AIMC Topic:
Cluster Analysis
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Decoupled GNNs based on multi-view contrastive learning for scRNA-seq data clustering.
Briefings in bioinformatics
May 1, 2025
Clustering is pivotal in deciphering cellular heterogeneity in single-cell RNA sequencing (scRNA-seq) data. However, it suffers from several challenges in handling the high dimensionality and complexity of scRNA-seq data. Especially when employing gr...
Advancing Nutritional Status Classification With Hybrid Artificial Intelligence: A Novel Methodological Approach.
Brain and behavior
May 1, 2025
PURPOSE: Malnutrition remains a critical public health issue in low-income countries, significantly hindering economic development and contributing to over 50% of infant deaths. Under nutrition weakens immune systems, increasing susceptibility to com...
Getting Started with Machine Learning for Experimental Biochemists and Other Molecular Scientists.
Current protocols
Apr 1, 2025
Machine learning (ML) is rapidly gaining traction in many areas of experimental molecular science for elucidating relationships and patterns in large or complex data sets. Historically, ML was largely the preserve of those with specialized training i...
MlyPredCSED: based on extreme point deviation compensated clustering combined with cross-scale convolutional neural networks to predict multiple lysine sites in human.
Briefings in bioinformatics
Mar 4, 2025
In post-translational modification, covalent bonds on lysine and attached chemical groups significantly change proteins' physical and chemical properties. They shape protein structures, enhance function and stability, and are vital for physiological ...
Analysis of eligibility criteria clusters based on large language models for clinical trial design.
Journal of the American Medical Informatics Association : JAMIA
Mar 1, 2025
OBJECTIVES: Clinical trials (CTs) are essential for improving patient care by evaluating new treatments' safety and efficacy. A key component in CT protocols is the study population defined by the eligibility criteria. This study aims to evaluate the...
A systematic exposure-wide framework leveraging machine learning to identify multidomain exposure factors and their joint influence on cognitive function: Evidence from a neurological cohort.
Alzheimer's & dementia : the journal of the Alzheimer's Association
Feb 1, 2025
INTRODUCTION: Cognitive decline has become a growing public concern, yet large-scale exposure data identifying the contributing factors remain limited.
Bayesian unsupervised clustering identifies clinically relevant osteosarcoma subtypes.
Briefings in bioinformatics
Nov 22, 2024
Identification of cancer subtypes is a critical step for developing precision medicine. Most cancer subtyping is based on the analysis of RNA sequencing (RNA-seq) data from patient cohorts using unsupervised machine learning methods such as hierarchi...
m6A-related genes and their role in Parkinson's disease: Insights from machine learning and consensus clustering.
Medicine
Nov 8, 2024
Parkinson disease (PD) is a chronic neurological disorder primarily characterized by a deficiency of dopamine in the brain. In recent years, numerous studies have highlighted the substantial influence of RNA N6-methyladenosine (m6A) regulators on var...
Clustering Electrophysiological Predisposition to Binge Drinking: An Unsupervised Machine Learning Analysis.
Brain and behavior
Nov 1, 2024
BACKGROUND: The demand for fresh strategies to analyze intricate multidimensional data in neuroscience is increasingly evident. One of the most complex events during our neurodevelopment is adolescence, where our nervous system suffers constant chang...
Machine learning-based clustering identifies obesity subgroups with differential multi-omics profiles and metabolic patterns.
Obesity (Silver Spring, Md.)
Nov 1, 2024
OBJECTIVE: Individuals living with obesity are differentially susceptible to cardiometabolic diseases. We hypothesized that an integrative multi-omics approach might improve identification of subgroups of individuals with obesity who have distinct ca...
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