Journal Article

An autoencoder learning method for predicting breast cancer subtypes

Masnadi-Shirazi, Maryam, Subramaniam, Shankar, Rostami, Zahra, Mukund, Kavitha

2025

Overview

Heterogeneity of breast cancer poses several challenges for detection and treatment. With next-generation sequencing, we can now map the transcriptional profile of each patient’s breast tissue, which has the potential for identifying and characterizing cancer subtypes. However, the large dimensionality of this transcriptomic data and the heterogeneity between the molecular profiles of breast cancers poses a barrier to identifying minimal markers and mechanistic consequences. In this study, we develop an autoencoder to identify a reduced set of gene markers that characterize the four major breast cancer subtypes with the accuracy of 82.38%. The reduced feature space created by our model captures the functional characteristics of each breast cancer subtype highlighting mechanisms that are unique to each subtype as well as those that are shared. Our high prediction accuracy shows that our markers can be valuable for breast cancer subtype detection and have the potential to provide insights into mechanisms associated with each subtype.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Autoencoder / Biology and Life Sciences / Biomarkers, Tumor - genetics / Breast cancer / Breast Neoplasms - classification / Breast Neoplasms - diagnosis / Breast Neoplasms - genetics / Breast Neoplasms - pathology / Computer and Information Sciences / DNA sequencing / Engineering and Technology / Feature selection / Female / Gene expression / Gene Expression Profiling / Gene Expression Regulation, Neoplastic / Genetic aspects / Genetic markers / Genomics / Health aspects / Heterogeneity / Humans / Machine Learning / Medicine and Health Sciences / Methods / Neural networks / Next-generation sequencing / Nucleotide sequencing / Research and Analysis Methods / Support vector machines / Transcriptome / Transcriptomics