Genetics

Deep learning tools are top performers in long non-coding RNA prediction.

The increasing amount of transcriptomic data has brought to light vast numbers of potential novel RNA transcripts. Accurately distinguishing novel long non-coding RNAs (lncRNAs) from protein-coding messenger RNAs (mRNAs) has challenged bioinformatic tool developers. Most recently, tools implementing deep learning architectures have been developed for this task, with the potential of discovering se...

Deep learning based on biologically interpretable genome representation predicts two types of human adaptation of SARS-CoV-2 variants.

Explosively emerging SARS-CoV-2 variants challenge current nomenclature schemes based on genetic diversity and biological significance. Genomic composition-based machine learning methods have recently performed well in identifying phenotype-genotype relationships. We introduced a framework involving dinucleotide (DNT) composition representation (DCR) to parse the general human adaptation of RNA vi...

SPLDExtraTrees: robust machine learning approach for predicting kinase inhibitor resistance.

Drug resistance is a major threat to the global health and a significant concern throughout the clinical treatment of diseases and drug development. T...

RNAI-FRID: novel feature representation method with information enhancement and dimension reduction for RNA-RNA interaction.

Different ribonucleic acids (RNAs) can interact to form regulatory networks that play important role in many life activities. Molecular biology experi...

S2Snet: deep learning for low molecular weight RNA identification with nanopore.

Ribonucleic acid (RNA) is a pivotal nucleic acid that plays a crucial role in regulating many biological activities. Recently, one study utilized a ma...

A tool for feature extraction from biological sequences.

With the advances in sequencing technologies, a huge amount of biological data is extracted nowadays. Analyzing this amount of data is beyond the abil...

CRISPR-Cas9 gRNA efficiency prediction: an overview of predictive tools and the role of deep learning.

The clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9) system has become a successful and promising ...

Direct Discriminative Decoder Models for Analysis of High-Dimensional Dynamical Neural Data.

With the accelerated development of neural recording technology over the past few decades, research in integrative neuroscience has become increasingl...

PST-PRNA: prediction of RNA-binding sites using protein surface topography and deep learning.

MOTIVATION: Protein-RNA interactions play essential roles in many biological processes, including pre-mRNA processing, post-transcriptional gene regul...

DNAcycP: a deep learning tool for DNA cyclizability prediction.

DNA mechanical properties play a critical role in every aspect of DNA-dependent biological processes. Recently a high throughput assay named loop-seq ...

Improving the noninvasive classification of glioma genetic subtype with deep learning and diffusion-weighted imaging.

BACKGROUND: Diagnostic classification of diffuse gliomas now requires an assessment of molecular features, often including IDH-mutation and 1p19q-code...

Extreme learning machine and genetic algorithm in quantitative analysis of sulfur hexafluoride by infrared spectroscopy.

Owing to the general disadvantages of traditional neural networks in gas concentration inversion, such as slow training speed, sensitive learning rate...

Tox-GAN: An Artificial Intelligence Approach Alternative to Animal Studies-A Case Study With Toxicogenomics.

Animal studies are a critical component in biomedical research, pharmaceutical product development, and regulatory submissions. There is a worldwide e...

Evaluating the predictive accuracy of curated biological pathways in a public knowledgebase.

ABSTRACT: Reactome is a database of human biological pathways manually curated from the primary literature and peer-reviewed by experts. To evaluate t...

Machine learning on thyroid disease: a review.

This study reviews the recent progress of machine learning for the early diagnosis of thyroid disease. Based on the results of this review, different ...

CapsNet-COVID19: Lung CT image classification method based on CapsNet model.

The outbreak of the Corona Virus Disease 2019 (COVID-19) has posed a serious threat to human health and life around the world. As the number of COVID-...

A robust and scalable graph neural network for accurate single-cell classification.

Single-cell RNA sequencing (scRNA-seq) techniques provide high-resolution data on cellular heterogeneity in diverse tissues, and a critical step for t...

A hybrid deep learning framework for gene regulatory network inference from single-cell transcriptomic data.

Inferring gene regulatory networks (GRNs) based on gene expression profiles is able to provide an insight into a number of cellular phenotypes from th...

Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network.

Single-cell RNA sequencing (scRNA-seq) permits researchers to study the complex mechanisms of cell heterogeneity and diversity. Unsupervised clusterin...

SVPath: an accurate pipeline for predicting the pathogenicity of human exon structural variants.

Although there are a large number of structural variations in the chromosomes of each individual, there is a lack of more accurate methods for identif...