AIMC Topic: Genome

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

Molecular biology and evolution Oct 4, 2023
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...
Computer Simulation Genomics Humans Neural Networks, Computer Genome Multifactorial Inheritance Polymorphism, Single Nucleotide Models, Genetic
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Application of deep learning in cancer epigenetics through DNA methylation analysis.

Briefings in bioinformatics Sep 22, 2023
DNA methylation is a fundamental epigenetic modification involved in various biological processes and diseases. Analysis of DNA methylation data at a genome-wide and high-throughput level can provide insights into diseases influenced by epigenetics, ...
Deep Learning Epigenesis, Genetic DNA Methylation Genome Humans Neoplasms
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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 Jun 1, 2023
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...
Machine Learning Algorithms Animals Swine Phenotype Reproducibility of Results Genomics Genome
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DeepFormer: a hybrid network based on convolutional neural network and flow-attention mechanism for identifying the function of DNA sequences.

Briefings in bioinformatics Mar 19, 2023
Identifying the function of DNA sequences accurately is an essential and challenging task in the genomic field. Until now, deep learning has been widely used in the functional analysis of DNA sequences, including DeepSEA, DanQ, DeepATT and TBiNet. Ho...
Genome Chromatin Neural Networks, Computer Genomics Base Sequence
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Targeting trypanosomes: how chemogenomics and artificial intelligence can guide drug discovery.

Biochemical Society transactions Feb 27, 2023
Trypanosomatids are protozoan parasites that cause human and animal neglected diseases. Despite global efforts, effective treatments are still much needed. Phenotypic screens have provided several chemical leads for drug discovery, but the mechanism ...
Trypanosoma Humans Genomics Drug Design Artificial Intelligence Genome Drug Discovery Animals
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The hitchhikers' guide to RNA sequencing and functional analysis.

Briefings in bioinformatics Jan 19, 2023
DNA and RNA sequencing technologies have revolutionized biology and biomedical sciences, sequencing full genomes and transcriptomes at very high speeds and reasonably low costs. RNA sequencing (RNA-Seq) enables transcript identification and quantific...
Genome RNA Transcriptome High-Throughput Nucleotide Sequencing Sequence Analysis, RNA Artificial Intelligence Gene Expression Profiling
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VPatho: a deep learning-based two-stage approach for accurate prediction of gain-of-function and loss-of-function variants.

Briefings in bioinformatics Jan 19, 2023
Determining the pathogenicity and functional impact (i.e. gain-of-function; GOF or loss-of-function; LOF) of a variant is vital for unraveling the genetic level mechanisms of human diseases. To provide a 'one-stop' framework for the accurate identifi...
Deep Learning Genome Gain of Function Mutation Humans
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EMBL's European Bioinformatics Institute (EMBL-EBI) in 2022.

Nucleic acids research Jan 6, 2023
The European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) is one of the world's leading sources of public biomolecular data. Based at the Wellcome Genome Campus in Hinxton, UK, EMBL-EBI is one of six sites of the Europe...
Artificial Intelligence Data Management Internet Genome Databases, Factual Computational Biology
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Defining the extent of gene function using ROC curvature.

Bioinformatics (Oxford, England) Dec 13, 2022
MOTIVATION: Interactions between proteins help us understand how genes are functionally related and how they contribute to phenotypes. Experiments provide imperfect 'ground truth' information about a small subset of potential interactions in a specif...
Gene Ontology Genome Phenotype Proteins Machine Learning
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Deciphering signatures of natural selection via deep learning.

Briefings in bioinformatics Sep 20, 2022
Identifying genomic regions influenced by natural selection provides fundamental insights into the genetic basis of local adaptation. However, it remains challenging to detect loci under complex spatially varying selection. We propose a deep learning...
Deep Learning Polymorphism, Single Nucleotide Humans Genome Genomics Selection, Genetic
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