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 (Frontiers in Immunology, 2022)
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 omic 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 besides 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 on breast cancer dataset to extract knowledge learned by 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 tumor microenvironment play important roles in underpinning breast cancer clinical traits. Our work shows that biological “knowledge” learned by an ANN model is encoded as weights throughout its neuronal connections and it is possible to extract learned knowledge via novel weight engineering approach to uncover important biological insights. Resources and code are available at www.anne.hulilab.org.