Dataset from a human-in-the-loop approach to identify functionally important protein residues from literature

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14740%2F24%3A00138864" target="_blank" >RIV/00216224:14740/24:00138864 - isvavai.cz</a>

Result on the web

<a href="https://www.nature.com/articles/s41597-024-03841-9" target="_blank" >https://www.nature.com/articles/s41597-024-03841-9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41597-024-03841-9" target="_blank" >10.1038/s41597-024-03841-9</a>

Result language

angličtina

Original language name

Dataset from a human-in-the-loop approach to identify functionally important protein residues from literature

Original language description

We present a novel system that leverages curators in the loop to develop a dataset and model for detecting structure features and functional annotations at residue-level from standard publication text. Our approach involves the integration of data from multiple resources, including PDBe, EuropePMC, PubMedCentral, and PubMed, combined with annotation guidelines from UniProt, and LitSuggest and HuggingFace models as tools in the annotation process. A team of seven annotators manually curated ten articles for named entities, which we utilized to train a starting PubmedBert model from HuggingFace. Using a human-in-the-loop annotation system, we iteratively developed the best model with commendable performance metrics of 0.90 for precision, 0.92 for recall, and 0.91 for F1-measure. Our proposed system showcases a successful synergy of machine learning techniques and human expertise in curating a dataset for residue-level functional annotations and protein structure features. The results demonstrate the potential for broader applications in protein research, bridging the gap between advanced machine learning models and the indispensable insights of domain experts.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10700 - Other natural sciences

Project

—

Continuities

—

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Scientific Data

ISSN

2052-4463

e-ISSN

2052-4463

Volume of the periodical

11

Issue of the periodical within the volume

1

Country of publishing house

DE - GERMANY

Number of pages

18

Pages from-to

1-18

UT code for WoS article

001325129100022

EID of the result in the Scopus database

2-s2.0-85205275590