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ForestSubtype: a cancer subtype identifying approach based on high-dimensional genomic data and a parallel random forest
BACKGROUND: Cancer subtype classification is helpful for personalized cancer treatment. Although, some approaches have been developed to classifying caner subtype based on high dimensional gene expression data, it is difficult to obtain satisfactory classification results. Meanwhile, some cancers ha...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10354904/ https://www.ncbi.nlm.nih.gov/pubmed/37468832 http://dx.doi.org/10.1186/s12859-023-05412-y |
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author | Luo, Junwei Feng, Yading Wu, Xuyang Li, Ruimin Shi, Jiawei Chang, Wenjing Wang, Junfeng |
author_facet | Luo, Junwei Feng, Yading Wu, Xuyang Li, Ruimin Shi, Jiawei Chang, Wenjing Wang, Junfeng |
author_sort | Luo, Junwei |
collection | PubMed |
description | BACKGROUND: Cancer subtype classification is helpful for personalized cancer treatment. Although, some approaches have been developed to classifying caner subtype based on high dimensional gene expression data, it is difficult to obtain satisfactory classification results. Meanwhile, some cancers have been well studied and classified to some subtypes, which are adopt by most researchers. Hence, this priori knowledge is significant for further identifying new meaningful subtypes. RESULTS: In this paper, we present a combined parallel random forest and autoencoder approach for cancer subtype identification based on high dimensional gene expression data, ForestSubtype. ForestSubtype first adopts the parallel RF and the priori knowledge of cancer subtype to train a module and extract significant candidate features. Second, ForestSubtype uses a random forest as the base module and ten parallel random forests to compute each feature weight and rank them separately. Then, the intersection of the features with the larger weights output by the ten parallel random forests is taken as our subsequent candidate features. Third, ForestSubtype uses an autoencoder to condenses the selected features into a two-dimensional data. Fourth, ForestSubtype utilizes k-means++ to obtain new cancer subtype identification results. In this paper, the breast cancer gene expression data obtained from The Cancer Genome Atlas are used for training and validation, and an independent breast cancer dataset from the Molecular Taxonomy of Breast Cancer International Consortium is used for testing. Additionally, we use two other cancer datasets for validating the generalizability of ForestSubtype. ForestSubtype outperforms the other two methods in terms of the distribution of clusters, internal and external metric results. The open-source code is available at https://github.com/lffyd/ForestSubtype. CONCLUSIONS: Our work shows that the combination of high-dimensional gene expression data and parallel random forests and autoencoder, guided by a priori knowledge, can identify new subtypes more effectively than existing methods of cancer subtype classification. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05412-y. |
format | Online Article Text |
id | pubmed-10354904 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-103549042023-07-20 ForestSubtype: a cancer subtype identifying approach based on high-dimensional genomic data and a parallel random forest Luo, Junwei Feng, Yading Wu, Xuyang Li, Ruimin Shi, Jiawei Chang, Wenjing Wang, Junfeng BMC Bioinformatics Research BACKGROUND: Cancer subtype classification is helpful for personalized cancer treatment. Although, some approaches have been developed to classifying caner subtype based on high dimensional gene expression data, it is difficult to obtain satisfactory classification results. Meanwhile, some cancers have been well studied and classified to some subtypes, which are adopt by most researchers. Hence, this priori knowledge is significant for further identifying new meaningful subtypes. RESULTS: In this paper, we present a combined parallel random forest and autoencoder approach for cancer subtype identification based on high dimensional gene expression data, ForestSubtype. ForestSubtype first adopts the parallel RF and the priori knowledge of cancer subtype to train a module and extract significant candidate features. Second, ForestSubtype uses a random forest as the base module and ten parallel random forests to compute each feature weight and rank them separately. Then, the intersection of the features with the larger weights output by the ten parallel random forests is taken as our subsequent candidate features. Third, ForestSubtype uses an autoencoder to condenses the selected features into a two-dimensional data. Fourth, ForestSubtype utilizes k-means++ to obtain new cancer subtype identification results. In this paper, the breast cancer gene expression data obtained from The Cancer Genome Atlas are used for training and validation, and an independent breast cancer dataset from the Molecular Taxonomy of Breast Cancer International Consortium is used for testing. Additionally, we use two other cancer datasets for validating the generalizability of ForestSubtype. ForestSubtype outperforms the other two methods in terms of the distribution of clusters, internal and external metric results. The open-source code is available at https://github.com/lffyd/ForestSubtype. CONCLUSIONS: Our work shows that the combination of high-dimensional gene expression data and parallel random forests and autoencoder, guided by a priori knowledge, can identify new subtypes more effectively than existing methods of cancer subtype classification. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05412-y. BioMed Central 2023-07-19 /pmc/articles/PMC10354904/ /pubmed/37468832 http://dx.doi.org/10.1186/s12859-023-05412-y 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 | Research Luo, Junwei Feng, Yading Wu, Xuyang Li, Ruimin Shi, Jiawei Chang, Wenjing Wang, Junfeng ForestSubtype: a cancer subtype identifying approach based on high-dimensional genomic data and a parallel random forest |
title | ForestSubtype: a cancer subtype identifying approach based on high-dimensional genomic data and a parallel random forest |
title_full | ForestSubtype: a cancer subtype identifying approach based on high-dimensional genomic data and a parallel random forest |
title_fullStr | ForestSubtype: a cancer subtype identifying approach based on high-dimensional genomic data and a parallel random forest |
title_full_unstemmed | ForestSubtype: a cancer subtype identifying approach based on high-dimensional genomic data and a parallel random forest |
title_short | ForestSubtype: a cancer subtype identifying approach based on high-dimensional genomic data and a parallel random forest |
title_sort | forestsubtype: a cancer subtype identifying approach based on high-dimensional genomic data and a parallel random forest |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10354904/ https://www.ncbi.nlm.nih.gov/pubmed/37468832 http://dx.doi.org/10.1186/s12859-023-05412-y |
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