Genetics

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 insertion...

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...

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

Many statistical methods for pathway analysis have been used to identify pathways associated with th...

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 deci...

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 activ...

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 t...

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

MOTIVATION: Genome sequencing experiments have revolutionized molecular biology by allowing research...

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 bindin...

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 i...

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...

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

We present a benchmark study of autonomous, chemical agents exhibiting associative learning of an en...

Nonlinear manipulation and analysis of large DNA datasets.

Information processing functions are essential for organisms to perceive and react to their complex ...

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...

Predicting RNA distance-based contact maps by integrated deep learning on physics-inferred secondary structure and evolutionary-derived mutational coupling.

MOTIVATION: Recently, AlphaFold2 achieved high experimental accuracy for the majority of proteins in...

Homologues not needed: Structure prediction from a protein language model.

Accurate protein structure predictors use clusters of homologues, which disregard sequence specific ...

Intratumoral Resolution of Driver Gene Mutation Heterogeneity in Renal Cancer Using Deep Learning.

UNLABELLED: Intratumoral heterogeneity arising from tumor evolution poses significant challenges bio...

Investigating Morphologic Correlates of Driver Gene Mutation Heterogeneity via Deep Learning.

Despite the crucial role of phenotypic and genetic intratumoral heterogeneity in understanding and p...

KLFDAPC: a supervised machine learning approach for spatial genetic structure analysis.

Geographic patterns of human genetic variation provide important insights into human evolution and d...

Adaptive sequencing using nanopores and deep learning of mitochondrial DNA.

Nanopore sequencing is an emerging technology that reads DNA by utilizing a unique method of detecti...

EPIMUTESTR: a nearest neighbor machine learning approach to predict cancer driver genes from the evolutionary action of coding variants.

Discovering rare cancer driver genes is difficult because their mutational frequency is too low for ...

Deep learning identifies A-to-I RNA edits using nanopore sequencing data.