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Contextualizing Genes by Using Text-Mined Co-Occurrence Features for Cancer Gene Panel Discovery
Developing a biomedical-explainable and validatable text mining pipeline can help in cancer gene panel discovery. We create a pipeline that can contextualize genes by using text-mined co-occurrence features. We apply Biomedical Natural Language Processing (BioNLP) techniques for literature mining in...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8573063/ https://www.ncbi.nlm.nih.gov/pubmed/34759963 http://dx.doi.org/10.3389/fgene.2021.771435 |
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author | Chen, Hui-O Lin, Peng-Chan Liu, Chen-Ruei Wang, Chi-Shiang Chiang, Jung-Hsien |
author_facet | Chen, Hui-O Lin, Peng-Chan Liu, Chen-Ruei Wang, Chi-Shiang Chiang, Jung-Hsien |
author_sort | Chen, Hui-O |
collection | PubMed |
description | Developing a biomedical-explainable and validatable text mining pipeline can help in cancer gene panel discovery. We create a pipeline that can contextualize genes by using text-mined co-occurrence features. We apply Biomedical Natural Language Processing (BioNLP) techniques for literature mining in the cancer gene panel. A literature-derived 4,679 × 4,630 gene term-feature matrix was built. The EGFR L858R and T790M, and BRAF V600E genetic variants are important mutation term features in text mining and are frequently mutated in cancer. We validate the cancer gene panel by the mutational landscape of different cancer types. The cosine similarity of gene frequency between text mining and a statistical result from clinical sequencing data is 80.8%. In different machine learning models, the best accuracy for the prediction of two different gene panels, including MSK-IMPACT (Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets), and Oncomine cancer gene panel, is 0.959, and 0.989, respectively. The receiver operating characteristic (ROC) curve analysis confirmed that the neural net model has a better prediction performance (Area under the ROC curve (AUC) = 0.992). The use of text-mined co-occurrence features can contextualize each gene. We believe the approach is to evaluate several existing gene panels, and show that we can use part of the gene panel set to predict the remaining genes for cancer discovery. |
format | Online Article Text |
id | pubmed-8573063 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85730632021-11-09 Contextualizing Genes by Using Text-Mined Co-Occurrence Features for Cancer Gene Panel Discovery Chen, Hui-O Lin, Peng-Chan Liu, Chen-Ruei Wang, Chi-Shiang Chiang, Jung-Hsien Front Genet Genetics Developing a biomedical-explainable and validatable text mining pipeline can help in cancer gene panel discovery. We create a pipeline that can contextualize genes by using text-mined co-occurrence features. We apply Biomedical Natural Language Processing (BioNLP) techniques for literature mining in the cancer gene panel. A literature-derived 4,679 × 4,630 gene term-feature matrix was built. The EGFR L858R and T790M, and BRAF V600E genetic variants are important mutation term features in text mining and are frequently mutated in cancer. We validate the cancer gene panel by the mutational landscape of different cancer types. The cosine similarity of gene frequency between text mining and a statistical result from clinical sequencing data is 80.8%. In different machine learning models, the best accuracy for the prediction of two different gene panels, including MSK-IMPACT (Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets), and Oncomine cancer gene panel, is 0.959, and 0.989, respectively. The receiver operating characteristic (ROC) curve analysis confirmed that the neural net model has a better prediction performance (Area under the ROC curve (AUC) = 0.992). The use of text-mined co-occurrence features can contextualize each gene. We believe the approach is to evaluate several existing gene panels, and show that we can use part of the gene panel set to predict the remaining genes for cancer discovery. Frontiers Media S.A. 2021-10-25 /pmc/articles/PMC8573063/ /pubmed/34759963 http://dx.doi.org/10.3389/fgene.2021.771435 Text en Copyright © 2021 Chen, Lin, Liu, Wang and Chiang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Chen, Hui-O Lin, Peng-Chan Liu, Chen-Ruei Wang, Chi-Shiang Chiang, Jung-Hsien Contextualizing Genes by Using Text-Mined Co-Occurrence Features for Cancer Gene Panel Discovery |
title | Contextualizing Genes by Using Text-Mined Co-Occurrence Features for Cancer Gene Panel Discovery |
title_full | Contextualizing Genes by Using Text-Mined Co-Occurrence Features for Cancer Gene Panel Discovery |
title_fullStr | Contextualizing Genes by Using Text-Mined Co-Occurrence Features for Cancer Gene Panel Discovery |
title_full_unstemmed | Contextualizing Genes by Using Text-Mined Co-Occurrence Features for Cancer Gene Panel Discovery |
title_short | Contextualizing Genes by Using Text-Mined Co-Occurrence Features for Cancer Gene Panel Discovery |
title_sort | contextualizing genes by using text-mined co-occurrence features for cancer gene panel discovery |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8573063/ https://www.ncbi.nlm.nih.gov/pubmed/34759963 http://dx.doi.org/10.3389/fgene.2021.771435 |
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