Genetics

Latest AI and machine learning research in genetics for healthcare professionals.

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SGCLMD: Signed graph-based contrastive learning model for predicting somatic mutation-drug association.

Somatic mutations could influence critical cellular processes, leading to uncontrolled cell growth a...

Quantitative prediction of disinfectant tolerance in Listeria monocytogenes using whole genome sequencing and machine learning.

Listeria monocytogenes is a potentially severe disease-causing bacteria mainly transmitted through f...

Mechanisms Tackling Salivary Gland Diseases with Extracellular Vesicle Therapies.

Extracellular vesicles (EVs) are lipid-enclosed particles released from cells, containing lipids, DN...

Hybrid CNN and random forest model with late fusion for detection of autism spectrum disorder in Toddlers.

Accurate and early diagnosis of Autism Spectrum Disorder (ASD) in toddlers is crucial for effective ...

Development of an external quality assurance (EQA) structure to evaluate the quality of genetic pathology reporting.

A standard for reporting genetic pathology results currently does not exist as a consensus. While ef...

Can AI reveal the next generation of high-impact bone genomics targets?

Genetic studies have revealed hundreds of loci associated with bone-related phenotypes, including bo...

Two-tier nature inspired optimization-driven ensemble of deep learning models for effective autism spectrum disorder diagnosis in disabled persons.

Autism spectrum disorder (ASD) includes a varied set of neuropsychiatric illnesses. This disorder is...

Brain tumor intelligent diagnosis based on Auto-Encoder and U-Net feature extraction.

Preoperative classification of brain tumors is critical to developing personalized treatment plans, ...

Which approach better predicts diabetes: Traditional econometric methods or machine learning? Evidence from a cross-sectional study in South Korea.

To prevent chronic disease from getting worse, it is important to detect and predict it at an early ...

Liver Tumor Prediction using Attention-Guided Convolutional Neural Networks and Genomic Feature Analysis.

The task of predicting liver tumors is critical as part of medical image analysis and genomics area ...

CasPro-ESM2: Accurate identification of Cas proteins integrating pre-trained protein language model and multi-scale convolutional neural network.

Cas proteins (CRISPR-associated protein) are the core components of the CRISPR-Cas system, playing c...

Deep-ProBind: binding protein prediction with transformer-based deep learning model.

Binding proteins play a crucial role in biological systems by selectively interacting with specific ...

Reconstructing 3D chromosome structures from single-cell Hi-C data with SO(3)-equivariant graph neural networks.

The spatial conformation of chromosomes and genomes of single cells is relevant to cellular function...

Systems biology of Haemonchus contortus - Advancing biotechnology for parasitic nematode control.

Parasitic nematodes represent a substantial global burden, impacting animal health, agriculture and ...

Assessment for antibiotic resistance in : A practical and interpretable machine learning model based on genome-wide genetic variation.

() antibiotic resistance poses a global health threat. Accurate identification of antibiotic resist...

Machine Learning and Mendelian Randomization Reveal a Tumor Immune Cell Profile for Predicting Bladder Cancer Risk and Immunotherapy Outcomes.

This study's objective was to develop predictive models for bladder cancer (BLCA) using tumor infilt...

RNAmigos2: accelerated structure-based RNA virtual screening with deep graph learning.

RNAs are a vast reservoir of untapped drug targets. Structure-based virtual screening (VS) identifie...

The relationship between epigenetic biomarkers and the risk of diabetes and cancer: a machine learning modeling approach.

INTRODUCTION: Epigenetic biomarkers are molecular indicators of epigenetic changes, and some studies...

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