The research in my lab focuses on developing machine learning, deep learning, and artificial intelligence (AI) methods to analyze big biological data and address fundamental problems in biological and medical sciences. Currently, we are developing  bioinformatics algorithms and tools for protein structure, interaction and function prediction, 3D genomics, biological network modeling, and omics data analysis. We have active projects in protein structure, interaction and function prediction, 3D genome structure modeling, inference and simulation of biological networks and systems, and omics (transcriptomics, genomics, epigenomics, and proteomics) data analysis (e.g., RNAseq data analysis). These projects are being funded by the National Institutes of Health (NIH), the National Science Foundation (NSF), and the Department of Energy (DOE).

The main techniques that we are developing include deep learning, artificial intelligence (AI), machine learning, data mining, optimization, and high performance computing (cloud computing and GPU). Our bioinformatics tools, web services, and datasets are freely available. Our MULTICOM suite for the prediction of protein structure and structural features were ranked among the best methods in the last several community-wide biennial Critical Assessments of Techniques for Protein Structure Prediction (CASP7, 8, 9, 10, 11, 12, 13, 14, and 15) in 2006, 2008, 2010, 2012, 2014, 2016, 2018, 2020, and 2022), respectively.

The citations to our research papers according to Google Scholar

Highlights:

In 2022 our MULTICOM methods were ranked No. 1 in estimating fold accuracy of protein quaternary structures (see the SCORE ranking), No.3 among server predictors of predicting protein quaternary structures, No. 3 among server predictors of predicting protein tertiary structures, and No. 7 among both human and server predictors of predicting both tertiary and quaternary structures in the 15th Community-Wide Critical Assessment of Techniques for Protein Structure Prediction (CASP15).