National Institute of Health
Investigator Initiated Research in Computational Genomics & Data Science (R01)
This opportunity invites applications for a broad range of research efforts in computational genomics, data science, statistics, and bioinformatics relevant to one or both of basic or clinical genomic science, and broadly applicable to human health and disease. This opportunity supports fundamental genomics research developing innovative analytical methodologies and approaches, early stage development of tools and software, and refinement or hardening of software and tools of high value to the biomedical genomics community.
Research topics appropriate for this FOA include, but are not limited to, development of novel computational, bioinformatics, statistical, or analytical approaches, tools, or software for:
- Interactive analysis and visualization of large genomic data sets.
- Identification or prioritization of disease-causal genetic variants.
- Causal statistical modeling related to genomic research.
- Analysis of single-cell or sub-cellular genomic data both in situ and in dissociated cells.
- Integrating model organism data and information with human data.
- Integrating/ interpreting various genomic data types, including sequence data, functional data, phenotypic data, and clinical data.
- Processing sequence data for sequence assembly, variant detection (SNPs, SVs), imputation, and resolution of haplotypes.
- Development of efficient and scalable algorithms for compute-intensive genomic applications.
- Achieving major cost reductions in genomic data processing and analysis.
- Enabling scalable & cost-effective curation of FAIR metadata for genomic & phenotypic data.
- Enhancing secure sharing and use of genomic data in combination with clinical data.
- Processing or analyzing new genomic data types, or major improvement in processing or analyzing existing genomic data types.
- Rigorous benchmarking of tools, methods, or algorithms for genomics.
- Hardening an existing widely-used genomic data processing pipeline to enable its reproducible implementation by the biomedical research community.
Deadlines: Letter of Intent: Oct. 16, 2018; Application: Nov. 16, 2018
https://grants.nih.gov/grants/guide/pa-files/PAR-18-844.html
Oncology Co-Clinical Imaging Research Resources to Encourage Consensus on Quantitative Imaging Methods & Precision Medicine (U24)
The objective of this opportunity is to invite Cooperative Agreement applications to develop research resources that will encourage a consensus on how Quantitative Imaging (QI) methods are optimized to improve the quality of imaging results for co-clinical trials.
The scientific goals of this opportunity are to: a) perform the appropriate optimization of the pre-clinical quantitative imaging methods, b) implement the optimized methods in the co-clinical trial, and finally c) populate a web-accessible research resource with all the data, methods, workflow documentation, and results collected from the co-clinical investigations.
Co-clinical trials are defined herein as investigations in patients and in parallel (or sequentially) in mouse or human-in-mouse models of cancer that mirror the genetics and biology of the patients' malignancies or pre-cancerous lesions.
The co-clinical trial should include either a) a therapeutic goal, such as the prediction, staging, and/or measurement of tumor response to therapies, or b) a screening and early detection or a cancer risk stratification goal for lethal cancer versus non-lethal disease. Applicants are encouraged to organize multi-disciplinary teams with experience in mouse models research, human investigations, imaging platforms, QI methods, decision support software and informatics to populate the research resource.
Deadline: Nov. 19, 2018
https://grants.nih.gov/grants/guide/pa-files/PAR-18-841.html
Simulation Modeling & Systems Science to Address Health Disparities (R01)
This opportunity is intended to support studies in Simulation Modeling and Systems Science (SMSS), which provides avenues for modeling relevant multiple processes, testing plausible scenarios, understanding the magnitude of intended and unintended consequences of specific interventions, and having the option to adjust and refine simulated intervention designs prior to actual implementation testing in the real world. SMSS approaches have been used to guide interventions in clinical preventive care, disaster planning, and for analyzing national health reform strategies. They have also been used to model potential public health outcomes in cases where it is not feasible to test various intervention strategies on real populations, particularly where interventions may involve factors far upstream from health outcomes, such as societal causes embedded in political, legal, economic and cultural factors.
The importance of using SMSS to address population health has been highlighted in Institute of Medicine (IOM) reports, including: For the Public's Health: The Role of Measurement in Action and Accountability (2011) and Bridging the Evidence Gap in Obesity Prevention: A Framework to Inform Decision Making (2010). Moreover, results from simulation models developed under NCI's Cancer Intervention and Surveillance Modeling Network (CISNET) were used to inform guidelines issued by the U.S. Preventive Services Task Force (e.g., breast cancer screening and colorectal cancer screening). However, SMSS have not been widely adopted in health disparities research to help understand the causes of disparities, guide efficient interventions, and/or inform policy making.
SMSS are also highly relevant to late-stage translation research because they integrate information and evidence from various sources such as epidemiology, clinical guidelines, sociology, behavioral science, psychology, neuroscience, and economics, to formulate complex predictive models. The etiology, pathways, and mechanisms that result in health disparities mimic a complex adaptive system. Models of health disparities seek to illuminate critical elements and intervention points that can tip the system for improved health or provide insights into why health has not improved. Modeling multi-level interventions is important for addressing how the interactions and influences of health determinants function.
Research Objectives
- Foster trans-disciplinary partnerships and collaborations in understanding the etiology and causal pathways of health disparities using SMSS.
- Use SMSS to identify modifiable barriers and cost-effective factors to reduce/ eventually eliminate health disparities.
- Provide evidence-based simulation or prediction of the impact of effective or ineffective health disparities interventions delivered in real-world settings.
- Promote big data harmonization and novel analytic methods in SMSS to address minority health/ health disparities.
Research Methodology
Examples of research methods could include, but are not limited to: System dynamics, Network analysis, Agent-based modeling, Microsimulation, Discrete event analysis, and Markov modeling.
Deadlines: Letter of Intent: Dec. 08, 2018; Application: Jan. 08, 2019
https://grants.nih.gov/grants/guide/pa-files/PAR-18-331.html
National Science Foundation
Future of Work at the Human-Technology Frontier: Advancing Cognitive & Physical Capabilities
The Future of Work at the Human-Technology Frontier (FW-HTF) is one of 10 new and far-sighted Big Ideas for Future Investments announced by NSF in 2016. NSF aims to respond to the challenges and opportunities of the changing landscape of jobs and work by supporting convergent research to: understand and develop the human-technology partnership; design new technologies to augment human performance; illuminate the emerging socio-technological landscape and understand the risks and benefits of new technologies; and foster lifelong and pervasive learning with technology. In order to be nimble and responsive to new opportunities and challenges as they are recognized, focus areas for the FW-HTF solicitation, the centerpiece of the FW-HTF Big Idea, may change from year to year.
This solicitation focuses on advancing cognitive and physical capabilities in the context of human-technology interactions. The solicitation will support two themes: Theme 1 will focus on Foundations for Augmenting Human Cognition and Theme 2 will focus on Embodied Intelligent Cognitive Assistants. In shaping projects responsive to these two themes, PIs consider the importance of understanding, anticipating, and shaping the larger implications at the individual, institutional, corporate, and national levels, including issues arising from the needs or consequences for training and education. In addition, projects should be framed in terms of their focus on the potential contribution toward a) transforming the frontiers of science and technology for human performance augmentation and workplace skill acquisition; b) improving both worker quality of life and employer financial metrics; c) enhancing the economic and social well-being of the country; and d) addressing societal needs through research on learning and instruction in the context of augmentation. Projects must include a Collaboration Plan which outlines the way in which the project will leverage and integrate multiple disciplinary perspectives.
Deadlines: Letter of Intent: April 16, 2019; Application: June 04, 2019
https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=505528
Computing & Communication Foundations (CCF)
Core Programs: This opportunity supports research and education projects that develop new knowledge in four core programs: Algorithmic Foundations (AF), Communications and Information Foundations (CIF), Foundations of Emerging Technologies (FET), and Software and Hardware Foundations (SHF).
The Algorithmic Foundations (AF) program supports potentially transformative projects in the theory of algorithms. Projects should be characterized by algorithmic innovation accompanied by rigorous analysis. Of interest is research on algorithms for problems that are central to computer science and engineering, as well as new techniques for the rigorous analysis of algorithms and computational complexity.
The CIF program supports basic research in communication theory, information theory, and signal processing. Included in the CIF program is the reliable transmission of information in the presence of a variety of resource constraints (e.g., energy, bandwidth, computation, time, and privacy) and channel impairments (e.g., noise, multipath, interference, and eavesdroppers). CIF likewise has a strong interest in the role of signal processing, coding, and information theory in distributed processing systems handling massive amounts of data and impacting the control, operation and robustness of real-time systems and networks, including human-in-the-loop modeling, processing, and learning.
Foundations of Emerging Technologies (FET) is a new program within CCF that aims to enable radical innovations across all areas traditionally supported by CCF, including the theory, algorithms, software, hardware, and architecture of computing and communication systems, through research at the intersection of computing and biological systems, nanoscale science and engineering, quantum information science, and other nascent, yet promising, areas. Interdisciplinary collaborations between computer and information scientists as well as those in various other fields such as biology, chemistry, engineering, mathematics and physics are highly encouraged, with the aim of pursuing foundational breakthroughs in computer and information science.
The SHF program supports fundamental research on formal and semi-formal methods for the specification, development, and verification of software and hardware systems. This includes, but is not limited to, abstraction, compositional, refinement-based, and probabilistic methods for the modeling and validation of systems involving discrete and continuous behavior. The program seeks proposals that enhance the applicability, usability, and efficiency of techniques such as abstract interpretation, model checking, theorem proving, automated decision procedures, and constraint solving. Research topics involving the semantics, logics, verification, and analysis of concurrent systems are in scope. SHF supports foundations, algorithms, and tools for software and hardware synthesis.
Deadline: Nov. 15, 2018
https://www.nsf.gov/publications/pub_summ.jsp?ods_key=nsf18568
Smart and Autonomous Systems
The Smart and Autonomous Systems (S&AS) program focuses on Intelligent Physical Systems (IPS) that are capable of robust, long-term autonomy requiring minimal or no human operator intervention in the face of uncertain, unanticipated, and dynamically changing situations. IPS are systems that combine perception, cognition, communication, and actuation to operate in the physical world. Examples include, but are not limited to, robotic platforms, self-driving vehicles, underwater exploration vehicles, and smart grids.
Most current IPS operate in pre-programmed ways and in a limited variety of contexts. They are largely incapable of handling novel situations, or of even understanding when they are outside their areas of expertise. To achieve robust, long-term autonomy, however, future IPS need to be aware of their capabilities and limitations and to adapt their behaviors to compensate for limitations and/or changing conditions.
To foster such intelligent systems, the S&AS program supports research in four main aspects of IPS: cognizant, taskable, adaptive, and ethical. Cognizant IPS exhibit high-level awareness of their own capabilities and limitations, anticipating potential failures and re-planning accordingly. Taskable IPS can interpret high-level, possibly vague, instructions, planning out and executing concrete actions that are dependent on the particular context in which the system is operating. Adaptive IPS can change their behaviors over time, learning from their own experiences and those of other entities, such as other IPS or humans, and from instruction or observation. Ethical IPS should adhere to a system of societal and legal rules, taking those rules into account when making decisions. Each of these research areas requires the IPS to be knowledge-rich, employing a variety of representation and reasoning mechanisms, such as semantic, probabilistic, commonsense, and meta-reasoning.
Deadline: June 03, 2019
http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=505325
Patient-Centered Outcomes Research Institute (PCORI)
Pragmatic Clinical Studies to Evaluate Patient-Centered Outcomes
For this solicitation, applicants are not required to demonstrate that patients and other stakeholders are already engaged as research team members at the time an application is submitted. However, applicants should outline how patients and other stakeholders will participate as partners in various phases of the proposed research, once awarded. Applicants should describe their plan to form a Study Advisory Committee (SAC) or other appropriate engagement body, to ensure that a broad spectrum of patients and other stakeholders advise and assist the research team with refining the study questions, outcomes, and protocols. For this cycle, the special area of interest is Anxiety Disorders in Children, Adolescents, and/or Young Adults.
Compare the effectiveness of one or more digital applications of cognitive behavioral therapy (CBT) to an appropriate active control (e.g., face-to-face CBT) for treatment of mild-to-moderate anxiety in children, adolescents, and young adults (thru age 25). Digital applications may include computer-based therapy (e.g., delivered via Internet or downloadable software) and CBT smartphone applications that are appropriate for treating anxiety.
PCORI is particularly interested in:
- Comparisons of patient-important treatment outcomes across CBT interventions with differing levels and types of support (e.g., parent, therapist, peer, and school counselor involvement).
- Studies in pediatric and primary-care clinics, community mental health centers, schools, and community social service organizations.
- Broad patient populations (e.g., comorbidities and range of severity) and a spectrum of developmental stages represented by patients ages 5 through 25 years.
- Outcomes to include child and family functioning, symptom response, progression, and acceptability of and adherence to recommended care.
- About 2-3 years of follow-up from baseline/randomization, with a minimum of 12 months. Applicants should make clear the length of the active intervention and whether maintenance or booster sessions are included.
Deadlines: Letter of Intent: Oct. 31, 2018; Application: Feb. 06, 2019
http://www.pcori.org/funding-opportunities/announcement/pragmatic-clinical-studies-Cycle-2-2017
Susan G. Komen
Career Catalyst Research Grants (CCR)
Over the past 10 years, Career Catalyst Research (CCR) Grants have fostered promising breast cancer researchers who are in the early stages of their faculty careers by providing support for up to three years of "protected time" for research career development under the guidance of a Mentor Committee. It is expected that following the successful completion of a CCR Grant, awardees will launch independent research careers, successfully compete for subsequent research project funding, and emerge as key leaders in the fight against breast cancer. The focus area for the FY19 Career Catalyst Research Award is Conquering Metastatic Breast Cancer.
Susan G. Komen is requesting Letters of Intent for the FY19 CCR Grant program that propose outstanding translational research into the understanding, detection, and treatment of metastatic breast cancer which will lead to a reduction in breast cancer deaths by 2026.
Deadlines: Letter of Intent: Aug. 01, 2018; Application: Oct. 17, 2018
https://ww5.komen.org/ResearchGrants/FundingOpportunities.html
Note
All faculty, researchers, and scientists on continuing contracts at IU interested in applying for Department of Defense funding are eligible for assistance by the consulting firm--Cornerstone Government Affairs-arranged by the Vice President for Research. Those interested in securing assistance from Cornerstone must submit a 2 page summary of their research project and a CV or biosketch to the VP for Research Office at vpr@iu.edu. Prior to submission, the IUPUI Office of the Vice Chancellor for Research is offering assistance with the 2 page summaries. For more information, contact Steven Chin schin@iupui.edu.