Results 111 to 120 of about 807,381 (349)
Bayesian Approach to Network Modularity
We present an efficient, principled, and interpretable technique for inferring module assignments and for identifying the optimal number of modules in a given network. We show how several existing methods for finding modules can be described as variant, special, or limiting cases of our work, and how the method overcomes the resolution limit problem ...
Jake M. Hofman, Chris H. Wiggins
openaire +5 more sources
A Multi‐Objective Molecular Generation Method Based on Pareto Algorithm and Monte Carlo Tree Search
Pareto Monte Carlo Tree Search Molecular Generation (PMMG), a molecular generation approach leveraging Monte Carlo Tree Search (MCTS) and Pareto algorithm, efficiently explores the Pareto front in high‐dimensional objective spaces for multi‐objective drug design.
Yifei Liu+12 more
wiley +1 more source
High‐end normal hemoglobin levels are associated with an increased odds of polycystic ovary syndrome (PCOS), potentially through a mechanism of elevating testosterone levels involving the hypoxia‐inducible factor 1 (HIF‐1) pathway. These findings provide novel insights into the pathophysiology of PCOS and highlight that hemoglobin levels may serve as a
Guiquan Wang+13 more
wiley +1 more source
Enhancer RNA (eRNA) can play a key role in cancer initiation and progression. Here, the authors conducts a comprehensive pan‐cancer eRNA‐based TWAS analysis and discovered a distinct class of eRNA‐mediated cancer susceptibility genes across 23 cancer types.
Wenyan Chen+14 more
wiley +1 more source
A Knowledge‐Guided Graph Learning Approach Bridging Phenotype‐ and Target‐Based Drug Discovery
Knowledge‐Guided Drug Relational Predictor (KGDRP), a graph representation learning approach, effectively integrates multiple omics data, including biological network data, gene expression data, and sequence data that incorporates chemical molecular structures.
Qing Ye+10 more
wiley +1 more source
Machine Learning‐Enhanced Optimization for High‐Throughput Precision in Cellular Droplet Bioprinting
A high‐throughput droplet‐based cellular bioprinting platform enhanced with machine learning optimizes five key printing parameters—bioink viscosity, nozzle size, printing time, printing pressure, and cell concentration—to enable accurate prediction of droplet size.
Jaemyung Shin+7 more
wiley +1 more source
Actionable Forecasting as a Determinant of Biological Adaptation
A new framework reveals how biological systems can achieve precise adaptation by tracking an actionable target that combines the current optimal state with its rate of change. This approach, implemented through dynamics‐informed neural networks, demonstrates that predictive mechanisms like circadian rhythms become beneficial when environmental sensing ...
Jose M. G. Vilar, Leonor Saiz
wiley +1 more source
Graphs for Margins of Bayesian Networks [PDF]
AbstractDirected acyclic graph (DAG) models—also called Bayesian networks—are widely used in probabilistic reasoning, machine learning and causal inference. If latent variables are present, then the set of possible marginal distributions over the remaining (observed) variables is generally not represented by any DAG.
openaire +3 more sources
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data [PDF]
David Heckerman+2 more
openalex +1 more source
Machine Learning‐Enabled Drug‐Induced Toxicity Prediction
Unexpected toxicity accounts for 30% of drug development failures. This review highlights ML innovations in predicting drug‐induced toxicity, emphasizing comparative analyses, interpretable algorithms, and multi‐source data integration. It categorizes toxicity types, summarizes ML models, and organizes key databases, offering strategies to address ...
Changsen Bai+5 more
wiley +1 more source