AI Medical Compendium Topic:
Genomics

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An open-source k-mer based machine learning tool for fast and accurate subtyping of HIV-1 genomes.

PloS one
For many disease-causing virus species, global diversity is clustered into a taxonomy of subtypes with clinical significance. In particular, the classification of infections among the subtypes of human immunodeficiency virus type 1 (HIV-1) is a routi...

Prognostic models in primary biliary cholangitis.

Journal of autoimmunity
Risk prediction modelling is important to better understand the determinants of the course and outcome of PBC and to inform the risk across the disease continuum in PBC enabling risk-stratified follow-up care and personalised therapy. Current prognos...

WEADE: A workflow for enrichment analysis and data exploration.

PloS one
Data analysis based on enrichment of Gene Ontology terms has become an important step in exploring large gene or protein expression datasets and several stand-alone or web tools exist for that purpose. However, a comprehensive and consistent analysis...

A universal SNP and small-indel variant caller using deep neural networks.

Nature biotechnology
Despite rapid advances in sequencing technologies, accurately calling genetic variants present in an individual genome from billions of short, errorful sequence reads remains challenging. Here we show that a deep convolutional neural network can call...

HLBS-PopOmics: an online knowledge base to accelerate dissemination and implementation of research advances in population genomics to reduce the burden of heart, lung, blood, and sleep disorders.

Genetics in medicine : official journal of the American College of Medical Genetics
Recent dramatic advances in multiomics research coupled with exponentially increasing volume, complexity, and interdisciplinary nature of publications are making it challenging for scientists to stay up-to-date on the literature. Strategies to addres...

Can Deep Learning Improve Genomic Prediction of Complex Human Traits?

Genetics
The genetic analysis of complex traits does not escape the current excitement around artificial intelligence, including a renewed interest in "deep learning" (DL) techniques such as Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNN...

Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine.

Omics : a journal of integrative biology
Machine learning (ML) is being ubiquitously incorporated into everyday products such as Internet search, email spam filters, product recommendations, image classification, and speech recognition. New approaches for highly integrated manufacturing and...

Hierarchical Convolutional Neural Networks for Segmentation of Breast Tumors in MRI With Application to Radiogenomics.

IEEE transactions on medical imaging
Breast tumor segmentation based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging problem and an active area of research. Particular challenges, similarly as in other segmentation problems, include the class-imbalance...

A heuristic method for fast and accurate phasing and imputation of single-nucleotide polymorphism data in bi-parental plant populations.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Key message New fast and accurate method for phasing and imputation of SNP chip genotypes within diploid bi-parental plant populations. This paper presents a new heuristic method for phasing and imputation of genomic data in diploid plant species. Ou...

Monitoring changes in the Gene Ontology and their impact on genomic data analysis.

GigaScience
BACKGROUND: The Gene Ontology (GO) is one of the most widely used resources in molecular and cellular biology, largely through the use of "enrichment analysis." To facilitate informed use of GO, we present GOtrack (https://gotrack.msl.ubc.ca), which ...