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:
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)
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)
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.
A selected list of publications:
(For a full publication list, please see the
U.S. National Library of Medicine).
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
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,
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,
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,
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
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
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,
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
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