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Protein function embeddings

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Started in 2022-10-17 Concluded in 2023-04-17

Description

This research project corresponds to a Master Thesis that encapsulates research focused on enhancing protein function annotations by leveraging protein function information through embeddings using two text-driven embedding techniques, Word2doc2Vec and Hybrid-Word2doc2Vec. This includes the metadata and a list, via hasPart of its outcomes even if thez are dependent on each other.

Keywords

Protein Function, Word2doc2Vec, Hybrid-Word2doc2Vec

Url

• URL: https://zbmed-semtec.github.io/theses/2023_Protein_function_embeddings

Previous project members

• Rohitha Ravinder Visit Person

Department

M.Sc. in Life Science Informatics

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• URL: https://www.b-it-center.de/b-it-programmes/msc-in-life-science-informatics/description

Semantic Technologies team at ZB MED

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• URL: https://zbmed-semtec.github.io/

Parent organization, consortium or research project

Deutsche Zentralbibliothek für Medizin (ZB MED) - Informationszentrum Lebenswissenschaften

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• Alternatename: ZB MED Information Centre for Life Sciences
• URL: https://zbmed.de/

Rheinische Friedrich-Wilhelms-Universität Bonn

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• Alternatename: University of Bonn
• URL: http://www3.uni-bonn.de/the-university

Bonn Aachen International Center for Information Technology

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• URL: http://www.b-it-center.de/

Outcomes

Comparative analysis of protein function text-based embeddings and its potential for prediction tasks : master thesis

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• Identifier: DOI:10.4126/FRL01-006472635
• Cite as: Ravinder R. Comparative analysis of protein functiontext-based embeddings and its potential forprediction tasks. Master Thesis. 2023. doi:10.4126/FRL01-006472635
• Datecreated: 2023-04-17

• URL: https://repository.publisso.de/resource/frl:6472635

• License: http://spdx.org/licenses/CC-BY-4.0

• Author: https://orcid.org/0009-0004-4484-6283

Protein-function-embeddings-thesis

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• Cite as: Ravinder, R., Castro, LJ., & Rebholz-Schuhmann, D. (2023). Protein-function-embeddings-thesis [Software source code]. https://github.com/zbmed-semtec/protein_function_embeddings_thesis
• Datepublished: 2023-06-21
• Description: This is the Software Source Code corresponding to the thesis along with its metadata.

• URL: https://github.com/zbmed-semtec/protein-function-embeddings-thesis#

• Programminglanguage: Python

• Release: https://doi.org/10.5281/zenodo.7781869
• License: http://spdx.org/licenses/GPL-3.0-only
• Authors: https://orcid.org/0009-0004-4484-6283. https://orcid.org/0000-0003-3986-0510. https://orcid.org/0000-0002-1018-0370.

Protein Function Embeddings: First Beta Release

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• Identifier: DOI:10.5281/zenodo.7781869
• Cite as: Ravinder, R., Castro, LJ., & Rebholz-Schuhmann, D. (2023). Protein Function Embeddings: First Beta Release (v1.0.1). Zenodo. https://doi.org/10.5281/zenodo.7781870
• Description: This is the Software release of the source code corresponding to the thesis along with its metadata.

• URL: https://zenodo.org/records/7781870

• Softwareversion: v1.0.1

• Datepublished: 2023-03-29
• License: http://spdx.org/licenses/GPL-3.0-only
• Authors: https://orcid.org/0009-0004-4484-6283. https://orcid.org/0000-0003-3986-0510. https://orcid.org/0000-0002-1018-0370.

Protein Function Embeddings: First Beta Release of Datasets

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• Conformsto: https://bioschemas.org/profiles/Dataset/1.1-DRAFT
• Identifier: DOI:10.5281/zenodo.7793383
• Cite as: Ravinder, R., Castro, LJ., & Rebholz-Schuhmann, D. (2023). Protein Function Embeddings: First Beta Release of Datasets (v1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7793384
• Description: This is the Dataset release corresponding to the thesis along with its metadata.

• URL: https://zenodo.org/records/7793384

• Version: v1.0.0

• Datepublished: 2023-04-02
• License: http://spdx.org/licenses/CC-BY-4.0
• Authors: https://orcid.org/0009-0004-4484-6283. https://orcid.org/0000-0003-3986-0510. https://orcid.org/0000-0002-1018-0370.

Comparative analysis of protein function text-based embeddings and their applicability to prediction tasks

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• Identifier: CEUR:Vol-3415/paper-21
• Cite as: Ravinder, R., Castro, LJ., Hofmann-Apitius, M., & Rebholz-Schuhmann, D. (2023). Comparative analysis of protein function text-based embeddings and their applicability to prediction tasks. In Proceedings of the Semantic Web Applications and Tools for Healthcare and Life Sciences (SWAT4HCLS 2023) (pp. 124-125)
• Datepublished: 2023-06-22

• URL: https://ceur-ws.org/Vol-3415/paper-21.pdf#

• License: http://spdx.org/licenses/CC-BY-4.0

• Authors: https://orcid.org/0009-0004-4484-6283. https://orcid.org/0000-0003-3986-0510. https://orcid.org/0000-0001-9012-6720. https://orcid.org/0000-0002-1018-0370.

External contributors

• Olga Giraldo Visit Person

• Leyla Jael Castro Visit Person

• Martin Hoffmann-Apitius Visit Person

• Dietrich Rebholz-Schuhmann Visit Person