Genetics

Machine learning identifies key cells and therapeutic targets during ferroptosis after spinal cord injury.

JOURNAL/nrgr/04.03/01300535-202606000-00064/figure1/v/2026-02-11T151048Z/r/image-tiff Ferroptosis, a type of cell death that mainly involves iron metabolism imbalance and lipid peroxidation, is strongly correlated with the phagocytic response caused by bleeding after spinal cord injury. Thus, in this study, bulk RNA sequencing data (GSE47681 and GSE5296) and single-cell RNA sequencing data (GSE162...

Detecting differentially expressed genes from RNA-seq data using fuzzy clustering.

A two-group comparison test is generally performed on RNA sequencing data to detect differentially expressed genes (DEGs). However, the accuracy of this method is low due to the small sample size. To address this, we propose a method using fuzzy clustering that artificially generates data with expression patterns similar to those of DEGs to identify genes that are highly likely to be classified in...

Utilizing machine learning and bioinformatics analysis to identify drought-responsive genes affecting yield in foxtail millet.

Drought stress is a major constraint on crop development, potentially causing huge yield losses and threatening global food security. Improving Crop's...

Exploring the roles of RNAs in chromatin architecture using deep learning.

Recent studies have highlighted the impact of both transcription and transcripts on 3D genome organization, particularly its dynamics. Here, we propos...

Machine learning-guided co-optimization of fitness and diversity facilitates combinatorial library design in enzyme engineering.

The effective design of combinatorial libraries to balance fitness and diversity facilitates the engineering of useful enzyme functions, particularly ...

Differentially used codons among essential genes in bacteria identified by machine learning-based analysis.

Codon usage bias (CUB), the uneven usage of synonymous codons encoding the same amino acid, differs among genes within and across bacteria genomes. CU...

Elucidating Microglial Heterogeneity and Functions in Alzheimer's Disease Using Single-cell Analysis and Convolutional Neural Network Disease Model Construction.

In this study, we conducted an in-depth exploration of Alzheimer's Disease (AD) by integrating state-of-the-art methodologies, including single-cell R...

DeepPGD: A Deep Learning Model for DNA Methylation Prediction Using Temporal Convolution, BiLSTM, and Attention Mechanism.

As part of the field of DNA methylation identification, this study tackles the challenge of enhancing recognition performance by introducing a special...

Single-cell RNA sequencing data analysis utilizing multi-type graph neural networks.

Single-cell RNA sequencing (scRNA-seq) is the sequencing technology of a single cell whose expression reflects the overall characteristics of the indi...

AAontology: An Ontology of Amino Acid Scales for Interpretable Machine Learning.

Amino acid scales are crucial for protein prediction tasks, many of them being curated in the AAindex database. Despite various clustering attempts to...

A personalized mRNA signature for predicting hypertrophic cardiomyopathy applying machine learning methods.

Hypertrophic cardiomyopathy (HCM) may lead to cardiac dysfunction and sudden death. This study was designed to develop a HCM signature applying bioinf...

Robust evaluation of deep learning-based representation methods for survival and gene essentiality prediction on bulk RNA-seq data.

Deep learning (DL) has shown potential to provide powerful representations of bulk RNA-seq data in cancer research. However, there is no consensus reg...

Exploring protein-mediated compaction of DNA by coarse-grained simulations and unsupervised learning.

Protein-DNA interactions and protein-mediated DNA compaction play key roles in a range of biological processes. The length scales typically involved i...

Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials.

Incorporating feature-engineered environmental data into machine learning-based genomic prediction models is an efficient approach to indirectly model...

Computational approaches for clinical, genomic and proteomic markers of response to glucagon-like peptide-1 therapy in type-2 diabetes mellitus: An exploratory analysis with machine learning algorithms.

INTRODUCTION: In 2021, the International Diabetes Federation reported that 537 million people worldwide are living with diabetes. While glucagon-like ...

EnzyACT: A Novel Deep Learning Method to Predict the Impacts of Single and Multiple Mutations on Enzyme Activity.

Enzyme engineering involves the customization of enzymes by introducing mutations to expand the application scope of natural enzymes. One limitation o...

DEL-Thyroid: deep ensemble learning framework for detection of thyroid cancer progression through genomic mutation.

Genes, expressed as sequences of nucleotides, are susceptible to mutations, some of which can lead to cancer. Machine learning and deep learning metho...

TabDEG: Classifying differentially expressed genes from RNA-seq data based on feature extraction and deep learning framework.

Traditional differential expression genes (DEGs) identification models have limitations in small sample size datasets because they require meeting dis...

Deep learning-based IDH1 gene mutation prediction using histopathological imaging and clinical data.

In the field of histopathology, many studies on the classification of whole slide images (WSIs) using artificial intelligence (AI) technology have bee...

PMTPred: machine-learning-based prediction of protein methyltransferases using the composition of k-spaced amino acid pairs.

Protein methyltransferases (PMTs) are a group of enzymes that help catalyze the transfer of a methyl group to its substrates. These enzymes play an im...