Genetics

Recent advances in network-based methods for disease gene prediction.

Disease-gene association through genome-wide association study (GWAS) is an arduous task for researchers. Investigating single nucleotide polymorphisms that correlate with specific diseases needs statistical analysis of associations. Considering the huge number of possible mutations, in addition to its high cost, another important drawback of GWAS analysis is the large number of false positives. T...

Coupled co-clustering-based unsupervised transfer learning for the integrative analysis of single-cell genomic data.

Unsupervised methods, such as clustering methods, are essential to the analysis of single-cell genomic data. The most current clustering methods are designed for one data type only, such as single-cell RNA sequencing (scRNA-seq), single-cell ATAC sequencing (scATAC-seq) or sc-methylation data alone, and a few are developed for the integrative analysis of multiple data types. The integrative analys...

Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets.

Although a wide variety of machine learning (ML) algorithms have been utilized to learn quantitative structure-activity relationships (QSARs), there i...

CyanoPATH: a knowledgebase of genome-scale functional repertoire for toxic cyanobacterial blooms.

CyanoPATH is a database that curates and analyzes the common genomic functional repertoire for cyanobacteria harmful algal blooms (CyanoHABs) in eutro...

A comprehensive survey on computational methods of non-coding RNA and disease association prediction.

The studies on relationships between non-coding RNAs and diseases are widely carried out in recent years. A large number of experimental methods and t...

Deep forest ensemble learning for classification of alignments of non-coding RNA sequences based on multi-view structure representations.

Non-coding RNAs (ncRNAs) play crucial roles in multiple biological processes. However, only a few ncRNAs' functions have been well studied. Given the ...

Machine learning-based integrative analysis of methylome and transcriptome identifies novel prognostic DNA methylation signature in uveal melanoma.

Uveal melanoma (UVM) is the most common primary intraocular human malignancy with a high mortality rate. Aberrant DNA methylation has rapidly emerged ...

A deep learning approach for filtering structural variants in short read sequencing data.

Short read whole genome sequencing has become widely used to detect structural variants in human genetic studies and clinical practices. However, accu...

Heuristic hyperparameter optimization of deep learning models for genomic prediction.

There is a growing interest among quantitative geneticists and animal breeders in the use of deep learning (DL) for genomic prediction. However, the p...

Thousands of induced germline mutations affecting immune cells identified by automated meiotic mapping coupled with machine learning.

Forward genetic studies use meiotic mapping to adduce evidence that a particular mutation, normally induced by a germline mutagen, is causative of a p...

CROTON: an automated and variant-aware deep learning framework for predicting CRISPR/Cas9 editing outcomes.

MOTIVATION: CRISPR/Cas9 is a revolutionary gene-editing technology that has been widely utilized in biology, biotechnology and medicine. CRISPR/Cas9 e...

Cardiovascular RNA markers and artificial intelligence may improve COVID-19 outcome: a position paper from the EU-CardioRNA COST Action CA17129.

The coronavirus disease 2019 (COVID-19) pandemic has been as unprecedented as unexpected, affecting more than 105 million people worldwide as of 8 Feb...

Deep representation learning improves prediction of LacI-mediated transcriptional repression.

Recent progress in DNA synthesis and sequencing technology has enabled systematic studies of protein function at a massive scale. We explore a deep mu...

Malware detection based on semi-supervised learning with malware visualization.

The traditional signature-based detection method requires detailed manual analysis to extract the signatures of malicious samples, and requires a larg...

Clinical Performance of a Gene-Based Machine Learning Classifier in Assessing Risk of Developing OUD in Subjects Taking Oral Opioids: A Prospective Observational Study.

OBJECTIVE: To reduce the incidence of Opioid Use Disorder (OUD), multiple guidelines recommend assessing the risk of OUD prior to prescribing oral opi...

Research on automatic preprocessing equipment of salivary analyte for PCR inspection of coronavirus.

Polymerase chain reaction (PCR) inspection of salivary analyte is performed by pretreatment, RNA extraction setup, RNA extraction, PCR setup, and the ...

High-throughput developability assays enable library-scale identification of producible protein scaffold variants.

Proteins require high developability-quantified by expression, solubility, and stability-for robust utility as therapeutics, diagnostics, and in other...

Probabilistic Contextual and Structural Dependencies Learning in Grammar-Based Genetic Programming.

Genetic Programming is a method to automatically create computer programs based on the principles of evolution. The problem of deceptiveness caused by...

Semi-supervised random forest regression model based on co-training and grouping with information entropy for evaluation of depression symptoms severity.

Semi-supervised learning has always been a hot topic in machine learning. It uses a large number of unlabeled data to improve the performance of the m...

GraphBind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residues.

Knowledge of the interactions between proteins and nucleic acids is the basis of understanding various biological activities and designing new drugs. ...