Genetics

PANDA: Prioritization of autism-genes using network-based deep-learning approach.

Understanding the genetic background of complex diseases and disorders plays an essential role in the promising precision medicine. The evaluation of candidate genes, however, requires time-consuming and expensive experiments given a large number of possibilities. Thus, computational methods have seen increasing applications in predicting gene-disease associations. We proposed a bioinformatics fra...

Deep learning improves the ability of sgRNA off-target propensity prediction.

BACKGROUND: CRISPR/Cas9 system, as the third-generation genome editing technology, has been widely applied in target gene repair and gene expression regulation. Selection of appropriate sgRNA can improve the on-target knockout efficacy of CRISPR/Cas9 system with high sensitivity and specificity. However, when CRISPR/Cas9 system is operating, unexpected cleavage may occur at some sites, known as of...

Combined Use of Three Machine Learning Modeling Methods to Develop a Ten-Gene Signature for the Diagnosis of Ventilator-Associated Pneumonia.

BACKGROUND This study aimed to use three modeling methods, logistic regression analysis, random forest analysis, and fully-connected neural network an...

Belongs to (Maleae, Rosaceae): Evidence From Chloroplast Genome and Nuclear Ribosomal DNA Data.

The (ER) clade consists of about 46 species distributed in East and Southeast Asia. Although and have been supported to form a clade, the monophyly...

A Translational Pipeline for Overall Survival Prediction of Breast Cancer Patients by Decision-Level Integration of Multi-Omics Data.

Breast cancer is the most prevalent and among the most deadly cancers in females. Patients with breast cancer have highly variable survival rates, ind...

Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks.

The goal of this study was to compare the predictive performance of artificial neural networks (ANNs) with Bayesian ridge regression, Bayesian Lasso, ...

A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns.

In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a pati...

RefDNN: a reference drug based neural network for more accurate prediction of anticancer drug resistance.

Cancer is one of the most difficult diseases to treat owing to the drug resistance of tumour cells. Recent studies have revealed that drug responses a...

ERAP1 polymorphisms interactions and their association with Behçet's disease susceptibly: Application of Model-Based Multifactor Dimension Reduction Algorithm (MB-MDR).

BACKGROUND: Behçet's disease (BD) is a chronic multi-systemic vasculitis with a considerable prevalence in Asian countries. There are many genes assoc...

Virtual genetic diagnosis for familial hypercholesterolemia powered by machine learning.

AIMS: Familial hypercholesterolemia (FH) is the most common genetic disorder of lipid metabolism. The gold standard for FH diagnosis is genetic testin...

Cenicriviroc, a dual CCR2 and CCR5 antagonist leads to a reduction in plasma fibrotic biomarkers in persons living with HIV on antiretroviral therapy.

Chronic HIV is associated with increased inflammation and tissue fibrosis despite suppressive antiretroviral therapy (ART). Monocytes and macrophages...

DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning.

Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases. GWAS never directly link variants to regulatory m...

Characterization of low level viraemia in HIV-infected patients receiving boosted protease inhibitor-based antiretroviral regimens.

To understand the pathogenesis of low level viraemia (LLV) in HIV-infected patients on boosted protease inhibitors (PI/b), we enrolled 34 subjects wit...

Deep neural networks for interpreting RNA-binding protein target preferences.

Deep learning has become a powerful paradigm to analyze the binding sites of regulatory factors including RNA-binding proteins (RBPs), owing to its st...

A deep learning image-based intrinsic molecular subtype classifier of breast tumors reveals tumor heterogeneity that may affect survival.

BACKGROUND: Breast cancer intrinsic molecular subtype (IMS) as classified by the expression-based PAM50 assay is considered a strong prognostic featur...

Heterogeneous Multi-Layered Network Model for Omics Data Integration and Analysis.

Advances in next-generation sequencing and high-throughput techniques have enabled the generation of vast amounts of diverse omics data. These big dat...

Deep learning models predict regulatory variants in pancreatic islets and refine type 2 diabetes association signals.

Genome-wide association analyses have uncovered multiple genomic regions associated with T2D, but identification of the causal variants at these remai...

Deep learning for plant genomics and crop improvement.

Our era has witnessed tremendous advances in plant genomics, characterized by an explosion of high-throughput techniques to identify multi-dimensional...

Design of (quinolin-4-ylthio)carboxylic acids as new Escherichia coli DNA gyrase B inhibitors: machine learning studies, molecular docking, synthesis and biological testing.

Spread of multidrug-resistant Escherichia coli clinical isolates is a main problem in the treatment of infectious diseases. Therefore, the modern scie...

Genome-scale transcriptional dynamics and environmental biosensing.

Genome-scale technologies have enabled mapping of the complex molecular networks that govern cellular behavior. An emerging theme in the analyses of t...