Genetics

ABEILLE: a novel method for ABerrant Expression Identification empLoying machine LEarning from RNA-sequencing data.

MOTIVATION: Current advances in omics technologies are paving the diagnosis of rare diseases proposing a complementary assay to identify the responsible gene. The use of transcriptomic data to identify aberrant gene expression (AGE) has demonstrated to yield potential pathogenic events. However, popular approaches for AGE identification are limited by the use of statistical tests that imply the ch...

TVAR: assessing tissue-specific functional effects of non-coding variants with deep learning.

MOTIVATION: Analysis of whole-genome sequencing (WGS) for genetics is still a challenge due to the lack of accurate functional annotation of non-coding variants, especially the rare ones. As eQTLs have been extensively implicated in the genetics of human diseases, we hypothesize that rare non-coding variants discovered in WGS play a regulatory role in predisposing disease risk.

Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation.

As the most pervasive epigenetic mark present on mRNA and lncRNA, N6-methyladenosine (m6A) RNA methylation regulates all stages of RNA life in various...

Construction and analysis of heart failure diagnosis model based on random forest and artificial neural network.

Heart failure is a global health problem and the number of sufferers is increasing as the population grows and ages. Existing diagnostic techniques fo...

A spatial attention guided deep learning system for prediction of pathological complete response using breast cancer histopathology images.

MOTIVATION: Predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC) patients accurat...

CLNN-loop: a deep learning model to predict CTCF-mediated chromatin loops in the different cell lines and CTCF-binding sites (CBS) pair types.

MOTIVATION: Three-dimensional (3D) genome organization is of vital importance in gene regulation and disease mechanisms. Previous studies have shown t...

DeNovoCNN: a deep learning approach to de novo variant calling in next generation sequencing data.

De novo mutations (DNMs) are an important cause of genetic disorders. The accurate identification of DNMs from sequencing data is therefore fundamenta...

MAMnet: detecting and genotyping deletions and insertions based on long reads and a deep learning approach.

Structural variations (SVs) play important roles in human genetic diversity; deletions and insertions are two common types of SVs that have been prove...

Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine.

Precision medicine uses genetic, environmental and lifestyle factors to more accurately diagnose and treat disease in specific groups of patients, and...

DeepHisCoM: deep learning pathway analysis using hierarchical structural component models.

Many statistical methods for pathway analysis have been used to identify pathways associated with the disease along with biological factors such as ge...

A deep learning-based method for the prediction of DNA interacting residues in a protein.

DNA-protein interaction is one of the most crucial interactions in the biological system, which decides the fate of many processes such as transcripti...

R5hmCFDV: computational identification of RNA 5-hydroxymethylcytosine based on deep feature fusion and deep voting.

RNA 5-hydroxymethylcytosine (5hmC) is a kind of RNA modification, which is related to the life activities of many organisms. Studying its distribution...

The genetic algorithm-aided three-stage ensemble learning method identified a robust survival risk score in patients with glioma.

Ensemble learning is a kind of machine learning method which can integrate multiple basic learners together and achieve higher accuracy. Recently, sin...

LanceOtron: a deep learning peak caller for genome sequencing experiments.

MOTIVATION: Genome sequencing experiments have revolutionized molecular biology by allowing researchers to identify important DNA-encoded elements gen...

DNAffinity: a machine-learning approach to predict DNA binding affinities of transcription factors.

We present a physics-based machine learning approach to predict in vitro transcription factor binding affinities from structural and mechanical DNA pr...

DeepGenGrep: a general deep learning-based predictor for multiple genomic signals and regions.

MOTIVATION: Accurate annotation of different genomic signals and regions (GSRs) from DNA sequences is fundamentally important for understanding gene s...

Identifying modifications on DNA-bound histones with joint deep learning of multiple binding sites in DNA sequence.

MOTIVATION: Histone modifications are epigenetic markers that impact gene expression by altering the chromatin structure or recruiting histone modifie...

Provenance of life: Chemical autonomous agents surviving through associative learning.

We present a benchmark study of autonomous, chemical agents exhibiting associative learning of an environmental feature. Associative learning systems ...

Nonlinear manipulation and analysis of large DNA datasets.

Information processing functions are essential for organisms to perceive and react to their complex environment, and for humans to analyze and rationa...

Deep learning models for RNA secondary structure prediction (probably) do not generalize across families.

MOTIVATION: The secondary structure of RNA is of importance to its function. Over the last few years, several papers attempted to use machine learning...