Notice Number: NOT-HL-19-712
Release Date: August 15, 2019
First Available Due Date: October 5, 2019
Expiration Date: January 8, 2022
NOT-HL-19-678: Bold New Bioengineering Research for Heart, Lung, Blood, and Sleep Disorders and Diseases
NOT-HL-19-676: Integrative Omics Analysis of NHLBI TOPMed Data
PA-19-056: NIH Research Project Grant (Parent R01 Clinical Trial Not Allowed)
National Heart, Lung, and Blood Institute (NHLBI)
The purpose of this Notice of Special Interest (NOSI) is to inform applicants to the National Heart, Lung, and Blood Institute (NHLBI) of an area of special interest in the development and utilization of data science methodologies for gaining new insights to improve health in heart, lung, blood or sleep (HLBS) domains .
Vast quantities of structured and unstructured data, from sources such as high-dimensional medical imaging, satellite imagery and spatial data, physiological signals, and smart electronic mobile devices, are currently being generated for biomedical research use. However, numerous challenges exist in the phases of the analytics life cycle – from data wrangling, model building, assessment to deployment. Some specific examples of issues and challenges are: extracting value from raw complex data; handling multicollinearity, missing values, and confounding bias; auto-detecting outliers and noise; improving misclassification and precise analysis; and optimizing predictability on an independent data set. The rise of machine learning (ML) and artificial intelligence (AI) techniques have enabled the advent of relevant solutions to impacting health that help “train” computer systems for auto-tuning and finding insights faster and more accurately. To address new challenges, NIH has articulated specific priorities and encourages rapid and open sharing of scientific efforts in development and dissemination of innovative data analytics approaches (see Goal 3, NIH Strategic Plan For Data Science). The NHLBI has also recognized the need for developing innovative data science approaches to enhance integration, analysis, and interpretation of data from multiple sources to gain insights on the biological, social, environmental, and behavioral determinants associated with HLBS health and disease (see Objective 7, Leverage emerging opportunities in data science to open new frontiers, NHLBI Strategic Vision, NIH Publication 16-HL-6150).
In order to stimulate novel approaches in this field, the NHLBI is seeking applications that will further the novel and broader use of data science methods, and to include team-based approaches in these research efforts. It is hoped that research outputs (i.e., data, metadata, tools, algorithms, program code, tutorials, findings, etc.) could potentially lead to impactful analytic decision-making both at the individual and public health levels. Potential new knowledge to be gained could include: improved understanding on how environmental factors interact with individuals to impact the onset of clinical health/disease; automation in identifying, classifying, or subtyping of a disease or condition; optimization of algorithms for personalized treatment, precision medicine or precision prevention of HLBS diseases or conditions; and generation of novel research hypotheses.
The specific research objectives of this NOSI are to: (1) stimulate the advancement and employment of novel data science methods for gaining novel insights; (2) enable innovative engineering solutions for better and faster data analytics; and (3) communicate research outputs through open-science platforms. The innovative methodologies of data science may focus on, for example, artificial intelligence, machine learning, deep-learning, or combinations of these techniques. The scope of scientific questions must be of relevance to the mission of NHLBI and aligned within the NHLBI’s Strategic Vision, NIH Publication 16-HL-6150. Multiple principle investigators and key personnel in complementary disciplines (for example, biostatistics/statistics, mathematics; computer science, engineering; biology, medicine, environmental science, social science, etc.) are encouraged to jointly participate. Research plans that actively adhere to open-science and FAIR (Findable, Accessible, Interoperable, and Reusable) principles would be of greatest interest to NHLBI. Research outputs may include but need not be limited to open-source, reusable, high-quality metadata; smart tools; algorithms; documentation; supplemental materials; presentations at conferences or publications containing guidelines with examples, tutorials, and source code. Sharing of resources and effective communication of outputs of appropriate interest to broader communities are essential elements of applications.
For topics related to TOPMed computational and systems biology analysis of trans-omics data, please check NOT-HL-19-676.
Application and Submission Information
This notice applies to due dates on or after October 5, 2019, and subsequent receipt dates through January 8, 2022.
Applications in response to this Notice must be submitted through the NIH Parent Announcement PA-19-056: NIH Research Project Grant (Parent R01 Clinical Trial Not Allowed). All instructions in the SF424 (R&R) Application Guide and PA-19-056 must be followed, with the following additions:
IMPORTANT: For funding consideration, applicants must include NOT-HL-19-712 in the Agency Routing Identifier field (Box 4b) of the SF424 (R&R) Form. Applications without this information in Box 4b will not be considered for this initiative.
Applications nonresponsive to the terms of this Notice will be not be considered for this initiative.
Investigators planning to submit an application in response to this NOSI are strongly encouraged to contact and discuss their proposed research/aims with an NHLBI program officer listed on this NOSI well in advance of the grant receipt date.
Please direct all inquiries to the contacts in Section VII of the listed funding opportunity announcements with the following additions/substitutions:
Lucy L. Hsu
National Heart, Lung, and Blood Institute (NHLBI)