Journal
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
Volume 19, Issue 4, Pages 2060-2070Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2021.3066086
Keywords
Feature extraction; Breast cancer; Gene expression; Genomics; Deep learning; Biological information theory; Bioinformatics; Neural network; deep learning; genetic data; feature extraction; adversarial autoencoder
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Funding
- Sydney Informatics Hub - University of Sydney
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In this paper, the authors propose a dual-stage neural network architecture named AFExNet, based on adversarial auto-encoder (AAE), to extract features from high-dimensional genetic data. The performance of AFExNet is evaluated using twelve different supervised classifiers, demonstrating consistent results across all performance metrics and classifier independence.
Technological advancements in high-throughput genomics enable the generation of complex and large data sets that can be used for classification, clustering, and bio-marker identification. Modern deep learning algorithms provide us with the opportunity of finding most significant features in such huge dataset to characterize diseases (e.g., cancer) and their sub-types. Thus, developing such deep learning method, which can successfully extract meaningful features from various breast cancer sub-types, is of current research interest. In this paper, we develop dual stage (unsupervised pre-training and supervised fine-tuning) neural network architecture termed AFExNet based on adversarial auto-encoder (AAE) to extract features from high dimensional genetic data. We evaluated the performance of our model through twelve different supervised classifiers to verify the usefulness of the new features using public RNA-Seq dataset of breast cancer. AFExNet provides consistent results in all performance metrics across twelve different classifiers which makes our model classifier independent. We also develop a method named 'TopGene' to find highly weighted genes from the latent space which could be useful for finding cancer bio-markers. Put together, AFExNet has great potential for biological data to accurately and effectively extract features. Our work is fully reproducible and source code can be downloaded from Github: https://github.com/NeuroSyd/breast-cancer-sub-types.
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