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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
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.
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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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