Abstract

Predicting gene expression in DREAM Challenges 2022 is beneficial for gaining insights into gene function, biological pathways, and genes involved in regulating development, cell behavior, and signaling. In this study, a novel ensemble model that integrates “proformer” and ensemble learning techniques is proposed. The approach begins with preprocessing a large dataset of gene sequences provided by the DREAM Challenges 2022. Multiple “proformers” are then constructed, each capable of independently predicting gene expression. The resulting gene expression predictions from the “proformers” are combined through averaging weighted summation of individual prediction from each “proformer” to produce final predictions. Experimental results demonstrated that the proposed model is able to effectively enhance the prediction performance through comprehensive evaluation with various metrics, and even outperformed the winner in the DREAM Challenges 2022.

Speaker Bio

Dr. Xishuang Dong is a member of CRI Center for Computational Systems Biology and CREDIT, and Assistant Professor at Department of Electrical and Computer Engineering at Prairie View A&M University (PVAMU). He received B.S. degree in computer science and technique at Harbin University of Science and Technology, M.S. degree in computer software and theory at Harbin Engineering University, and Ph.D. in computer application at Harbin Institute of Technology. His research interests include: (1) machine learning based computational systems biology; (2) biomedical information processing; (3) deep learning for big data analysis; (4) natural language processing.