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Learning retention mechanisms and evolutionary parameters of duplicate genes from their expression data

bioRxiv (Cold Spring Harbor Laboratory)(2020)10.1101/2020.06.19.162107Source: DataRank Database

Learning retention mechanisms and evolutionary parameters of duplicate genes from their expression data is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2020). On theSindex it has a DataRank of 0.

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Enrichment:Funding (4 grants)OA: greenTopics (24)IDs (PubMed, OpenAlex)SDGs (1)

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Authors (2)

Raquel Assis ORCID,Michael DeGiorgio ORCID

Live enrichment

PubMed:21097893 OpenAlex:https://openalex.org/W3036041191

National Science Foundation

Grant: 1949268

SG: Inferring phylogenies under ancestral population structure

National Science Foundation

Grant: 2001059

Interrogating the role of natural selection in the functional evolution of duplicate genes

National Institutes of Health

Grant: 5R35GM128590-07

Identifying complex modes of adaptation from population-genomic data

National Science Foundation

Grant: 2001063

Collaborative Research: Understanding the Deep Ancestry of the Indigenous People of North America

greenLicense: CC BY

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2020/06/20/2020.06.19.162107.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2020.06.19.162107

repositoryhttps://doi.org/10.1101/2020.06.19.162107

https://doi.org/10.1093/molbev/msaa267

Fields of Study

Genomics and Phylogenetic StudiesGenetic diversity and population structureGene expression and cancer classification0301 basic medicine0303 health sciences03 medical and health sciences

Keywords

Classifier (UML)GeneLeverage (statistics)Computer scienceGene duplicationArtificial intelligenceComputational biologyBiologyMachine learningGeneticsEvolution, MolecularModels, GeneticMethodsAnimalsGene ExpressionDrosophilaNeural Networks, ComputerSoftware

Sustainable Development Goals

SDG 0: Peace, Justice and strong institutions