AI Medical Compendium Topic:
Genomics

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Inference of Coalescence Times and Variant Ages Using Convolutional Neural Networks.

Molecular biology and evolution
Accurate inference of the time to the most recent common ancestor (TMRCA) between pairs of individuals and of the age of genomic variants is key in several population genetic analyses. We developed a likelihood-free approach, called CoalNN, which use...

SoyDNGP: a web-accessible deep learning framework for genomic prediction in soybean breeding.

Briefings in bioinformatics
Soybean is a globally significant crop, playing a vital role in human nutrition and agriculture. Its complex genetic structure and wide trait variation, however, pose challenges for breeders and researchers aiming to optimize its yield and quality. A...

GOWDL: gene ontology-driven wide and deep learning model for cell typing of scRNA-seq data.

Briefings in bioinformatics
Single-cell RNA-sequencing (scRNA-seq) allows for obtaining genomic and transcriptomic profiles of individual cells. That data make it possible to characterize tissues at the cell level. In this context, one of the main analyses exploiting scRNA-seq ...

Explainable AI for Bioinformatics: Methods, Tools and Applications.

Briefings in bioinformatics
Artificial intelligence (AI) systems utilizing deep neural networks and machine learning (ML) algorithms are widely used for solving critical problems in bioinformatics, biomedical informatics and precision medicine. However, complex ML models that a...

Phen2Disease: a phenotype-driven model for disease and gene prioritization by bidirectional maximum matching semantic similarities.

Briefings in bioinformatics
Human Phenotype Ontology (HPO)-based approaches have gained popularity in recent times as a tool for genomic diagnostics of rare diseases. However, these approaches do not make full use of the available information on disease and patient phenotypes. ...

Genome-wide scans for selective sweeps using convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Recent methods for selective sweep detection cast the problem as a classification task and use summary statistics as features to capture region characteristics that are indicative of a selective sweep, thereby being sensitive to confoundi...

Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics.

Bioinformatics (Oxford, England)
MOTIVATION: Developing new crop varieties with superior performance is highly important to ensure robust and sustainable global food security. The speed of variety development is limited by long field cycles and advanced generation selections in plan...

Using machine learning to realize genetic site screening and genomic prediction of productive traits in pigs.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology
Genomic prediction, which is based on solving linear mixed-model (LMM) equations, is the most popular method for predicting breeding values or phenotypic performance for economic traits in livestock. With the need to further improve the performance o...

Analysis of super-enhancer using machine learning and its application to medical biology.

Briefings in bioinformatics
The analysis of super-enhancers (SEs) has recently attracted attention in elucidating the molecular mechanisms of cancer and other diseases. SEs are genomic structures that strongly induce gene expression and have been reported to contribute to the o...