Bruce Ling

Senior Research Scientist, Pediatrics - Neonatology

Bio

Clinical Focus
• Big data analytics for quality improvement and clinical effectiveness
• Disease biomarker discovery through multi-omics based analyses

Academic Appointments
• Assistant Professor Surgery

Professional Education
• B.S., Biochemistry, Fudan University, China (1990)
• M.A., Molecular and Developmental Biology, UCLA, US (1994)
• Ph.D., Biological Chemistry, UCLA, US (1996)
• Postdoctoral training, medicine/oncology/Computer science, Stanford University, US (1996-1998)
• Business administration, Leavey School of Business, Santa Clara University, US (2000-2001)

Academic Appointments

Senior Research Scientist, Pediatrics - Neonatology
Member, Bio-X
Member, Cardiovascular Institute
Member, Maternal & Child Health Research Institute (MCHRI)
Member, Stanford Cancer Institute

Current Research and Scholarly Interests

A significant focus of my career is the use of AI to decode real-world datasets of electronic health records, high-resolution LCMS-based liquid/tissue biopsy proteomics/metabolomics, and multiple modality medical imaging.

For population health management, we use tens of millions of real-world state-wide EMRs to develop risk surveillance systems that forecast aspects like disease progression, resource utilization, and mortality across a diverse patient demographic. This prompts timely clinical actions by simplifying intervention orders and crafting care strategies tailored to address modifiable patient risk components.

For first-in-class molecular diagnostics, we have developed unique LCMS based multi-omic approaches that allow the simultaneous absolute quantification of thousands of metabolites and proteins in blood and FFPE pathological slides to predict clinical outcomes. Our collaborations with key opinion leaders in pregnancy disorder and pediatric diseases, such as Kawasaki disease, have been productive and have helped to fill critical unmet medical needs.

For computer-aided pathology (CAP) and computer-aided medical imaging analytics (CAMIA), we have developed deep learning-based computational solutions to decode clinical outcome-correlating signals in pathological whole slide images and echocardiograms. Our multi-modality and multi-omics approaches synergize to promise the next generation of disease diagnostics and risk stratification solutions.

Professional Interests

Hypothesis driven, Big data Empowered, Translational Sciences

• The implementation and use of “big data” based medicine and data analysis in complex health care settings for improved cost and care effectiveness
• Mulit-omics (genomics and proteomics focused) analyses of gene/protein biomarkers for the diagnosis and prognosis of the inflammatory entity of Necrotizing Enterocolitis (NEC), sepsis in premature infants, Kawasaki disease, CNS cancer metastasis, and women preganancy preterm diseases.
• Precision medicine – development of novel computational analysis for NGS analysis and NGS based disease diagnostics integrating vast amount of public domain NGS genomic resources
• Clinical sensor – development of novel chemical based sensor technologies to allow urine based diagnostics

All Publications

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