Ping Zhang  Ping Zhang photo       

contact information

Research Staff Member - Center for Computational Health
IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, USA
  +1dash914dash945dash4498

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Professional Associations

Professional Associations:  ACM SIGKDD  |  American Medical Informatics Association  |  IEEE Computer Society  |  International Society for Computational Biology


SDM 2014 Tutorial
Data Mining in Drug Discovery and Development

Speakers: Ping Zhang (IBM T.J. Waston Research Center), Lun Yang (GlaxoSmithKline)

Abstract:
Increasingly, effective drug discovery and development involve the searching and data mining of large amounts of heterogeneous information from many sources covering the domains of chemistry, biology and pharmacology amongst others. In this tutorial, we provide a review of the publicly-available large-scale databases relevant to drug discovery, describe the kinds of data mining approaches that can be applied to them, and identify directions for future research. Many of those insights come from drug discovery community, which is highly related to data mining but focuses on bioinformatics and/or cheminformatics specifics. We survey various related articles from data mining venues as well as from bioinformatics/cheminformatics venues to share with the audience key problems and trends in drug discovery research, with different applications such as drug combinations, drug repositioning, personalized medicine, and payer evidence.

 

SDM 2016 Tutorial

Biomedical Data Mining with Matrix Models (part 1: foundations; part 2: applications)

Speakers: Fei Wang (University of Connecticut), Ping Zhang (IBM T.J. Waston Research Center)

Abstract:

In the last decade, advances in high-throughput technologies, growth of clinical data warehouses, and rapid accumulation of biomedical knowledge provided unprecedented opportunities and challenges to researchers in biomedical informatics. One distinct solution, to efficiently conduct big data analytics for biomedical problems, is the application of matrix computation and factorization methods such as non-negative matrix factorization, joint matrix factorization, tensor factorization. Compared to probabilistic and information theoretic approaches, matrix-based methods are fast, easy to understand and implement. In this tutorial, we provide a review of recent advances in algorithms and methods using matrix and their potential applications in biomedical informatics. We survey various related articles from data mining venues as well as from biomedical informatics venues to share with the audience key problems and trends in matrix computation research, with different novel applications such as drug repositioning, personalized medicine, and electronic phenotyping.

 

KDD 2016 Tutorial

Healthcare Data Mining with Matrix Models (part 1, part 2)

Speakers: Fei Wang (Cornell University), Ping Zhang (IBM T.J. Waston Research Center), Joel Dudley (Icahn School of Medicine at Mount Sinai)

 

ICHI 2016 Tutorial

Towards Large-Scale Drug Safety Surveillance: A Big Data Perspective

Speakers: Ying Li (IBM T.J. Waston Research Center), Ping Zhang (IBM T.J. Waston Research Center)

 

CIKM 2016 Tutorial

Big Data Science in Drug Discovery and Development (part 1, part 2, part 3)

Speakers: Ping Zhang (IBM T.J. Waston Research Center), Xia Ning (Indiana University - Purdue University Indianapolis), David Wild (Indiana University)