Genetics

BiGG Models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree.

The BiGG Models knowledge base (http://bigg.ucsd.edu) is a centralized repository for high-quality genome-scale metabolic models. For the past 12 years, the website has allowed users to browse and search metabolic models. Within this update, we detail new content and features in the repository, continuing the original effort to connect each model to genome annotations and external databases as wel...

Deep Learning for Acute Myeloid Leukemia Diagnosis.

By changing the lifestyle and increasing the cancer incidence, accurate diagnosis becomes a significant medical action. Today, DNA microarray is widely used in cancer diagnosis and screening since it is able to measure gene expression levels. Analyzing them by using common statistical methods is not suitable because of the high gene expression data dimensions. So, this study aims to use new techni...

Future Vision 2020 and Beyond-5 Critical Trends in Eye Research.

Ophthalmology has been at the forefront of many innovations in basic science and clinical research. The randomized prospective multicenter clinical tr...

circDeep: deep learning approach for circular RNA classification from other long non-coding RNA.

MOTIVATION: Over the past two decades, a circular form of RNA (circular RNA), produced through alternative splicing, has become the focus of scientifi...

DeePaC: predicting pathogenic potential of novel DNA with reverse-complement neural networks.

MOTIVATION: We expect novel pathogens to arise due to their fast-paced evolution, and new species to be discovered thanks to advances in DNA sequencin...

Deep Learning in the Study of Protein-Related Interactions.

Protein-related interaction prediction is critical to understanding life processes, biological functions, and mechanisms of drug action. Experimental ...

Recent Advances on the Semi-Supervised Learning for Long Non-Coding RNA-Protein Interactions Prediction: A Review.

In recent years, more and more evidence indicates that long non-coding RNA (lncRNA) plays a significant role in the development of complex biological ...

Using Machine Learning to Identify True Somatic Variants from Next-Generation Sequencing.

BACKGROUND: Molecular profiling has become essential for tumor risk stratification and treatment selection. However, cancer genome complexity and tech...

Convolutional Neural Network Visualization for Identification of Risk Genes in Bipolar Disorder.

BACKGROUND: Bipolar disorder (BD) is a type of chronic emotional disorder with a complex genetic structure. However, its genetic molecular mechanism i...

PAGE-Net: Interpretable and Integrative Deep Learning for Survival Analysis Using Histopathological Images and Genomic Data.

The integration of multi-modal data, such as histopathological images and genomic data, is essential for understanding cancer heterogeneity and comple...

Statistical and Machine Learning Methods for eQTL Analysis.

An immense amount of observable diversity exists for all traits and across global populations. In the post-genomic era, equipped with efficient sequen...

Ensemble-Based Somatic Mutation Calling in Cancer Genomes.

Identification of somatic mutations in tumor tissue is challenged by both technical artifacts, diverse somatic mutational processes, and genetic heter...

SomaticSeq: An Ensemble and Machine Learning Method to Detect Somatic Mutations.

A standard strategy to discover somatic mutations in a cancer genome is to use next-generation sequencing (NGS) technologies to sequence the tumor tis...

Antioxidant Proteins' Identification Based on Support Vector Machine.

BACKGROUND: Evidence have increasingly indicated that for human disease, cell metabolism are deeply associated with proteins. Structural mutations and...

Identification of 2'-O-methylation Site by Investigating Multi-feature Extracting Techniques.

BACKGROUND: RNA methylation is a reversible post-transcriptional modification involving numerous biological processes. Ribose 2'-O-methylation is part...

Gene Ontology Curation of Neuroinflammation Biology Improves the Interpretation of Alzheimer's Disease Gene Expression Data.

BACKGROUND: Gene Ontology (GO) is a major bioinformatic resource used for analysis of large biomedical datasets, for example from genome-wide associat...

[Artificial intelligence-guided precision medicine in hematological disorders].

Precision medicine in oncology uses genomic data to provide the right intervention in the right patients at the right time. For this purpose, next-gen...

CHDGKB: a knowledgebase for systematic understanding of genetic variations associated with non-syndromic congenital heart disease.

Congenital heart disease (CHD) is one of the most common birth defects, with complex genetic and environmental etiologies. The reports of genetic vari...

The Genome3D Consortium for Structural Annotations of Selected Model Organisms.

Genome3D consortium is a collaborative project involving protein structure prediction and annotation resources developed by six world-leading structur...

VacPred: Sequence-based prediction of plant vacuole proteins using machine-learning techniques.

Subcellular localization prediction of the proteome is one of major goals of large-scale genome or proteome sequencing projects to define the gene fun...