Genetics

MRI Radiomics for IDH Genotype Prediction in Glioblastoma Diagnosis

Radiomics is a relatively new field which utilises automatically identified features from radiological scans. It has found a widespread application, particularly in oncology because many of the important oncological biomarkers are not visible to the naked eye. The recent advent of big data, including in medical imaging, and the development of new ML techniques brought the possibility of faster a...

DeepPBI-KG: a deep learning method for the prediction of phage-bacteria interactions based on key genes.

Phages, the natural predators of bacteria, were discovered more than 100 years ago. However, increasing antimicrobial resistance rates have revitalized phage research. Methods that are more time-consuming and efficient than wet-laboratory experiments are needed to help screen phages quickly for therapeutic use. Traditional computational methods usually ignore the fact that phage-bacteria interacti...

MLSNet: a deep learning model for predicting transcription factor binding sites.

Accurate prediction of transcription factor binding sites (TFBSs) is essential for understanding gene regulation mechanisms and the etiology of diseas...

MultiSC: a deep learning pipeline for analyzing multiomics single-cell data.

Single-cell technologies enable researchers to investigate cell functions at an individual cell level and study cellular processes with higher resolut...

Predicting functional outcome in ischemic stroke patients using genetic, environmental, and clinical factors: a machine learning analysis of population-based prospective cohort study.

Ischemic stroke (IS) is a leading cause of adult disability that can severely compromise the quality of life for patients. Accurately predicting the I...

m6ATM: a deep learning framework for demystifying the m6A epitranscriptome with Nanopore long-read RNA-seq data.

N6-methyladenosine (m6A) is one of the most abundant and well-known modifications in messenger RNAs since its discovery in the 1970s. Recent studies h...

Structure-preserved integration of scRNA-seq data using heterogeneous graph neural network.

The integration of single-cell RNA sequencing (scRNA-seq) data from multiple experimental batches enables more comprehensive characterizations of cell...

Multi-view learning framework for predicting unknown types of cancer markers via directed graph neural networks fitting regulatory networks.

The discovery of diagnostic and therapeutic biomarkers for complex diseases, especially cancer, has always been a central and long-term challenge in m...

Semi-supervised learning with pseudo-labeling compares favorably with large language models for regulatory sequence prediction.

Predicting molecular processes using deep learning is a promising approach to provide biological insights for non-coding single nucleotide polymorphis...

siRNADiscovery: a graph neural network for siRNA efficacy prediction via deep RNA sequence analysis.

The clinical adoption of small interfering RNAs (siRNAs) has prompted the development of various computational strategies for siRNA design, from tradi...

scDTL: enhancing single-cell RNA-seq imputation through deep transfer learning with bulk cell information.

The increasing single-cell RNA sequencing (scRNA-seq) data enable researchers to explore cellular heterogeneity and gene expression profiles, offering...

Predicting bacterial transcription factor binding sites through machine learning and structural characterization based on DNA duplex stability.

Transcriptional factors (TFs) in bacteria play a crucial role in gene regulation by binding to specific DNA sequences, thereby assisting in the activa...

Precision DNA methylation typing via hierarchical clustering of Nanopore current signals and attention-based neural network.

Decoding DNA methylation sites through nanopore sequencing has emerged as a cutting-edge technology in the field of DNA methylation research, as it en...

AIGen: an artificial intelligence software for complex genetic data analysis.

The recent development of artificial intelligence (AI) technology, especially the advance of deep neural network (DNN) technology, has revolutionized ...

Robust self-supervised learning strategy to tackle the inherent sparsity in single-cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for elucidating cellular heterogeneity and tissue function in various biological contexts. H...

Nmix: a hybrid deep learning model for precise prediction of 2'-O-methylation sites based on multi-feature fusion and ensemble learning.

RNA 2'-O-methylation (Nm) is a crucial post-transcriptional modification with significant biological implications. However, experimental identificatio...

RiceSNP-BST: a deep learning framework for predicting biotic stress-associated SNPs in rice.

Rice consistently faces significant threats from biotic stresses, such as fungi, bacteria, pests, and viruses. Consequently, accurately and rapidly id...

PRAGA: Prototype-aware Graph Adaptive Aggregation for Spatial Multi-modal Omics Analysis

Spatial multi-modal omics technology, highlighted by Nature Methods as an advanced biological technique in 2023, plays a critical role in resolving ...

Recent advances in deep learning and language models for studying the microbiome

Recent advancements in deep learning, particularly large language models (LLMs), made a significant impact on how researchers study microbiome and m...

Highly conserved sequence-specific double-stranded DNA binding networks contributing to divergent genomic evolution of human and chimpanzee brain development

Emergence during mammalian evolution of concordant and divergent traits of genomic regulatory networks encompassing ubiquitous, qualitatively nearly...