Warren Kibbe

Overview:

Warren A. Kibbe, PhD, is chief for Translational Biomedical Informatics in the Department of Biostatistics and Bioinformatics and Chief Data Officer for the Duke Cancer Institute. He joined the Duke University School of Medicine in August after serving as the acting deputy director of the National Cancer Institute (NCI) and director of the NCI’s Center for Biomedical Informatics and Information Technology where he oversaw 60 federal employees and more than 600 contractors, and served as an acting Deputy Director for NCI. As an acting Deputy Director, Dr. Kibbe was involved in the myriad of activities that NCI oversees as a research organization, as a convening body for cancer research, and as a major funder of cancer research, funding nearly $4B US annually in cancer research throughout the United States. 

Positions:

Professor in Biostatistics and Bioinformatics

Biostatistics & Bioinformatics
School of Medicine

Chief, Division of Translational Biomedical Informatics

Biostatistics & Bioinformatics
School of Medicine

Chief Data Officer, DCI

Biostatistics & Bioinformatics
School of Medicine

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

Ph.D. 1990

California Institute of Technology

Grants:

IPA NCI/NIH - Warren Kibbe

Administered By
Biostatistics & Bioinformatics
Awarded By
National Cancer Institute
Role
Research Associate
Start Date
End Date

RADx-UP CDCC

Administered By
Duke Clinical Research Institute
Awarded By
National Institutes of Health
Role
Co-Principal Investigator
Start Date
End Date

The Duke FUNCTION Center: Pioneering the comprehensive identification of combinatorial noncoding causes of disease

Administered By
Biostatistics & Bioinformatics
Awarded By
National Institutes of Health
Role
Co-Principal Investigator
Start Date
End Date

Publications:

Cancer Data Science and Computational Medicine.

Authors
Yu, P; Kibbe, W
MLA Citation
Yu, Peter, and Warren Kibbe. “Cancer Data Science and Computational Medicine.Jco Clin Cancer Inform, vol. 5, May 2021, pp. 487–89. Pubmed, doi:10.1200/CCI.21.00006.
URI
https://scholars.duke.edu/individual/pub1481812
PMID
33950710
Source
pubmed
Published In
Jco Clinical Cancer Informatics
Volume
5
Published Date
Start Page
487
End Page
489
DOI
10.1200/CCI.21.00006

The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. MATERIALS AND METHODS: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. RESULTS: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. CONCLUSIONS: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19.
Authors
Haendel, MA; Chute, CG; Bennett, TD; Eichmann, DA; Guinney, J; Kibbe, WA; Payne, PRO; Pfaff, ER; Robinson, PN; Saltz, JH; Spratt, H; Suver, C; Wilbanks, J; Wilcox, AB; Williams, AE; Wu, C; Blacketer, C; Bradford, RL; Cimino, JJ; Clark, M; Colmenares, EW; Francis, PA; Gabriel, D; Graves, A; Hemadri, R; Hong, SS; Hripscak, G; Jiao, D; Klann, JG; Kostka, K; Lee, AM; Lehmann, HP; Lingrey, L; Miller, RT; Morris, M; Murphy, SN; Natarajan, K; Palchuk, MB; Sheikh, U; Solbrig, H; Visweswaran, S; Walden, A; Walters, KM; Weber, GM; Zhang, XT; Zhu, RL; Amor, B; Girvin, AT; Manna, A; Qureshi, N; Kurilla, MG; Michael, SG; Portilla, LM; Rutter, JL; Austin, CP; Gersing, KR; N3C Consortium,
MLA Citation
Haendel, Melissa A., et al. “The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment.J Am Med Inform Assoc, vol. 28, no. 3, Mar. 2021, pp. 427–43. Pubmed, doi:10.1093/jamia/ocaa196.
URI
https://scholars.duke.edu/individual/pub1475490
PMID
32805036
Source
pubmed
Published In
J Am Med Inform Assoc
Volume
28
Published Date
Start Page
427
End Page
443
DOI
10.1093/jamia/ocaa196

Reply: Matters Arising 'Investigating sources of inaccuracy in wearable optical heart rate sensors'.

Authors
Bent, B; Enache, OM; Goldstein, B; Kibbe, W; Dunn, JP
MLA Citation
Bent, Brinnae, et al. “Reply: Matters Arising 'Investigating sources of inaccuracy in wearable optical heart rate sensors'.Npj Digit Med, vol. 4, no. 1, Feb. 2021, p. 39. Pubmed, doi:10.1038/s41746-021-00409-4.
URI
https://scholars.duke.edu/individual/pub1475432
PMID
33637842
Source
pubmed
Published In
Npj Digital Medicine
Volume
4
Published Date
Start Page
39
DOI
10.1038/s41746-021-00409-4

Investigating sources of inaccuracy in wearable optical heart rate sensors.

As wearable technologies are being increasingly used for clinical research and healthcare, it is critical to understand their accuracy and determine how measurement errors may affect research conclusions and impact healthcare decision-making. Accuracy of wearable technologies has been a hotly debated topic in both the research and popular science literature. Currently, wearable technology companies are responsible for assessing and reporting the accuracy of their products, but little information about the evaluation method is made publicly available. Heart rate measurements from wearables are derived from photoplethysmography (PPG), an optical method for measuring changes in blood volume under the skin. Potential inaccuracies in PPG stem from three major areas, includes (1) diverse skin types, (2) motion artifacts, and (3) signal crossover. To date, no study has systematically explored the accuracy of wearables across the full range of skin tones. Here, we explored heart rate and PPG data from consumer- and research-grade wearables under multiple circumstances to test whether and to what extent these inaccuracies exist. We saw no statistically significant difference in accuracy across skin tones, but we saw significant differences between devices, and between activity types, notably, that absolute error during activity was, on average, 30% higher than during rest. Our conclusions indicate that different wearables are all reasonably accurate at resting and prolonged elevated heart rate, but that differences exist between devices in responding to changes in activity. This has implications for researchers, clinicians, and consumers in drawing study conclusions, combining study results, and making health-related decisions using these devices.
Authors
Bent, B; Goldstein, BA; Kibbe, WA; Dunn, JP
MLA Citation
Bent, Brinnae, et al. “Investigating sources of inaccuracy in wearable optical heart rate sensors.Npj Digit Med, vol. 3, no. 1, Feb. 2020, p. 18. Pubmed, doi:10.1038/s41746-020-0226-6.
URI
https://scholars.duke.edu/individual/pub1473739
PMID
33558645
Source
pubmed
Published In
Npj Digital Medicine
Volume
3
Published Date
Start Page
18
DOI
10.1038/s41746-020-0226-6

The YPT protein family in yeast

GTP-binding proteins of the Ypt family are members of the ras superfamily of proteins. The first example of a YPT gene, YPT1, was cloned and sequenced as part of the actin-β-tubulin gene cluster in the yeast Saccharomyces cerevisiae and the homology of the Ypt1 protein (Ypt1p) with ras proteins was immediately noted. 1 The subsequent identification by cDNA cloning of mammalian proteins that are strikingly similar in primary structure to the yeast Ypt1p 2, 3 suggested the existence of a larger family of evolutionarily conserved proteins distinct from ras gene products. In fact, multiple members of the Ypt family have been found in the evolutionarily distant yeasts S. cerevisiae and Schizosaccharomyces pombe. 1, 4 - 7 The existence in mammals of 20 or more Ypt-related proteins, designated Rab, 2, 3, 8 - 10 signifies the importance of this still-growing family.
Authors
Kibbe, WA; Hengst, L; Gallwitz, D
MLA Citation
Kibbe, W. A., et al. “The YPT protein family in yeast.” The Ras Superfamily of GTPases, 2017, pp. 367–85. Scopus, doi:10.1201/9780203709931.
URI
https://scholars.duke.edu/individual/pub1451313
Source
scopus
Published Date
Start Page
367
End Page
385
DOI
10.1201/9780203709931

Research Areas:

Bioinformatics
Clinical medicine--Research
Informatics
Medical Informatics
Software
Software Design
Software Validation