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Simulation-based inference of evolutionary parameters from adaptation dynamics using neural networks

bioRxiv (Cold Spring Harbor Laboratory)(2021)10.1101/2021.09.30.462581Source: DataRank Database

Simulation-based inference of evolutionary parameters from adaptation dynamics using neural networks is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2021). On theSindex it has a DataRank of 0. It has been cited 2 times.

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Enrichment:Funding (5 grants)OA: greenTopics (20)IDs (PubMed, OpenAlex)

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Authors (6)

Julie N. Chuong ORCID,Fangfei Li ORCID,Gavin Sherlock ORCID,David Gresham ORCID,Yoav Ram ORCID

Live enrichment

PubMed:21097893 OpenAlex:https://openalex.org/W3204758309

National Institutes of Health

Grant: 5R35GM131824-07

Fitness Effects of Beneficial Mutations

National Institutes of Health

Grant: 3R01AI136992-03S1

Adaptation of Candida glabrata to host-associated niches

National Science Foundation

Grant: 1342536

Graduate Research Fellowship Program (GRFP)

National Institutes of Health

Grant: 5R01GM134066-04

Constraints and Consequences of Copy Number Variation

National Science Foundation

Grant: 1818234

Dynamics and determinants of copy number variation in evolving populations

greenLicense: cc-by-nc-nd

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2021/10/01/2021.09.30.462581.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2021.09.30.462581

repositoryhttps://doi.org/10.1101/2021.09.30.462581

repositoryhttp://api.osf.io/v2/nodes/e9d5x/

Fields of Study

Evolution and Genetic DynamicsGenetic Mapping and Diversity in Plants and AnimalsChromosomal and Genetic Variations0301 basic medicine0303 health sciences03 medical and health sciences

Keywords

Approximate Bayesian computationInferenceEvolutionary dynamicsChemostatComputer scienceMarkov chain Monte CarloBayesian inferencePopulationArtificial neural networkBayesian probabilityBiologyMachine learningArtificial intelligenceGenetics