AIMC Topic: Bayes Theorem

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Association between metal mixture in urine and abnormal blood pressure and mediated effect of oxidative stress based on BKMR and Machine learning method.

Ecotoxicology and environmental safety
BACKGROUND: Exposure to heavy metals represents a significant risk factor for hypertension and blood pressure disorders. Notably, current evidence indicates that the key biological processes of oxidative stress, inflammation, and endothelial dysfunct...

Enhancing PI3Kγ inhibitor discovery: a machine learning-based virtual screening approach integrating pharmacophores, docking, and molecular descriptors.

Molecular diversity
PI3Kγ is a lipid kinase that is expressed primarily in leukocytes and plays a significant role in tumors, inflammation, and autoimmune diseases. Consequently, considerable attention has been given to the development of pharmacological inhibitors of P...

Data-driven insights for enhanced cellulose conversion to 5-hydroxymethylfurfural using machine learning.

Bioresource technology
Converting cellulose into 5-Hydroxymethylfurfural (HMF) provides a promising strategy for creating bio-based chemicals, offering sustainable alternatives to petroleum-based materials in polymers, biofuels, and pharmaceuticals. However, the efficient ...

Predicting rheumatoid arthritis in the middle-aged and older population using patient-reported outcomes: insights from the SHARE cohort.

International journal of medical informatics
BACKGROUND: In light of global population aging and the increasing prevalence of Rheumatoid Arthritis (RA) with age, strategies are needed to address this public health challenge. Machine learning (ML) may play a vital role in early identification of...

A Bayesian Optimization-Based Hybrid Deep Prediction Method for Zinc-Binding Protein Interaction Sites.

Journal of chemical information and modeling
The binding of zinc ions to proteins plays a crucial role in normal physiological functions and life activities of organisms. To enhance the prediction accuracy of zinc-binding protein interaction sites, the paper proposes a novel hybrid deep predict...

Assessing Uncertainty in Machine Learning for Polymer Property Prediction: A Benchmark Study.

Journal of chemical information and modeling
Machine learning (ML) has emerged as a transformative tool in material science, enabling accelerated discovery and design of novel molecules while reducing experimental costs. Uncertainty quantification (UQ) is crucial for enhancing the reliability o...

VBayesMM: variational Bayesian neural network to prioritize important relationships of high-dimensional microbiome multiomics data.

Briefings in bioinformatics
The analysis of high-dimensional microbiome multiomics datasets is crucial for understanding the complex interactions between microbial communities and host physiological states across health and disease conditions. Despite their importance, current ...

A deep learning-based method for predicting the frequency classes of drug side effects based on multi-source similarity fusion.

Bioinformatics (Oxford, England)
MOTIVATION: Drug side effects refer to harmful or adverse reactions that occur during drug use, unrelated to the therapeutic purpose. A core issue in drug side effect prediction is determining the frequency of these drug side effects in the populatio...

A Bayesian Approach to the G-Formula via Iterative Conditional Regression.

Statistics in medicine
In longitudinal observational studies with time-varying confounders, the generalized computation algorithm formula (g-formula) is a principled tool to estimate the average causal effect of a treatment regimen. However, the standard non-iterative g-fo...

Construction and Validation of Artificial Neural Network Model Suggesting Nursing Diagnosis: A Proof-of-Concept Study.

Computers, informatics, nursing : CIN
There are challenges involving human resource management, as the selection and evaluation processes for nursing diagnostic labels are time-consuming, resulting in an excessive workload. This, in turn, can lead to insufficient attention being given to...