Synergistic Engineering and Big Data Analytics Research Facilitates Emerging Precision Medicine


Title:Synergistic Engineering and Big Data Analytics Research Facilitates Emerging Precision Medicine

Reporter   :Dr. Jack Yang Harvard Medical School

Time          :Oct.20,2016 14:30

Location    :The third floor meeting room of bioelectronics laboratory in Southeast University

Contacter  :SunXiao 


The advancement of our society depends on the swift technology development to tackle the exponential growth of big-data. Hence it has rendered enormous solicitation for developing novel engineering and intelligent computing approaches. In particular, with the advent of high-throughput next-generation sequencing technology, along with the inevitability to keep pace with the global priority in big-data analytics research, we are obligated to leverage engineering and intelligent computing tactics to synergistically integrate multi-layer genomic big-data at systems level to lug out molecular mechanisms relating to disease initiation, prognosis, pathway-based bio-marker and drug target identifications.

Given that large-scale genomic, transcriptomic and proteomic data have fostered unprecedented prospects to facilitate the emerging precision medicine research in the context of articulating the entire catalog of genomic alterations and pathways, we therefore developed seminal cross- disciplinary approaches to synergistically integrate gene expression profiles with protein interactions in erecting gene networks that can be rummaged for identifying effective biomarkers and disease associated pathways.

As protein-DNA and protein-RNA interactions mediate many biological processes that are essential for cellular function, it is mandatory to ascertain the DNA or RNA-binding amino acid residues in proteins for better apprehension of the molecular mechanisms relating to the protein-nucleic acid recognition. Utilizing machine learning and statistical analysis techniques, we innovatively constructed BindN+ and IUP software tools to predict DNA/RNA-binding sites and Intrinsically Unstructured Proteins (IUP) to this culmination. We demonstrated that our software tools outperformed the other machine learning based methods. We then further protracted our encounters to the integrative genomics and present here a case-study of kidney rental clear cell carcinoma, which contains multiple genomic data from more than 500 patients.

By incorporating the whole genome RNA-seq data, we are able to ascertain a set of molecular markers and perform further contemporaneous analysis of protein interactions that lead to reveal the disrupted networks in the disease development.Furthermore, we have developed an online tool called IDEAS toIdentify Differential Expression of genes for Applications in genome-wide Studies. We have utilized this tool to accelerate systematic knowledge discoveries.Exploiting our bracingly developed intelligent computing approaches, we are able to shed new light on comprehensively pinpointing bio-markers, drug targets and disturbed pathways. Developing pivotal engineering and computer science approaches can provide profound impact to catalyze the forthcoming precision medicine research. 



After receiving the MS and Ph.D. degrees, both from Purdue University (PU), West Lafayette main campus, Dr. Jack Yang taught Electrical and Computer Engineering (ECE) for the Purdue BSECE, MSECE and Ph.D. programs at Purdue Engineering College on Campus of Indiana University Purdue University Indianapolis (IUP). He then completed his Post Doctor from Harvard University and then conducted computer science and biomedical engineering research at Harvard Medical School. Dr. Yang’s cross disciplinary work aims to develop seminal engineering and computer science approaches to solve challenging big-data genomics and large-scale biomedical problems. He has published over one hundred SCI, PubMed and DBLP indexed articles to promote the emerging engineering and computer science research in biomedical fields. Dr. Yang’s synergistic efforts included his leadership roles as Editor-in-Chief of International Journal of Computational Biology and Drug Design, International Journal of Functional Informatics and Personalized Medicine and Chair of Board of Directors of International Society of Intelligent Biological Medicine. He also served as General Chair of IEEE 7th Bioinformatics and Bioengineering International Conference at Harvard University. Dr. Yang has extended teaching and research experience and is on editorial boards of a number of SCI indexed journals including Journal of Supercomputing and International Journal of Pattern Recognition and Artificial Intelligence. He has made significant contributions to advancing engineering and computer science research to biomedical fields.