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A Knowledge-Based Discovery Approach Couples Artificial Neural Networks With Weight Engineering to Uncover Immune-Related Processes Underpinning Clinical Traits of Breast Cancer
Immune-related processes are important in underpinning the properties of clinical traits such as prognosis and drug response in cancer. The possibility to extract knowledge learned by artificial neural networks (ANNs) from omics data to explain cancer clinical traits is a very attractive subject for...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9330471/ https://www.ncbi.nlm.nih.gov/pubmed/35911770 http://dx.doi.org/10.3389/fimmu.2022.920669 |
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author | Zhang, Cheng Correia, Cristina Weiskittel, Taylor M. Tan, Shyang Hong Meng-Lin, Kevin Yu, Grace T. Yao, Jingwen Yeo, Kok Siong Zhu, Shizhen Ung, Choong Yong Li, Hu |
author_facet | Zhang, Cheng Correia, Cristina Weiskittel, Taylor M. Tan, Shyang Hong Meng-Lin, Kevin Yu, Grace T. Yao, Jingwen Yeo, Kok Siong Zhu, Shizhen Ung, Choong Yong Li, Hu |
author_sort | Zhang, Cheng |
collection | PubMed |
description | Immune-related processes are important in underpinning the properties of clinical traits such as prognosis and drug response in cancer. The possibility to extract knowledge learned by artificial neural networks (ANNs) from omics data to explain cancer clinical traits is a very attractive subject for novel discovery. Recent studies using a version of ANNs called autoencoders revealed their capability to store biologically meaningful information indicating that autoencoders can be utilized as knowledge discovery platforms aside from their initial assigned use for dimensionality reduction. Here, we devise an innovative weight engineering approach and ANN platform called artificial neural network encoder (ANNE) using an autoencoder and apply it to a breast cancer dataset to extract knowledge learned by the autoencoder model that explains clinical traits. Intriguingly, the extracted biological knowledge in the form of gene–gene associations from ANNE shows immune-related components such as chemokines, carbonic anhydrase, and iron metabolism that modulate immune-related processes and the tumor microenvironment play important roles in underpinning breast cancer clinical traits. Our work shows that biological “knowledge” learned by an ANN model is indeed encoded as weights throughout its neuronal connections, and it is possible to extract learned knowledge via a novel weight engineering approach to uncover important biological insights. |
format | Online Article Text |
id | pubmed-9330471 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93304712022-07-29 A Knowledge-Based Discovery Approach Couples Artificial Neural Networks With Weight Engineering to Uncover Immune-Related Processes Underpinning Clinical Traits of Breast Cancer Zhang, Cheng Correia, Cristina Weiskittel, Taylor M. Tan, Shyang Hong Meng-Lin, Kevin Yu, Grace T. Yao, Jingwen Yeo, Kok Siong Zhu, Shizhen Ung, Choong Yong Li, Hu Front Immunol Immunology Immune-related processes are important in underpinning the properties of clinical traits such as prognosis and drug response in cancer. The possibility to extract knowledge learned by artificial neural networks (ANNs) from omics data to explain cancer clinical traits is a very attractive subject for novel discovery. Recent studies using a version of ANNs called autoencoders revealed their capability to store biologically meaningful information indicating that autoencoders can be utilized as knowledge discovery platforms aside from their initial assigned use for dimensionality reduction. Here, we devise an innovative weight engineering approach and ANN platform called artificial neural network encoder (ANNE) using an autoencoder and apply it to a breast cancer dataset to extract knowledge learned by the autoencoder model that explains clinical traits. Intriguingly, the extracted biological knowledge in the form of gene–gene associations from ANNE shows immune-related components such as chemokines, carbonic anhydrase, and iron metabolism that modulate immune-related processes and the tumor microenvironment play important roles in underpinning breast cancer clinical traits. Our work shows that biological “knowledge” learned by an ANN model is indeed encoded as weights throughout its neuronal connections, and it is possible to extract learned knowledge via a novel weight engineering approach to uncover important biological insights. Frontiers Media S.A. 2022-07-14 /pmc/articles/PMC9330471/ /pubmed/35911770 http://dx.doi.org/10.3389/fimmu.2022.920669 Text en Copyright © 2022 Zhang, Correia, Weiskittel, Tan, Meng-Lin, Yu, Yao, Yeo, Zhu, Ung and Li 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 | Immunology Zhang, Cheng Correia, Cristina Weiskittel, Taylor M. Tan, Shyang Hong Meng-Lin, Kevin Yu, Grace T. Yao, Jingwen Yeo, Kok Siong Zhu, Shizhen Ung, Choong Yong Li, Hu A Knowledge-Based Discovery Approach Couples Artificial Neural Networks With Weight Engineering to Uncover Immune-Related Processes Underpinning Clinical Traits of Breast Cancer |
title | A Knowledge-Based Discovery Approach Couples Artificial Neural Networks With Weight Engineering to Uncover Immune-Related Processes Underpinning Clinical Traits of Breast Cancer |
title_full | A Knowledge-Based Discovery Approach Couples Artificial Neural Networks With Weight Engineering to Uncover Immune-Related Processes Underpinning Clinical Traits of Breast Cancer |
title_fullStr | A Knowledge-Based Discovery Approach Couples Artificial Neural Networks With Weight Engineering to Uncover Immune-Related Processes Underpinning Clinical Traits of Breast Cancer |
title_full_unstemmed | A Knowledge-Based Discovery Approach Couples Artificial Neural Networks With Weight Engineering to Uncover Immune-Related Processes Underpinning Clinical Traits of Breast Cancer |
title_short | A Knowledge-Based Discovery Approach Couples Artificial Neural Networks With Weight Engineering to Uncover Immune-Related Processes Underpinning Clinical Traits of Breast Cancer |
title_sort | knowledge-based discovery approach couples artificial neural networks with weight engineering to uncover immune-related processes underpinning clinical traits of breast cancer |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9330471/ https://www.ncbi.nlm.nih.gov/pubmed/35911770 http://dx.doi.org/10.3389/fimmu.2022.920669 |
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