Generate bioinformatics data using generative adversaria l network: a review

Abstract

Data is the most important part in machine learning. In bioinformatics field the sensitivity of the data is high and due to that the accessibility of the data for a secondary purpose (e.g.: research) is consist with many legal and ethical issues. Due to that in many bioinformatics researches collecting the data consume more time than the development phase. There are some researches done to solve the legal and ethical issues by anonymising the data using encryption, de-identification and perturbation of potentially identifiable attributes. For some extend those solutions restricted the data breach but in other hand anonymized data not performed well during the analysis and mining tasks. Recently Generative adversarial networks (GANs) have become a research focus of artificial intelligence. The goal of GANs is to estimate the potential distribution of real data samples and generate new samples from that distribution. Here, researcher review GAN in bioinformatics to generate data sets, presenting examples of current research. To provide a useful and comprehensive perspective, Researcher categorize research both by the bioinformatics data and GAN architecture and flow. Additionally, discussed about the issues of GAN in bioinformatics to generate data sets and suggest future research directions. Researcher believes that this review will provide valuable insights for researchers to apply GAN to generate bioinformatics data sets.