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Transfer learning to leverage larger datasets for improved prediction of protein stability changes

bioRxiv (Cold Spring Harbor Laboratory)(2023)10.1101/2023.07.27.550881Source: DataRank Database

Transfer learning to leverage larger datasets for improved prediction of protein stability changes is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2023). On theSindex it has a DataRank of 0.798. It has been cited 19 times, with 19 citing works in its 1-hop citation network.

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0.798DataRank · unranked

Scored on demand from live citation data

Open Access19 citations · 19 citing works in its network

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (3 grants)OA: greenTopics (17)IDs (PubMed, OpenAlex)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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DataRank Breakdown

Base Score 56%Citation Network 44%

Base Score Contribution

0.449

From this paper's citation signal

Citation Network Contribution

0.349

From 12 citing papers with measurable signal

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Top 5 citers driving the network score

Ranked by each citer's contribution to N(p) — log1p(C_q) divided by its reference count — out of 19 citers.

Proteins with alternative folds reveal blind spots in AlphaFold-based protein structure prediction
Current Opinion in Structural Biology202570 citations54 referencesContributes 0.079
DDGemb: predicting protein stability change upon single- and multi-point variations with embeddings and deep learning
Bioinformatics202417 citations39 referencesContributes 0.074
Assessing computational tools for predicting protein stability changes upon missense mutations using a new dataset
Protein Science202319 citations59 referencesContributes 0.051
De novo protein design—From new structures to programmable functions
Cell2024331 citations130 referencesContributes 0.045
Mapping kinase domain resistance mechanisms for the MET receptor tyrosine kinase via deep mutational scanning
eLife202414 citations83 referencesContributes 0.033

Authors (4)

Michael Brocidiacono ORCID,Nicholas Z. Randolph ORCID,Brian Kuhlman ORCID,Henry Dieckhaus ORCID

Live enrichment

PubMed:37547004 OpenAlex:https://openalex.org/W4385393084

National Institutes of Health

Grant: 5R35GM131923-02

Computational Design of Protein Structures and Complexes

National Science Foundation

Grant: 2040435

Graduate Research Fellowship Program (GRFP)

NIGMS NIH HHS

Grant: R35 GM131923

greenLicense: cc-by-nd

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2023/07/30/2023.07.27.550881.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2023.07.27.550881

repositoryhttps://doi.org/10.1101/2023.07.27.550881

repositoryhttps://pubmed.ncbi.nlm.nih.gov/37547004

Fields of Study

Protein Structure and DynamicsMicrobial Metabolic Engineering and BioproductionMachine Learning in Bioinformatics0206 medical engineering02 engineering and technology

Keywords

Leverage (statistics)Stability (learning theory)Transfer of learningMachine learningComputer scienceArtificial intelligenceProtein StabilityProteinsNeural Networks, ComputerAmino Acid SequenceBiological SciencesArticle

Sustainable Development Goals

SDG 0: Industry, innovation and infrastructure