Gene Family Abundance Visualization based on Feature Selection Combined Deep Learning to Improve Disease Diagnosis

Hai Thanh Nguyen (College of Information Communication and Technology, Can Tho University Campus II, 3/2 Street, Ninh Kieu District, Can Tho city, 900000,)
Tai Tan Phan (College of Information Communication and Technology, Can Tho University Campus II, 3/2 Street, Ninh Kieu District, Can Tho city, 900000,)
Tinh Cong Dao (College of Information Communication and Technology, Can Tho University Campus II, 3/2 Street, Ninh Kieu District, Can Tho city, 900000,)
Phuc Vinh Dang Ta (College of Information Communication and Technology, Can Tho University Campus II, 3/2 Street, Ninh Kieu District, Can Tho city, 900000,)
Cham Ngoc Thi Nguyen (College of Information Communication and Technology, Can Tho University Campus II, 3/2 Street, Ninh Kieu District, Can Tho city, 900000,)
Ngoc Huynh Pham (College of Information Communication and Technology, Can Tho University Campus II, 3/2 Street, Ninh Kieu District, Can Tho city, 900000,)
Hiep Xuan Huynh (College of Information Communication and Technology, Can Tho University Campus II, 3/2 Street, Ninh Kieu District, Can Tho city, 900000,)

Journal of Engineering and Technological Sciences
Vol. 53 No. 1 (2021)

Advancements in machine learning in general and in deep learning in particular have achieved great success in numerous fields. For personalized medicine approaches, frameworks derived from learning algorithms play an important role in supporting scientists to investigate and explore novel data sources such as metagenomic data to develop and examine methodologies to improve human healthcare. Some challenges when processing this data type include its very high dimensionality and the complexity of diseases. Metagenomic data that include gene families often have millions of features. This leads to a further increase of complexity in processing and requires a huge amount of time for computation. In this study, we propose a method combining feature selection using perceptron weight-based filters and synthetic image generation to leverage deep-learning advancements in order to predict various diseases based on gene family abundance data. An experiment was conducted using gene family datasets of five diseases, i.e. liver cirrhosis, obesity, inflammatory bowel diseases, type 2 diabetes, and colorectal cancer. The proposed method provides not only visualization for gene family abundance data but also achieved a promising performance level.