AI-powered biological text mining and knowledge discovery

Traditionally, biomedical knowledge is stored in scientific publications. Reading published papers, reviews and books is the way to grasp what has been discovered and known in a particular scientific field. As scientific publications are being accumulated at an ever increasing speed, automatic extraction and effective organization of such information become critical for the integrative analysis of existing knowledge and experimental data.
We have developed a deep learning-powered search engine for biomedical literature, which can be accessed at BioKDE.com.

Selected publication and database/webserver:

• Jinchan Qu, Albert Steppi, Dongrui Zhong, Jie Hao, Jian Wang, Pei-Yau Lung, Tingting Zhao, Zhe He, Jinfeng Zhang. Triage of documents containing protein interactions affected by mutations using an NLP based machine learning approach. BMC Genomics, accepted.
• Pei-Yau Lung, Zhe He, Tingting Zhao, Disa Yu, Jinfeng Zhang. Extracting chemical–protein interactions from literature using sentence structure analysis and feature engineering. Database, 2019, bay138.
• Sentil Balaji, Charles Mcclendon, Rajesh Chowdhary, Jun S Liu and Jinfeng Zhang, IMID: Integrated molecular interaction database , Bioinformatics, (2012) 28 (5): 747-749.
• Lindsey Bell, Rajesh Chowdhary, Jun S Liu, Xufeng Niu, Jinfeng Zhang. Integrated bio-entity network: a system for biological knowledge discovery. PLoS ONE, 2011, 6(6): e21474, doi:10.1371/journal.pone.0021474.
• R Chowdhary, J Zhang, JS Liu. Bayesian Inference of Protein-protein Interactions from Biological Literature , Bioinformatics, 25(12), 1536-1542 (2009).

Proteins, the machines of life, are involved in most biological processes. Protein functions are determined by their structures, which is in turn determined by their sequences. We aim to achieve a better understanding of the sequence-structure-function relationship of proteins through computational approaches. In the past, we have worked on various problems from protein packing, energy functions for simplified models, side chain packing and side chain entropy, protein interactions, protein folding, loop modeling, and structure prediction.

Selected publications:

• Yuan Zhang, Xin Sui, Scott Stagg, Jinfeng Zhang. FTIP: An Accurate and Efficient Method for Global Protein Surface Comparison. Bioinformatics, 2020; 36(10):3056-3063.
• Yuan Zhang, Yang Chen, Chenran Wang, Chun-Chao Lo, Xiuwen Liu, Wei Wu, Jinfeng Zhang. ProDCoNN: Protein Design Using a Convolutional Neural Network. Proteins, 2020; 88(7):819-829.
• K Tang, J Zhang, J Liang. Distance-Guided Forward and Backward Chain-Growth Mo nte Carlo Method for Conformational Sampling and Structural Prediction of Antibody CDR-H3 Loops. Journal of Chemical Theory and Computation, 2017, 13 (1), 380–388.
• Ke Tang, Samuel Wong, Jun S. Liu, Jinfeng Zhang, Jie Liang, (2015) Conformational sampling and structure prediction of multiple interacting loops in soluble and beta-barrel membrane proteins using multi-loop distance-guided chain-growth Monte Carlo method. Bioinformatics, (2015) 31 (16): 2646-2652.
• J Laborde, D Robinson, A Srivastava, E Klassen and J Zhang, RNA global alignment in the joint sequence-structure space using Elastic Shape Analysis, Nucl. Acids Res. (2013) 41 (11): e114. doi: 10.1093/nar/gkt187
• W Liu, A Srivastava, J Zhang. A mathematical framework for protein structure comparison. PLoS Computational Biology 7(2), (2011): e1001075. doi:10.1371/journal.pcbi.1001075.
• J Zhang, SC Kou, JS Liu. Biopolymer structure simulation and optimization via fragment regrowth Monte Carlo. Journal of Chemical Physics, 126, 225101, (2007).
• J Zhang, JS Liu. On side-chain conformational entropy of proteins. PLoS Computational Biology, 2(12): e168. doi:10.1371/journal.pcbi.0020168, (2006)

Omics data analysis methods

We have also developed methods for omics data analysis including gene expression data and epigenomics data.

Selected publications in methods for omics data analysis:

• Serin Zhang, Jiang Shao, Disa Yu, Xing Qiu, Jinfeng Zhang. MatchMixeR: A Cross-platform Normalization Method for Gene Expression Data Integration. Bioinformatics, 2020;36(8):2486-2491.
• Senthil Girimurugan, Yuhang Liu, Pei-Yau Lung, Daniel Vera, Jonathan Dennis, Hank Bass, Jinfeng Zhang, iSeg: an efficient algorithm for segmentation of genomic and epigenomic data. BMC Bioinformatics, 2018, 19(1):131. doi: 10.1186/s12859-018-2140-3.
• Yuhang Liu, Jinfeng Zhang*, Xing Qiu*. Super-delta: a new differential gene expression analysis procedure with robust data normalization BMC Bioinformatics, 2017, 18:582, https://doi.org/10.1186/s12859-017-1992-2.

Cancer Genomics and Precision Medicine

Cancer is one of the leading causes of death and is the most costly disease. Using computational and integrative approaches, we aim to understand better the heterogeneity of cancer and apply the understanding to precision medicine.

Selected publications:

• Yan Li, Xiaodong Pang, Zihan Cui, Yidong Zhou, Feng Mao, Yan Lin, Xiaohui Zhang, Songjie Shen, Peixin Zhu, Tingting Zhao, Qiang Sun, Jinfeng Zhang. Genetic factors associated with cancer racial disparity - an integrative study across twenty-one cancer types. Molecular Oncology, accepted.
• Y Shi, A Steppi, Y Cao, J Wang, MM He, L Li, J Zhang. Integrative comparison of mRNA expression patterns in breast cancers from Caucasian and Asian Americans with implications for precision medicine. Cancer Research, 2017; 77(2):423-433.
• Yan Li, Albert Steppi, Yidong Zhou, Feng Mao, Philip Craig Miller, Max M. He, Tingting Zhao, Qiang Sun, Jinfeng Zhang*. Tumoral expression of drug and xenobiotic metabolizing enzymes in breast cancer patients of different ethnicities with implications to personalized medicine . Scientific Reports, 2017 7:4747. doi:10.1038/s41598-017-04250-2.
• Mayassa J. Bou-Dargham, Linlin Sha, Qing-Xiang Amy Sang, Jinfeng Zhang. Immune landscape of human prostate cancer: Immune evasion mechanisms and biomarkers for personalized immunotherapy. BMC Cancer, 20, Article number: 572 (2020).
• Kaixian Yu, Qing-Xiang Amy Sang, Pei-Yau Lung, Winston Tan, Ty Lively, Cedric Sheffield, Mayassa Bou Dargham, Jun S . Liu, Jinfeng Zhang. Personalized chemotherapy selection for breast cancer using gen e expression profiles. Scientific Reports, 2017 3;7:43294. doi: 10.1038/srep43294.