Genetics

High-throughput single-cell RNA-seq data imputation and characterization with surrogate-assisted automated deep learning.

Single-cell RNA sequencing (scRNA-seq) technologies have been heavily developed to probe gene expression profiles at single-cell resolution. Deep imputation methods have been proposed to address the related computational challenges (e.g. the gene sparsity in single-cell data). In particular, the neural architectures of those deep imputation models have been proven to be critical for performance. H...

circRNA-binding protein site prediction based on multi-view deep learning, subspace learning and multi-view classifier.

Circular RNAs (circRNAs) generally bind to RNA-binding proteins (RBPs) to play an important role in the regulation of autoimmune diseases. Thus, it is crucial to study the binding sites of RBPs on circRNAs. Although many methods, including traditional machine learning and deep learning, have been developed to predict the interactions between RNAs and RBPs, and most of them are focused on linear RN...

Assessing deep learning methods in cis-regulatory motif finding based on genomic sequencing data.

Identifying cis-regulatory motifs from genomic sequencing data (e.g. ChIP-seq and CLIP-seq) is crucial in identifying transcription factor (TF) bindin...

Deep learning-based advances and applications for single-cell RNA-sequencing data analysis.

The rapid development of single-cell RNA-sequencing (scRNA-seq) technology has raised significant computational and analytical challenges. The applica...

NmRF: identification of multispecies RNA 2'-O-methylation modification sites from RNA sequences.

2'-O-methylation (Nm) is a post-transcriptional modification of RNA that is catalyzed by 2'-O-methyltransferase and involves replacing the H on the 2'...

DeepDISOBind: accurate prediction of RNA-, DNA- and protein-binding intrinsically disordered residues with deep multi-task learning.

Proteins with intrinsically disordered regions (IDRs) are common among eukaryotes. Many IDRs interact with nucleic acids and proteins. Annotation of t...

Protein-RNA interaction prediction with deep learning: structure matters.

Protein-RNA interactions are of vital importance to a variety of cellular activities. Both experimental and computational techniques have been develop...

Deep learning tackles single-cell analysis-a survey of deep learning for scRNA-seq analysis.

Since its selection as the method of the year in 2013, single-cell technologies have become mature enough to provide answers to complex research quest...

Prediction of disease-associated nsSNPs by integrating multi-scale ResNet models with deep feature fusion.

More than 6000 human diseases have been recorded to be caused by non-synonymous single nucleotide polymorphisms (nsSNPs). Rapid and accurate predictio...

Heavy chain sequence-based classifier for the specificity of human antibodies.

Antibodies specifically bind to antigens and are an essential part of the immune system. Hence, antibodies are powerful tools in research and diagnost...

SIGNET: single-cell RNA-seq-based gene regulatory network prediction using multiple-layer perceptron bagging.

High-throughput single-cell RNA-seq data have provided unprecedented opportunities for deciphering the regulatory interactions among genes. However, s...

Detection of transcription factors binding to methylated DNA by deep recurrent neural network.

Transcription factors (TFs) are proteins specifically involved in gene expression regulation. It is generally accepted in epigenetics that methylated ...

scMRA: a robust deep learning method to annotate scRNA-seq data with multiple reference datasets.

MOTIVATION: Single-cell RNA-seq (scRNA-seq) has been widely used to resolve cellular heterogeneity. After collecting scRNA-seq data, the natural next ...

EDCNN: identification of genome-wide RNA-binding proteins using evolutionary deep convolutional neural network.

MOTIVATION: RNA-binding proteins (RBPs) are a group of proteins associated with RNA regulation and metabolism, and play an essential role in mediating...

[Next-generation diagnostic pathology].

With the technological progresses and applications of human genome sequencing, bioinformatics analysis and data mining, and molecular pathology and ar...

LncRNAWiki 2.0: a knowledgebase of human long non-coding RNAs with enhanced curation model and database system.

LncRNAWiki, a knowledgebase of human long non-coding RNAs (lncRNAs), has been rapidly expanded by incorporating more experimentally validated lncRNAs....

scEnhancer: a single-cell enhancer resource with annotation across hundreds of tissue/cell types in three species.

Previous studies on enhancers and their target genes were largely based on bulk samples that represent 'average' regulatory activities from a large po...

The reactome pathway knowledgebase 2022.

The Reactome Knowledgebase (https://reactome.org), an Elixir core resource, provides manually curated molecular details across a broad range of physio...

lncRNAfunc: a knowledgebase of lncRNA function in human cancer.

The long non-coding RNAs associating with other molecules can coordinate several physiological processes and their dysfunction can impact diverse huma...

A Deep-Learning Approach for Inference of Selective Sweeps from the Ancestral Recombination Graph.

Detecting signals of selection from genomic data is a central problem in population genetics. Coupling the rich information in the ancestral recombina...