Apply Data Science in Translational Dental, Oral, and Craniofacial Research

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  1. Goal
  2. Background
  3. Gaps and Opportunities
  4. Impact
  5. Current Portfolio
  6. References

September 2022

Translational Genomics Research Branch
Division of Extramural Research

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Goal

This initiative will encourage grant applications for projects that develop and use data science resources, methods, and tools for dental, oral, and craniofacial (DOC) research, and draw those applications to NIDCR from diverse talents who are currently active in DOC, non-DOC, and disease agnostic data science spaces. A broad range of support will be provided, including for research, research training and career development, product development, and conferences. Overall, the initiative will promote research that develops and uses state of the art data science resources, methods, and tools in biomedical and behavioral DOC research spanning the full translational continuum. In particular, applicants will be strongly encouraged to develop and disseminate standards and tools to make retrospective and prospective DOC data FAIR, use the data to discover disease prevention and treatment targets, and translate discoveries into evidence-based clinical applications.

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Background

Concerted efforts in building data ecosystems, maximizing data re-use in research, and promoting data driven research have built a momentum to further advance data science and apply it to dentistry and medicine. The NIDCR Strategic Plan 2021-2026 prioritizes applying data science to enhance DOC science and oral health. Accordingly, the Data Science Strategy Working Group (DSS-WG) of the National Advisory Dental and Craniofacial Research Council has been convened to assess the landscape and make strategic recommendations on implementation of the data science priority. Taking a far-reaching approach, this initiative will foster a diverse pool of translational DOC data science investigators who will be primed to respond to more specifically focused initiatives anticipated to result from the DSS-WG recommendations.

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Gaps and Opportunities

To date, the potential of data science in the DOC field has not yet been fully realized across the translational continuum. Examples of research opportunities that this initiative is aimed to capture are as follows.

  • Maximizing the reuse of research, health, and clinical data and enabling data-driven research by 1) making existing and new data FAIR, 2) making existing and new data AI/ML/DL-ready; and 3) developing Common Data Elements, ontological systems, natural language processing capabilities, and data analytics for DOC research.
  • Integrating multiple data modalities to further improve diagnostic test efficacy, such as merging RNA sequence-based diagnostics into an AI/ML-powered clinical diagnostic pipeline to complement digital pathology slides-based diagnoses of head and neck cancer to better inform treatment planning.
  • Integrating electronic dental and medical records to promote comprehensive, data-driven research and facilitate better-informed care and treatment planning.
  • Developing deep-phenotyping, data curation, and data analysis web interfaces for clinicians to support clinical decision making. For instance, an interface may have imaging and multi-omics data curation and analysis functions for clinicians to use and inform treatment planning.
  • Recruiting, curating, and analyzing health, ‘omic, behavioral, and social data obtained from multiple populations and age groups to understand oral health disparities and identifying prevention and intervention strategies for oral diseases and conditions including dental caries.
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Impact

Data science resources, tools, and know-how will enhance rigor in research, power evidence-based dentistry and medicine, and streamline clinical procedures.

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Current Portfolio

Analysis of NIDCR’s data science program portfolio showed that recent and current data science FOAs (listed below) and grants focus more on basic science research. As such, opportunities exist for more clinically driven data science initiatives to expedite the realization of the power of data science in developing and delivering health solutions. The analysis also led to the conclusion that NIDCR should continue to collaborate with the NIH Office of Data Science Strategy and the NIH Common Fund, which have supported NIDCR-funded FaceBase, and DOC data re-analysis projects, respectively.

  • PAR-20-045, NIDCR Research Grants for Analyses of Existing Genomics Data (R01)
  • PAR-20-046 and PAR-16-070, NIDCR Small Research Grants for Analyses of Existing Genomics Data (R03)
  • PAR-19-145 and PAR-22-160, NIDCR Small Research Grants for Oral Health Data Analysis and Statistical Methodology Development (R03)
  • NOT-DE-20-006, Notice of Special Interest (NOSI) of NIDCR in Supporting Dental, Oral, and Craniofacial Research Using Bioinformatic, Computational, and Data Science Methods
  • RFA-RM-21-011, Expert-Driven Small Projects to Strengthen Gabriella Miller Kids First Discovery
  • NOT-OD-20-073, Notice of Special Interest (NOSI): Administrative Supplements to Support Enhancement of Software Tools for Open Science
  • NOT-OD-22-069, Notice of Special Interest (NOSI): Support for existing data repositories to align with FAIR and TRUST principles and evaluate usage, utility, and impact
  • NOT-OD-21-094, Notice of Special Interest (NOSI): Administrative Supplements to Support Collaborations to Improve the AI/ML-Readiness of NIH-Supported Data
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References

1.     National Institute of Dental and Craniofacial Research. NIDCR Strategic Plan 2021-2026 (PDF - 7.4MB). 2021.

2.     Wright JT, Herzberg MC. Science for the Next Century: Deep Phenotyping (PDF - 690 KB). J Dent Res. 2021 Jul;100(8):785-789.

3.     Bera K, et al. Artificial Intelligence in Digital Pathology - New Tools for Diagnosis and Precision Oncology (PDF - 1.5 MB). Nat Rev Clin Oncol. 2019 Nov;16(11):703-715.

4.     Chan JW, et al. Artificial Intelligence-Guided Prediction of Dental Doses Before Planning of Radiation Therapy for Oropharyngeal Cancer: Technical Development and Initial Feasibility of Implementation (PDF - 639 KB). Adv Radiat Oncol. 2021 Dec 29;7(2):100886.

5.     Duncan WD, et al. Structuring, Reuse and Analysis of Electronic Dental Data Using the Oral Health and Disease Ontology (PDF - 1.4 MB). J Biomed Semantics. 2020 Aug 20;11(1):8.

6.     Cole SW, et al. Population-Based RNA Profiling in Add Health finds Social Disparities in Inflammatory and Antiviral Gene Regulation to Emerge by Young Adulthood (PDF - 1 MB). Proc Natl Acad Sci USA. 2020 Mar 3;117(9):4601-4608.

7.     Pierron D, et al. Smell and Taste Changes are Early Indicators of the COVID-19 Pandemic and Political Decision Effectiveness (PDF - 1 MB). Nat Commun. 2020 Oct 14;11(1):5152.

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Last Reviewed
December 2024