Using published pathway figures in enrichment analysis and machine learning

Pathway Figure OCR (PFOCR) is a novel kind of pathway database approaching the breadth and depth of Gene Ontology while providing rich, mechanistic diagrams and direct literature support. Here, we highlight the utility of PFOCR in disease research in comparison with popular pathway databases through...

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Detalles Bibliográficos
Autores principales: Shin, Min-Gyoung, Pico, Alexander R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Database
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10676589/
https://www.ncbi.nlm.nih.gov/pubmed/38007419
http://dx.doi.org/10.1186/s12864-023-09816-1
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author Shin, Min-Gyoung
Pico, Alexander R.
author_facet Shin, Min-Gyoung
Pico, Alexander R.
author_sort Shin, Min-Gyoung
collection PubMed
description Pathway Figure OCR (PFOCR) is a novel kind of pathway database approaching the breadth and depth of Gene Ontology while providing rich, mechanistic diagrams and direct literature support. Here, we highlight the utility of PFOCR in disease research in comparison with popular pathway databases through an assessment of disease coverage and analytical applications. In addition to common pathway analysis use cases, we present two advanced case studies demonstrating unique advantages of PFOCR in terms of cancer subtype and grade prediction analyses. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-023-09816-1.
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spelling pubmed-106765892023-11-25 Using published pathway figures in enrichment analysis and machine learning Shin, Min-Gyoung Pico, Alexander R. BMC Genomics Database Pathway Figure OCR (PFOCR) is a novel kind of pathway database approaching the breadth and depth of Gene Ontology while providing rich, mechanistic diagrams and direct literature support. Here, we highlight the utility of PFOCR in disease research in comparison with popular pathway databases through an assessment of disease coverage and analytical applications. In addition to common pathway analysis use cases, we present two advanced case studies demonstrating unique advantages of PFOCR in terms of cancer subtype and grade prediction analyses. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-023-09816-1. BioMed Central 2023-11-25 /pmc/articles/PMC10676589/ /pubmed/38007419 http://dx.doi.org/10.1186/s12864-023-09816-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Database
Shin, Min-Gyoung
Pico, Alexander R.
Using published pathway figures in enrichment analysis and machine learning
title Using published pathway figures in enrichment analysis and machine learning
title_full Using published pathway figures in enrichment analysis and machine learning
title_fullStr Using published pathway figures in enrichment analysis and machine learning
title_full_unstemmed Using published pathway figures in enrichment analysis and machine learning
title_short Using published pathway figures in enrichment analysis and machine learning
title_sort using published pathway figures in enrichment analysis and machine learning
topic Database
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10676589/
https://www.ncbi.nlm.nih.gov/pubmed/38007419
http://dx.doi.org/10.1186/s12864-023-09816-1
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