Genetics

Joint graphical model estimation using Stein-type shrinkage for fast large scale network inference in scRNAseq data

Graphical modeling is a widely used tool for analyzing conditional dependencies between variables and traditional methods may struggle to capture shared and distinct structures in multi-group or multi-condition settings. Joint graphical modeling (JGM) extends this framework by simultaneously estimating network structures across multiple related datasets, allowing for a deeper understanding of co...

Large Language Models in Bioinformatics: A Survey

Large Language Models (LLMs) are revolutionizing bioinformatics, enabling advanced analysis of DNA, RNA, proteins, and single-cell data. This survey provides a systematic review of recent advancements, focusing on genomic sequence modeling, RNA structure prediction, protein function inference, and single-cell transcriptomics. Meanwhile, we also discuss several key challenges, including data scar...

Biological Sequence with Language Model Prompting: A Survey

Large Language models (LLMs) have emerged as powerful tools for addressing challenges across diverse domains. Notably, recent studies have demonstra...

TEDDY: A Family Of Foundation Models For Understanding Single Cell Biology

Understanding the biological mechanism of disease is critical for medicine, and in particular drug discovery. AI-powered analysis of genome-scale bi...

Enabling Fast, Accurate, and Efficient Real-Time Genome Analysis via New Algorithms and Techniques

The advent of high-throughput sequencing technologies has revolutionized genome analysis by enabling the rapid and cost-effective sequencing of larg...

COME: contrastive mapping learning for spatial reconstruction of single-cell RNA sequencing data.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) enables high-throughput transcriptomic profiling at single-cell resolution. The inherent spatial lo...

Cox-Sage: enhancing Cox proportional hazards model with interpretable graph neural networks for cancer prognosis.

High-throughput sequencing technologies have facilitated a deeper exploration of prognostic biomarkers. While many deep learning (DL) methods primaril...

Optimizing sample size for supervised machine learning with bulk transcriptomic sequencing: a learning curve approach.

Accurate sample classification using transcriptomics data is crucial for advancing personalized medicine. Achieving this goal necessitates determining...

DRAG: design RNAs as hierarchical graphs with reinforcement learning.

The rapid development of RNA vaccines and therapeutics puts forward intensive requirements on the sequence design of RNAs. RNA sequence design, or RNA...

Deep learning-driven survival prediction in pan-cancer studies by integrating multimodal histology-genomic data.

Accurate cancer prognosis is essential for personalized clinical management, guiding treatment strategies and predicting patient survival. Conventiona...

BAMBI integrates biostatistical and artificial intelligence methods to improve RNA biomarker discovery.

RNA biomarkers enable early and precise disease diagnosis, monitoring, and prognosis, facilitating personalized medicine and targeted therapeutic stra...

MethPriorGCN: a deep learning tool for inferring DNA methylation prior knowledge and guiding personalized medicine.

DNA methylation plays a crucial role in human diseases pathogenesis. Substantial experimental evidence from clinical and biological studies has confir...

MUTATE: a human genetic atlas of multiorgan artificial intelligence endophenotypes using genome-wide association summary statistics.

Artificial intelligence (AI) has been increasingly integrated into imaging genetics to provide intermediate phenotypes (i.e. endophenotypes) that brid...

EMcnv: enhancing CNV detection performance through ensemble strategies with heterogeneous meta-graph neural networks.

Copy number variation (CNV) is a crucial biomarker for many complex traits and diseases. Although numerous CNV detection tools are available, no singl...

DOMSCNet: a deep learning model for the classification of stomach cancer using multi-layer omics data.

The rapid advancement of next-generation sequencing (NGS) technology and the expanding availability of NGS datasets have led to a significant surge in...

CoupleVAE: coupled variational autoencoders for predicting perturbational single-cell RNA sequencing data.

With the rapid advances in single-cell sequencing technology, it is now feasible to conduct in-depth genetic analysis in individual cells. Study on th...

Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.

The development of single-cell and spatial transcriptomics has revolutionized our capacity to investigate cellular properties, functions, and interact...

A graph neural network approach for accurate prediction of pathogenicity in multi-type variants.

Accurate prediction of pathogenic variants in human disease-associated genes would have a profound effect on clinical decision-making; however, it rem...

Mutual-assistance learning for trustworthy biomarker discovery and disease prediction.

Integrating and analyzing multiple omics datasets, such as genomics, environmental influences, and imaging endophenotypes, has yielded an abundance of...

WheatGP, a genomic prediction method based on CNN and LSTM.

Wheat plays a crucial role in ensuring food security. However, its complex genetic structure and trait variation pose significant challenges for breed...