Peipei Ping, Ph.D.


Work Titles
UCLA Professor, Medicine Professor, Physiology Member, Bioinformatics GPB Home Area Member, Medical Informatics GPB Home Area Member, Molecular, Cellular & Integrative Physiology GPB Home Area Faculty, Cardiovascular Research Laboratory Faculty, Cardiology Affiliated Faculty, Scalable Analytics Institute (ScAi)
Education:
Degrees:
Ph.D., University of Arizona, 1985 - 1990

Contact Information:

Email Address:

ppingucla@gmail.com


Website:

Laboratory Website

Fax Number:

310-267-5623

Lab Number:

310-267-5624

Office Phone Number:

310-206-0058

Work Address:

Office
Rm 1619 MRL
675 Charles E. Young Dr. South
Los Angeles, CA 90095


Research Interest:

Projects in our program are supported by either an NHLBI supported training program (T32 HL139450) for integrated data science training in cardiovascular medicine and/or an NIH award (R35 HL135772) for omics phenotyping via an integrated data science platform.

These projects frequently integrate one or more of the following approaches or topics: Multi-Omics Profiling and Integration, Time-series or Temporal Analysis of Omics Data, Machine Learning-supported Information Extraction (Text Mining), Data Harmonization, Biomarker Discovery, and Knowledgebase Development. Our overall goal is to reach a better understanding of relationships among molecular phenotypes, symptoms, and disease, with a focus on cardiovascular pathophysiology.

Current interests with rotation openings include:

  1. Massive Information Extraction and Deep Analysis of Text Data: Implementing machine learning (ML)-supported information extraction approaches, adapting advanced data structures (e.g., knowledge graphs), and leveraging clinical coding systems (e.g., ICD-11) to extract relationships among clinical observations and disease diagnoses from clinical text documents. (Caufield, Zhou, & Garlid et al., 2018; Caufield et al., 2018; Liem & Maruli et al., 2018; and Ping et al., 2017)
  2. Harmonizing Knowledge and New Discovery of Therapeutics in Cardiovascular Medicine: Applying data science strategies to uncover hidden relationships among cardiovascular drugs, their molecular targets, and potential adverse effects in the setting of the pathogenesis of heart disease. (Caufield, Zhou, & Garlid et al., 2018; Ping et al., 2017; see rotation project by Samir Akre)
  3. ML-supported Omics Phenotyping of Human Diseases: Developing technological platforms and computational tools for in-depth analysis of integrated omics data to elucidate protein temporal dynamics, post-translational modifications, and metabolic responses during cardiac remodeling and disease progression. (Chung & Mirza et al., 2019; Wang & Choi et al., 2018; Lau et al., 2018; Perez-Riverol & Bai et al., 2017; Lam et al., 2016; and Lau et al., 2016)

Examples of recent rotation projects include “Evaluating Disease Normalization Methods on Biomedical Text” (Henry Zheng, Fall 2019) and “Developing Knowledge Graphs relating Cardiovascular Disease and Oxidative Stress”, (Samir Akre, Fall 2019).

The Ping research group has had a history of promoting high-quality research, advocating for integration between omics and data science, and receiving recognition from the international scientific community. Understanding cardiovascular physiology and pathophysiology has been a long-term goal of Dr. Ping’s research program. Her research also seeks to adapt emerging data science, natural language processing, machine learning, and information extraction techniques to solving relevant questions in cardiovascular medicine. Providing education and training to the next generation of investigators is a major mission of our laboratory. Dr. Ping has had 20 years of experience in mentoring students; 17 of her former trainees currently hold positions in academic institutions, including UCLA, UC Davis, University of Colorado, University of Heidelberg, and Fudan University. In parallel, 41 students trained in her laboratory hold positions in industry, including Amazon, Google, Microsoft, and Intel.

Publications:

A selected list of publications:

(For a full publication list, please see the   link , 2020; NIH: U.S. National Library of Medicine).
Chung NC, Mirza B, Choi H, Wang J, Wang D, Ping P, Wang W.   Unsupervised classification of multi-omics data during cardiac remodeling using deep learning Methods, 2019; 166: 66-73.
Caufield, J. H., Zhou, Y., Garlid, A. O., Liem, D. A., Cao, Q., Lee, J. M., Murali, S., Spendlove, S., Wang, W., Zhang, L., Sun, Y., Han, J., Watson, K. A., & Ping, P.   A reference set of curated biomedical data and metadata from clinical case reports Scientific data, 2018; 5: 180258.
Caufield, J. H., Liem, D. A., Garlid, A. O., Watson, K., Bui, A. T., Wang, W., & Ping, P.   A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts Journal of visualized experiments : JoVE, 2018; 5(139): 180258.
Wang J, Choi H, Chung NC, Cao Q, Ng DCM, Mirza B, Scruggs SB, Wang D, Garlid AO, Ping P.   Integrated Dissection of Cysteine Oxidative Post-translational Modification Proteome During Cardiac Hypertrophy Journal of proteome research, 2018; 17(12): 4243-4257.
Liem, D., Murali, S., Sigdel, D., Shi, Y., Wang, X., Shen, J., Choi, H., Caufield, J. H., Wang, W., Ping, P., & Han, J.   Phrase mining of textual data to analyze extracellular matrix protein patterns across cardiovascular disease American journal of physiology. Heart and circulatory physiology, 2018; 315(4): H910-H924.
Lau E, Cao Q, Lam MPY, Wang J, Ng DCM, Bleakley BJ, Lee JM, Liem DA, Wang D, Hermjakob H, Ping P.   Integrated omics dissection of proteome dynamics during cardiac remodeling Nature communications, 2018; 9(1): 120.
Perez-Riverol Y, Bai M, da Veiga Leprevost F, Squizzato S, Park YM, Haug K, Carroll AJ, Spalding D, Paschall J, Wang M, Del-Toro N, Ternent T, Zhang P, Buso N, Bandeira N, Deutsch EW, Campbell DS, Beavis RC, Salek RM, Sarkans U, Petryszak R, Keays M, Fahy E, Sud M, Subramaniam S, Barbera A, Jiménez RC, Nesvizhskii AI, Sansone SA, Steinbeck C, Lopez R, Vizcaíno JA, Ping P, Hermjakob H.   Discovering and linking public omics data sets using the Omics Discovery Index Nature biotechnology, 2017; 35(5): 406-409.
Ping P, Watson K, Han J, Bui A.   Individualized Knowledge Graph: A Viable Informatics Path to Precision Medicine Circulation research, 2017; 120(7): 1078-1080.
Lam MP, Ping P, Murphy E.   Proteomics Research in Cardiovascular Medicine and Biomarker Discovery Journal of the American College of Cardiology, 2016; 68(25): 2819-2830.
Lam MP, Lau E, Ng DC, Wang D, Ping P.   Cardiovascular proteomics in the era of big data: experimental and computational advances Clinical proteomics, 2016; 13(25): 23.
Lau E, Watson KE, Ping P.   Connecting the Dots: From Big Data to Healthy Heart Circulation, 2016; 134(5): 362-4.
Lam MP, Venkatraman V, Xing Y, Lau E, Cao Q, Ng DC, Su AI, Ge J, Van Eyk JE, Ping P.   Data-Driven Approach To Determine Popular Proteins for Targeted Proteomics Translation of Six Organ Systems Journal of proteome research, 2016; 15(11): 4126-4134.
Lau E, Cao Q, Ng DC, Bleakley BJ, Dincer TU, Bot BM, Wang D, Liem DA, Lam MP, Ge J, Ping P.   A large dataset of protein dynamics in the mammalian heart proteome Scientific data, 2016; 3(11): 160015.
Ping P, Gustafsson ÅB, Bers DM, Blatter LA, Cai H, Jahangir A, Kelly D, Muoio D, O'Rourke B, Rabinovitch P, Trayanova N, Van Eyk J, Weiss JN, Wong R, Schwartz Longacre L.   Harnessing the Power of Integrated Mitochondrial Biology and Physiology: A Special Report on the NHLBI Mitochondria in Heart Diseases Initiative Circulation research, 2015; 117(3): 234-8.

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