The purpose of this FOA is to attract data and computational scientists to propose novel ways to integrate data of different types and scales to allow new types of analysis through big data science approaches. It is expected that the development and application of novel computational, bioinformatics, statistical, and analytical approaches can be leveraged to reveal the effects of the interaction of the HIV virus and drugs of abuse on viral activity, latency, and disease progression, as well as new aspects of addiction biology.
The purpose of this FOA is to attract data and computational scientists to propose novel ways to integrate data from various levels to allow new types of analysis through big data science approaches. Development and application of novel computational, bioinformatics, statistical, and analytical approaches can help to identify the effects of interaction between the HIV virus and drugs of abuse on viral replication, latency, and disease progression, as well as reveal new aspects of addiction biology.
Analyses may involve two or more basic and/or clinical data types or knowledge sources and should address fundamental research questions associated with substance abuse research, as well as develop computational tools (e.g., aggregated datasets, standards, analytic software) facilitating future analyses of substance abuse research data. Primary data may be of multiple types and formats, and available through sources which include, but are not limited to, large databases and repositories of existing data, publicly available information (e.g., Twitter data), images, videos, electronic health record (EHR) data, and free text from published manuscripts. Analysis should include at least 1 type of basic biological data (e.g. imaging, genetic, physiological, molecular, etc. – see below).
This mechanism may be used to generate new data or to analyze data from NIH-funded studies, or data derived from other sources, including genetic, epigenetic, molecular, proteomic, metabolomic, brain-imaging, micro-electrode, behavioral, clinical, social, services, or environmental studies, as well as data generated from electronic health records. Applications can be related to the specific aims of the original data collection but must be distinct. Applicants are encouraged to collaborate with investigators holding private data sets, use innovative statistical strategies to link methodologically comparable datasets, or utilize readily available public use and administrative data. Supported efforts may include the activities necessary to accomplish analyses, such as locating, verifying, and evaluating data sets and preparing them for semantic and computational interoperability. Any interoperable databases, standards, or software produced are recommended to be made open-source and freely available to the research community while adhering to privacy and ethics concerns.
Because the nature of this funding opportunity requires the combined application of deep domain knowledge of drug abuse research with the computational analytics of big data science, proposed projects are recommended to involve a multidisciplinary team that applies an integrative, quantitative, computer analytics approach including quantitatively trained researchers in the field of data science, mathematics, statistics, engineering, computer science, or bioinformatics. Newly-formed collaborations or teams to foster sharing of expertise between the fields of HIV/AIDS, substance use disorders, and data science are encouraged.
Applicants with preliminary data may wish to apply to this R01 FOA. High risk/high payoff projects that lack preliminary data are most appropriate for the companion R21 FOA. For both the R01 and R21, the development of tools or technologies that may significantly improve our ability to understand the effects of the interaction of the HIV virus and drugs of abuse and novel aspects of addiction biology.