71 to 80 of about 1,485 (251)
A Feature Subset Selection Method Based On High-Dimensional Mutual Information
Feature selection is an important step in building accurate classifiers and provides better understanding of the data sets. In this paper, we propose a feature subset selection method based on high-dimensional mutual information.
Chee Keong Kwoh, Yun Zheng
doaj +1 more source
PDIA6–SCD1 Axis Rewires Lipid Metabolism to Drive Gastric Cancer Progression
Protein disulfide isomerase A6 (PDIA6) is identified as an oncogenic driver in gastric cancer. PDIA6 directly binds and stabilizes SCD1 by limiting its ubiquitin–proteasome‐mediated degradation, thereby sustaining monounsaturated fatty acid (MUFA)‐enriched lipid homeostasis and lipid metabolic reprogramming.
Zhen Tian +13 more
wiley +1 more source
AI‐Physics‐Experiment Trinity for Integrated Protein Dynamics Modeling
This review unites experiments, physics‐based simulations, and AI as a synergistic triad for protein dynamics modeling. It highlights integrative strategies, resolves sampling and forcefield bottlenecks, and outlines challenges and future directions for accurate, interpretable conformational ensemble prediction.
Chen Shi +4 more
wiley +1 more source
Evaluating Directed Acyclic Graphs with DAGMetrics: Insights from Tuber and Soil Microbiome Data
Understanding and evaluating directed acyclic graphs (DAGs) is crucial for causal discovery, particularly in high-dimensional and small-sample datasets such as microbial abundance data.
Pavel Averin +4 more
doaj +1 more source
Driving anger, known as “road rage”, has gradually become a serious traffic psychology issue. Although driving anger identification is solved in some studies, there is still a gap in driving anger grading which is helpful to take different intervening ...
Ping Wan +3 more
doaj +1 more source
Feature Selection With Ensemble Learning Based on Improved Dempster-Shafer Evidence Fusion
Feature selection or attribute reduction is an important data preprocessing technique for dimensionality reduction in machine learning and data mining.
Yifeng Zheng +4 more
doaj +1 more source
We introduce a vision‐based real‐time monitoring system for additive manufacturing that detects subtle moisture‐induced degradation via a diffusion model‐based framework. The approach enables nondestructive assessment of moisture‐induced damage level and mechanical performance and establishes a practical route toward more intelligent, reliable, and ...
Jiyoung Jung +4 more
wiley +1 more source
SKALE 2.0 maps disease‐associated protein aggregation as a phase‐resolved structural process, linking mutation‐induced geometric perturbations to nucleation, elongation, and suppressor design. Across neurodegenerative proteins, the framework reveals cryptic aggregation vulnerabilities, separates phase‐concordant and phase‐switching mutations, and ...
Jia Shen Sio +6 more
wiley +1 more source
Employing a digital single‐molecule activity tracker (dSMAT), this research demonstrates that high‐photon‐flux irradiation drives progressive oxidative scarring in polymerases. Unlike simple thermal denaturation, real‐time kinetic tracking dynamically visualizes enzymes degrading into multiple impaired subpopulations.
Anran Zheng +11 more
wiley +1 more source
Exact Discrete Stochastic Simulation With Deep‐Learning‐Scale Gradient Optimization
A 203,796‐parameter gene regulatory network classifies handwritten digits with 98.4% accuracy using exact stochastic dynamics. The framework decouples forward simulation from backward differentiation, making continuous‐time Markov chain models compatible with deep‐learning optimization.
Jose M. G. Vilar, Leonor Saiz
wiley +1 more source

