Genetics

Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function.

The circadian clock is an important adaptation to life on Earth. Here, we use machine learning to predict complex, temporal, and circadian gene expression patterns in Most significantly, we classify circadian genes using DNA sequence features generated de novo from public, genomic resources, facilitating downstream application of our methods with no experimental work or prior knowledge needed. We...

Optical microlever assisted DNA stretching.

Optical microrobotics is an emerging field that has the potential to improve upon current optical tweezer studies through avenues such as limiting the exposure of biological molecules of interest to laser radiation and overcoming the current limitations of low forces and unwanted interactions between nearby optical traps. However, optical microrobotics has been historically limited to rigid, singl...

Radiogenomic and Deep Learning Network Approaches to Predict Mutation from Radiotherapy Plan CT.

BACKGROUND/AIM: We aimed to investigate the role of radiogenomic and deep learning approaches in predicting the KRAS mutation status of a tumor using ...

Predictive Models of Genetic Redundancy in Arabidopsis thaliana.

Genetic redundancy refers to a situation where an individual with a loss-of-function mutation in one gene (single mutant) does not show an apparent ph...

dSPRINT: predicting DNA, RNA, ion, peptide and small molecule interaction sites within protein domains.

Domains are instrumental in facilitating protein interactions with DNA, RNA, small molecules, ions and peptides. Identifying ligand-binding domains wi...

Synthetic observations from deep generative models and binary omics data with limited sample size.

Deep generative models can be trained to represent the joint distribution of data, such as measurements of single nucleotide polymorphisms (SNPs) from...

SICaRiO: short indel call filtering with boosting.

Despite impressive improvement in the next-generation sequencing technology, reliable detection of indels is still a difficult endeavour. Recognition ...

Predicting the interaction biomolecule types for lncRNA: an ensemble deep learning approach.

Long noncoding RNAs (lncRNAs) play significant roles in various physiological and pathological processes via their interactions with biomolecules like...

A survey on deep learning in DNA/RNA motif mining.

DNA/RNA motif mining is the foundation of gene function research. The DNA/RNA motif mining plays an extremely important role in identifying the DNA- o...

A comprehensive comparison of residue-level methylation levels with the regression-based gene-level methylation estimations by ReGear.

MOTIVATION: DNA methylation is a biological process impacting the gene functions without changing the underlying DNA sequence. The DNA methylation mac...

Machine learning application identifies novel gene signatures from transcriptomic data of spontaneous canine hemangiosarcoma.

Angiosarcomas are soft-tissue sarcomas that form malignant vascular tissues. Angiosarcomas are very rare, and due to their aggressive behavior and hig...

Prognostic outcome prediction by semi-supervised least squares classification.

Although great progress has been made in prognostic outcome prediction, small sample size remains a challenge in obtaining accurate and robust classif...

Prediction of driver variants in the cancer genome via machine learning methodologies.

Sequencing technologies have led to the identification of many variants in the human genome which could act as disease-drivers. As a consequence, a va...

Revisiting genome-wide association studies from statistical modelling to machine learning.

Over the last decade, genome-wide association studies (GWAS) have discovered thousands of genetic variants underlying complex human diseases and agric...

Identification of pan-cancer Ras pathway activation with deep learning.

The identification of hidden responders is often an essential challenge in precision oncology. A recent attempt based on machine learning has been pro...

Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework.

Origins of replication sites (ORIs), which refers to the initiative locations of genomic DNA replication, play essential roles in DNA replication proc...

DeepSSV: detecting somatic small variants in paired tumor and normal sequencing data with convolutional neural network.

It is of considerable interest to detect somatic mutations in paired tumor and normal sequencing data. A number of callers that are based on statistic...

Iterative feature representation algorithm to improve the predictive performance of N7-methylguanosine sites.

MOTIVATION: N7-methylguanosine (m7G) is an important epigenetic modification, playing an essential role in gene expression regulation. Therefore, accu...

Precise uncertain significance prediction using latent space matrix factorization models: genomics variant and heterogeneous clinical data-driven approaches.

Several studies to date have proposed different types of interpreters for measuring the degree of pathogenicity of variants. However, in predicting th...

Computational identification of eukaryotic promoters based on cascaded deep capsule neural networks.

A promoter is a region in the DNA sequence that defines where the transcription of a gene by RNA polymerase initiates, which is typically located prox...