Genetics

miCGR: interpretable deep neural network for predicting both site-level and gene-level functional targets of microRNA.

MicroRNAs (miRNAs) are critical regulators in various biological processes to cleave or repress translation of messenger RNAs (mRNAs). Accurately predicting miRNA targets is essential for developing miRNA-based therapies for diseases such as cancer and cardiovascular disease. Traditional miRNA target prediction methods often struggle due to incomplete knowledge of miRNA-target interactions and lac...

FunlncModel: integrating multi-omic features from upstream and downstream regulatory networks into a machine learning framework to identify functional lncRNAs.

Accumulating evidence indicates that long noncoding RNAs (lncRNAs) play important roles in molecular and cellular biology. Although many algorithms have been developed to reveal their associations with complex diseases by using downstream targets, the upstream (epi)genetic regulatory information has not been sufficiently leveraged to predict the function of lncRNAs in various biological processes....

Bayesian unsupervised clustering identifies clinically relevant osteosarcoma subtypes.

Identification of cancer subtypes is a critical step for developing precision medicine. Most cancer subtyping is based on the analysis of RNA sequenci...

A review of deep learning models for the prediction of chromatin interactions with DNA and epigenomic profiles.

Advances in three-dimensional (3D) genomics have revealed the spatial characteristics of chromatin interactions in gene expression regulation, which i...

scRGCL: a cell type annotation method for single-cell RNA-seq data using residual graph convolutional neural network with contrastive learning.

Cell type annotation is a critical step in analyzing single-cell RNA sequencing (scRNA-seq) data. A large number of deep learning (DL)-based methods h...

Machine learning-enabled virtual screening indicates the anti-tuberculosis activity of aldoxorubicin and quarfloxin with verification by molecular docking, molecular dynamics simulations, and biological evaluations.

Drug resistance in Mycobacterium tuberculosis (Mtb) is a significant challenge in the control and treatment of tuberculosis, making efforts to combat ...

KPRR: a novel machine learning approach for effectively capturing nonadditive effects in genomic prediction.

Nonadditive genetic effects pose significant challenges to traditional genomic selection methods for quantitative traits. Machine learning approaches,...

RiceSNP-ABST: a deep learning approach to identify abiotic stress-associated single nucleotide polymorphisms in rice.

Given the adverse effects faced by rice due to abiotic stresses, the precise and rapid identification of single nucleotide polymorphisms (SNPs) associ...

Integrating scRNA-seq and scATAC-seq with inter-type attention heterogeneous graph neural networks.

Single-cell multi-omics techniques, which enable the simultaneous measurement of multiple modalities such as RNA gene expression and Assay for Transpo...

Deep learning in integrating spatial transcriptomics with other modalities.

Spatial transcriptomics technologies have been extensively applied in biological research, enabling the study of transcriptome while preserving the sp...

scGO: interpretable deep neural network for cell status annotation and disease diagnosis.

Machine learning has emerged as a transformative tool for elucidating cellular heterogeneity in single-cell RNA sequencing. However, a significant cha...

scHiClassifier: a deep learning framework for cell type prediction by fusing multiple feature sets from single-cell Hi-C data.

Single-cell high-throughput chromosome conformation capture (Hi-C) technology enables capturing chromosomal spatial structure information at the cellu...

Inferring the genetic relationships between unsupervised deep learning-derived imaging phenotypes and glioblastoma through multi-omics approaches.

This study aimed to investigate the genetic association between glioblastoma (GBM) and unsupervised deep learning-derived imaging phenotypes (UDIPs). ...

Classification-based pathway analysis using GPNet with novel P-value computation.

Pathway analysis plays a critical role in bioinformatics, enabling researchers to identify biological pathways associated with various conditions by a...

Introducing TEC-LncMir for prediction of lncRNA-miRNA interactions through deep learning of RNA sequences.

The interactions between long noncoding RNA (lncRNA) and microRNA (miRNA) play critical roles in life processes, highlighting the necessity to enhance...

Inferring tumor purity using multi-omics data based on a uniform machine learning framework MoTP.

Existing algorithms for assessing tumor purity are limited to a single omics data, such as gene expression, somatic copy number variations, somatic mu...

Learning genotype-phenotype associations from gaps in multi-species sequence alignments.

Understanding the genetic basis of phenotypic variation is fundamental to biology. Here we introduce GAP, a novel machine learning framework for predi...

DECA: harnessing interpretable transformer model for cellular deconvolution of chromatin accessibility profile.

The assay for transposase-accessible chromatin with sequencing (ATAC-seq) identifies chromatin accessibility across the genome, crucial for gene expre...

Noninvasive fetal genotyping using deep neural networks.

Circulating cell-free DNA (cfDNA) is a powerful diagnostics tool that is widely studied in the context of liquid biopsy in oncology and other fields. ...

Reducibility among NP-Hard graph problems and boundary classes

Many NP-hard graph problems become easy for some classes of graphs, such as coloring is easy for bipartite graphs, but NP-hard in general. So we can...