Genetics

WEGO 2.0: a web tool for analyzing and plotting GO annotations, 2018 update.

WEGO (Web Gene Ontology Annotation Plot), created in 2006, is a simple but useful tool for visualizing, comparing and plotting GO (Gene Ontology) annotation results. Owing largely to the rapid development of high-throughput sequencing and the increasing acceptance of GO, WEGO has benefitted from outstanding performance regarding the number of users and citations in recent years, which motivated us...

pBRIT: gene prioritization by correlating functional and phenotypic annotations through integrative data fusion.

MOTIVATION: Computational gene prioritization can aid in disease gene identification. Here, we propose pBRIT (prioritization using Bayesian Ridge regression and Information Theoretic model), a novel adaptive and scalable prioritization tool, integrating Pubmed abstracts, Gene Ontology, Sequence similarities, Mammalian and Human Phenotype Ontology, Pathway, Interactions, Disease Ontology, Gene Asso...

Multiobjective multifactor dimensionality reduction to detect SNP-SNP interactions.

MOTIVATION: Single-nucleotide polymorphism (SNP)-SNP interactions (SSIs) are popular markers for understanding disease susceptibility. Multifactor dim...

Machine-learned analysis of the association of next-generation sequencing-based human TRPV1 and TRPA1 genotypes with the sensitivity to heat stimuli and topically applied capsaicin.

Heat pain and its modulation by capsaicin varies among subjects in experimental and clinical settings. A plausible cause is a genetic component, of wh...

RNA sequencing and machine learning as molecular scalpels.

Gene prioritization using Bayesian matrix factorization with genomic and phenotypic side information.

MOTIVATION: Most gene prioritization methods model each disease or phenotype individually, but this fails to capture patterns common to several diseas...

A pan-genome-based machine learning approach for predicting antimicrobial resistance activities of the Escherichia coli strains.

MOTIVATION: Antimicrobial resistance (AMR) is becoming a huge problem in both developed and developing countries, and identifying strains resistant or...

Convolutional neural networks for classification of alignments of non-coding RNA sequences.

MOTIVATION: The convolutional neural network (CNN) has been applied to the classification problem of DNA sequences, with the additional purpose of mot...

Classifying tumors by supervised network propagation.

MOTIVATION: Network propagation has been widely used to aggregate and amplify the effects of tumor mutations using knowledge of molecular interaction ...

Discriminating early- and late-stage cancers using multiple kernel learning on gene sets.

MOTIVATION: Identifying molecular mechanisms that drive cancers from early to late stages is highly important to develop new preventive and therapeuti...

Deep-2'-O-Me: Predicting 2'-O-methylation sites by Convolutional Neural Networks.

2'-O-methylation (2'-O-me) of ribose moiety is one of the significant and ubiquitous post-transcriptional RNA modifications which is vital for metabol...

Cancer Type Prediction and Classification Based on RNA-sequencing Data.

Pan-cancer analysis is a significant research topic in the past few years. Due to many advancing sequencing technologies, researchers possess more res...

DeFine: deep convolutional neural networks accurately quantify intensities of transcription factor-DNA binding and facilitate evaluation of functional non-coding variants.

The complex system of gene expression is regulated by the cell type-specific binding of transcription factors (TFs) to regulatory elements. Identifyin...

Feature specific quantile normalization enables cross-platform classification of molecular subtypes using gene expression data.

MOTIVATION: Molecular subtypes of cancers and autoimmune disease, defined by transcriptomic profiling, have provided insight into disease pathogenesis...

Machine learning for classifying tuberculosis drug-resistance from DNA sequencing data.

MOTIVATION: Correct and rapid determination of Mycobacterium tuberculosis (MTB) resistance against available tuberculosis (TB) drugs is essential for ...

LeNup: learning nucleosome positioning from DNA sequences with improved convolutional neural networks.

MOTIVATION: Nucleosome positioning plays significant roles in proper genome packing and its accessibility to execute transcription regulation. Despite...

Computational identification of binding energy hot spots in protein-RNA complexes using an ensemble approach.

MOTIVATION: Identifying RNA-binding residues, especially energetically favored hot spots, can provide valuable clues for understanding the mechanisms ...

Biomedical informatics and machine learning for clinical genomics.

While tens of thousands of pathogenic variants are used to inform the many clinical applications of genomics, there remains limited information on qua...

Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning.

Sequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology that offers faster and cheaper sequencing tha...

Deep learning of genomic variation and regulatory network data.

The human genome is now investigated through high-throughput functional assays, and through the generation of population genomic data. These advances ...