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Adapting protein language models for rapid DTI prediction

bioRxiv (Cold Spring Harbor Laboratory)(2022)10.1101/2022.11.03.515084Source: DataRank Database

Adapting protein language models for rapid DTI prediction is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2022). On theSindex it has a DataRank of 0. It has been cited 12 times.

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Open Access12 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (4 grants)OA: greenTopics (17)IDs (PubMed, OpenAlex)

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Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (4)

Rohit Singh ORCID,Lenore Cowen ORCID,Bonnie Berger ORCID,Samuel Sledzieski ORCID

Live enrichment

PubMed:20819859 OpenAlex:https://openalex.org/W4308268814

National Science Foundation

Grant: 1745302

Graduate Research Fellowship Program (GRFP)

National Institutes of Health

Grant: 1R35GM141861-01

Manifold representations and active learning for 21 st century biology

National Science Foundation

Grant: 1939263

HDR: DIRSE-IL: Collaborative Research: Harnessing data advances in systems biology to design a biological 3D printer: the synthetic coral

National Science Foundation

Grant: 1934553

HDR TRIPODS: Building the Foundation for a Data-Intensive Studies Center-

greenLicense: cc-by-nc-nd

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2022/11/04/2022.11.03.515084.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2022.11.03.515084

repositoryhttps://doi.org/10.1101/2022.11.03.515084

Fields of Study

Computational Drug Discovery MethodsProtein Structure and Dynamicsvaccines and immunoinformatics approaches0301 basic medicine03 medical and health sciences0206 medical engineering02 engineering and technology

Keywords

Generalizability theoryComputer scienceTask (project management)EmbeddingArtificial intelligenceSet (abstract data type)Machine learningTransfer of learningTraining setEngineering