October 2, 2023
National Heart, Lung, and Blood Institute (NHLBI)
This Request for Information (RFI) seeks input on how Generative Artificial Intelligence (AI) can be employed to enhance the use and integration of big data (e.g., omics data) in clinical measurements and high-throughput omics screening in heart, lung, blood, and sleep (HLBS) research.
Generative AI models learn the patterns and structure of their input training data, and then generate new content that has similar characteristics. The generative AI model uses neural networks to identify the patterns and structures within existing data in order to generate new content, including text, imagery, audio, and synthetic data.
The biological language coded inside human genomes regulates gene expression (e.g., RNAs and proteins) in response to environmental perturbations that can lead to changes in clinical measurements. The question arises: How can generative AI help human experts recognize patterns of dynamic gene-environment interactions underlying health and disease?
NHLBI's TOPMed program has produced 200,000 whole-genome sequences and is on track to amass multi-omics data (including RNA-seq, methylome, metabolome, and proteome) for nearly 270,000 samples. Despite the enormous amount of data generated by high-throughput screening with new technologies and omics platforms, only a fraction of these data are being optimally assessed and incorporated into practice.
NHLBI seeks to develop innovative approaches for the integration, analysis, and interpretation of these multi-dimensional data. The ultimate goal is to deepen our understanding of how these diverse data contribute to the health and disease states of heart, lung, blood, and sleep (HLBS) systems. By achieving this, we can enhance diagnosis, treatment, and prevention strategies, ultimately improving patient outcomes.
Through this RFI, our objective is to understand how Generative AI can help in these endeavors, effectively serving as a tool for integrating and analyzing these vast and complex datasets.
NHLBI is specifically seeking input on how Generative Artificial Intelligence (AI) can be employed to enhance the use and integration of big data (e.g., omics data) in clinical measurements and high-throughput omics screening in HLBS research.
Topics of interest include, but are not limited to, the following:
All comments must be submitted electronically on the submission website.
Responses must be received by 11:59:59 pm (ET) on December 5, 2023.
Responses to this RFI are voluntary and may be submitted anonymously. Please do not include any personally identifiable information or any information that you do not wish to make public. Proprietary, classified, confidential, or sensitive information should not be included in your response. The Government will use the information submitted in response to this RFI at its discretion. The Government reserves the right to use any submitted information on public websites, in reports, in summaries of the state of the science, in any possible resultant solicitation(s), grant(s), or cooperative agreement(s), or in the development of future funding opportunity announcements. This RFI is for informational and planning purposes only and is not a solicitation for applications or an obligation on the part of the Government to provide support for any ideas identified in response to it. Please note that the Government will not pay for the preparation of any information submitted or for use of that information.
We look forward to your input and hope that you will share this RFI opportunity with your colleagues.
NHLBI Gen AI RFI