A DEEP LEARNING APPROACH FOR CANCER DETECTION AND RELEVANT GENE IDENTIFICATION

• Published in: Biocomputing 2017 Vol. 22; pp. 219 - 229
• Main Authors: DANAEE, PADIDEH, GHAEINI, REZA, HENDRIX, DAVID A.
• Format: Book Chapter Journal Article
• Language: English
• Published: United States WORLD SCIENTIFIC 01.01.2017
• Subjects: Algorithms; Biomarkers, Tumor - genetics; Breast Neoplasms - classification; Breast Neoplasms - diagnosis; Breast Neoplasms - genetics; Computational Biology; Databases, Genetic - statistics & numerical data; Female; Gene Expression Profiling - statistics & numerical data; Gene Ontology; Humans; PATTERNS IN BIOMEDICAL DATA-HOW DO WE FIND THEM?; Principal Component Analysis; Supervised Machine Learning; Stacked Denoising Autoencoder; Dimensionality Reduction; Cancer Detection; RNA-seq Expression; Deep Learning; Classification
• ISBN: 9789813207820; 9789813207806; 9813207825; 9813207809; 9789813207813; 9813207817
• ISSN: 2335-6936
• DOI: 10.1142/9789813207813_0022

Cancer detection from gene expression data continues to pose a challenge due to the high dimensionality and complexity of these data. After decades of research there is still uncertainty in the clinical diagnosis of cancer and the identification of tumor-specific markers. Here we present a deep learning approach to cancer detection, and to the identification of genes critical for the diagnosis of breast cancer. First, we used Stacked Denoising Autoencoder (SDAE) to deeply extract functional features from high dimensional gene expression profiles. Next, we evaluated the performance of the extracted representation through supervised classification models to verify the usefulness of the new features in cancer detection. Lastly, we identified a set of highly interactive genes by analyzing the SDAE connectivity matrices. Our results and analysis illustrate that these highly interactive genes could be useful cancer biomarkers for the detection of breast cancer that deserve further studies.