The research in my lab focuses on
developing machine learning, deep learning, and artificial intelligence (AI) methods to
analyze big biomedical data and address fundamental problems in biomedical sciences. Currently, we are
developing bioinformatics algorithms and tools for protein structure and function prediction, 3D genomics,
biological network modeling, and omics data analysis.
We have active projects in protein structure and function prediction, 3D genome structure modeling, inference and simulation of biological networks and systems, protein interaction and docking,
biological sequence alignments, transcriptomics (RNAseq data analysis), genomics, epigenomics, and proteomics. 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) in 2006, 2008, 2010, 2012, 2014, 2016 and 2018, respectively (e.g., the official CASP13 Results).
A brief presentation of the research in the Bioinformatics, Data Mining and Machine Learning (BDM) Lab
The citations to our research papers according to Google Scholar
In 2018 our MULTICOM protein structure prediction system was ranked among top three in protein tertiary structure modeling during the 13th Critical Assessment of Techniques for Protein Structure Prediction (CASP13). (Official ranking).