Data Generation Using Gene Expression Generator

Farou, Zakarya; Mouhoub, Noureddine; Horváth, Tomáš

Abstract

Generative adversarial networks (GANs) could be used efficiently for image and video generation when labeled training data is available in bulk. In general, building a good machine learning model requires a reasonable amount of labeled training data. However, there are areas such as the biomedical field where the creation of such a dataset is time-consuming and requires expert knowledge. Thus, the aim is to use data augmentation techniques as an alternative to data collection to improve data classification. This paper presents the use of a modified version of a GAN called Gene Expression Generator (GEG) to augment the available data samples. The proposed approach was used to generate synthetic data for binary biomedical datasets to train existing supervised machine learning approaches. Experimental results show that the use of GEG for data augmentation with a modified version of leave one out cross-validation (LOOCV) increases the performance of classification accuracy.

Citation

Farou, Z., Mouhoub, N., & Horváth, T. (2020, November). Data Generation Using Gene Expression Generator. Presented at IDEAL 2020: 21st International Conference on Intelligent Data Engineering and Automated Learning, Guimarães, Portugal

• Presentation Conference Type: Conference Paper (published)
• Conference Name: IDEAL 2020: 21st International Conference on Intelligent Data Engineering and Automated Learning
• Start Date: Nov 4, 2020
• End Date: Nov 6, 2020
• Online Publication Date: Oct 27, 2020
• Publication Date: 2020
• Deposit Date: Apr 8, 2024
• Publisher: Springer
• Pages: 54-65
• Series Title: Lecture Notes in Computer Science
• Series Number: 12490
• Series ISSN: 0302-9743
• Book Title: Intelligent Data Engineering and Automated Learning – IDEAL 2020: 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part II
• ISBN: 9783030623647
• DOI: https://doi.org/10.1007/978-3-030-62365-4_6
• Keywords: Data generation, Generative adversarial networks, Gene expression data, Cancer classification
• Public URL: http://researchrepository.napier.ac.uk/Output/3587441