Genetics

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 stage. Therefore, the accuracy of the prediction is particularly important. To investigate the accuracy of different methods, this study compares the out-of-sample errors of machine learning algorithms and traditional econometric methods in predicting diabetes. The object of prediction in this study...

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 since diagnosis and prognosis are important in making correct medical decisions. Silent characteristics of liver tumors and interactions between genomic and imaging features are also the main sources of challenges toward reliable predictions. To overcome these hurdles, this study presents two integr...

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 critical roles in defending against foreign DNA and...

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 molecules, such as DNA, RNA, or peptides, to regul...

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 and useful for elucidating the mechanism underlyi...

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

Parasitic nematodes represent a substantial global burden, impacting animal health, agriculture and economies worldwide. Of these worms, Haemonchus co...

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 resistant strains is essential for the control of infect...

Incorporating time as a third dimension in transcriptomic analysis using machine learning and explainable AI.

Transcriptomic data analysis entails the measurement of RNA transcript (gene expression products) abundance in a cell or a cell population at a single...

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 infiltrated immune cell (TIIC)-related genes. Multiple R...

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) identifies candidate molecules by leveraging binding site i...

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 have suggested that these biomarkers have predict...

GANDALF: Generative ANsatz for DNA damage evALuation and Forecast. A neural network-based regression for estimating early DNA damage across micro-nano scales.

PURPOSE: This study aims to develop a comprehensive simulation framework to connect radiation effects from the microscopic to the nanoscopic scale.

Psychedelic Drugs in Mental Disorders: Current Clinical Scope and Deep Learning-Based Advanced Perspectives.

Mental disorders are a representative type of brain disorder, including anxiety, major depressive depression (MDD), and autism spectrum disorder (ASD)...

A fusion model to predict the survival of colorectal cancer based on histopathological image and gene mutation.

Colorectal cancer (CRC) is a prevalent gastrointestinal tumor worldwide with high morbidity and mortality. Predicting the survival of CRC patients not...

Identification and Experimental Validation of NETosis-Mediated Abdominal Aortic Aneurysm Gene Signature Using Multi-omics, Machine Learning, and Mendelian Randomization.

Abdominal aortic aneurysm (AAA) is a life-threatening disorder with limited therapeutic options. Neutrophil extracellular traps (NETs) are formed by a...

Mouse-Geneformer: A deep learning model for mouse single-cell transcriptome and its cross-species utility.

Deep learning techniques are increasingly utilized to analyze large-scale single-cell RNA sequencing (scRNA-seq) data, offering valuable insights from...

CacPred: a cascaded convolutional neural network for TF-DNA binding prediction.

BACKGROUND: Transcription factors (TFs) regulate the genes' expression by binding to DNA sequences. Aligned TFBSs of the same TF are seen as cis-regul...

Pharmacological potential of bioactive compounds in extract: A comprehensive review.

The pharmacological potential of bioactive compounds found in the plant, , have diverse medicinal applications. Key constituents such as vinblastine a...

Accurate Prediction of CRISPR/Cas13a Guide Activity Using Feature Selection and Deep Learning.

CRISPR/Cas13a serves as a key tool for nucleic acid tests; therefore, accurate prediction of its activity is essential for creating robust and sensiti...

A novel seven-tier framework for the classification of MEFV missense variants using adaptive and rigid classifiers.

There is a great discrepancy between the clinical categorization of MEFV gene variants and in silico tool predictions. In this study, we developed a s...