A Knowledge-Based Discovery Approach Couples Artificial Neural Networks With Weight Engineering to Uncover Immune-Related Processes Underpinning Clinical Traits of Breast Cancer

Cheng Zhang, Cristina Correia, Taylor M. Weiskittel, Shyang Hong Tan, Kevin Meng-Lin, Grace T. Yu, Jingwen Yao, Kok Siong Yeo, Shizhen Zhu, Choong Yong Ung, Hu Li

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number920669
JournalFrontiers in immunology
Volume13
DOIs
StatePublished - Jul 14 2022

Keywords

  • artificial intelligence
  • clinical traits
  • gene–gene associations
  • neural networks
  • weight engineering

ASJC Scopus subject areas

  • Immunology and Allergy
  • Immunology

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