The Accrual of patients to Clinical Trials (ACT) network is a nationwide network of sites that share EHR data to significantly increase participant accrual to the nation’s highest priority clinical trials. It is funded by NCATS' Clinical and Translational Science Awards (CTSA) program that supports efforts to solve system-wide translational research problems to improve the success of U.S. clinical trials. The ACT network is built on existing platforms (i2b2/SHRINE) to create a federated network with common standards, data terminology and shared resources. The ACT investigators are focused on 1) data harmonization across EHR platforms, 2) technical needs assessment and implementation, 3) regulatory approaches to ensure compliance with protocols for data access and participant contact, and 4) governance development to establish proper agreements among institutions. More information is available at NCATS. This work is funded by grant UL1 TR001857-01S1 from...
Centers, Labs and Projects
Accrual of patients to Clinical Trials (ACT) network
ACT Biomedicine Lab
Madhavi Ganapathiraju's ACT (Algorithms for Computational and Translational) Biomedicine Lab focuses on applying machine learning and signal processing algorithms for Computational Systems Biology. Specifically, the team is interested in discovering protein-protein interactions. They also work on predicting protein function and cellular localization. Core areas of specialization of students working in this group are machine learning and/or signal processing, and they come from the Department of Biomedical Informatics Training Program, the Intelligent Systems Program, the Joint CMU-Pitt PhD Program in Computational Biology, or internships through the TECBio Research Experiences for Undergraduates Program (www.tecbioreu.pitt.edu) or First Experiences in Research Program, at University of Pittsburgh.
All of Us Research Program (Precision Medicine Initiative Cohort Program)
The University of Pittsburgh is funded as one of the Healthcare Provider Organizations for the national All of Us Research Program, which is a historic effort to gather data from 1 million people living in the United States. The goal of the program is to revolutionize how disease is prevented and treated based on individual differences in lifestyle, environment and genetics. The University of Pittsburgh's program, called the All of Us Pennsylvania Research Program, will enroll 150,000 participants. This work is funded by grant UG3 OD023153 from the Office of the Director, NIH.
Biomedical Informatics Core, CTSI
The Biomedical Informatics Core of the Clinical and Translational Science Institute (CTSI) will establish a research data warehouse; develop and deploy user-friendly, web-based informatics tools such as a Cohort Discovery Tool, a Computable Phenotype Library, and a Data Transfer Tool; and develop and deploy a secure data analytic environment. This work is funded by grant UL1 TR001857 from NCATS, NIH.
Center for Causal Discovery
As an inaugural member of the NIH Big Data to Knowledge (BD2K) Consortium, the Center for Causal Discovery (CCD) will: Develop highly efficient causal discovery algorithms that can be practically applied to very large biomedical datasets Conduct projects addressing 3 distinct biomedical questions (cancer driver mutations, lung fibrosis, brain causome) as a vehicle for algorithm development and optimization Disseminate causal discovery algorithms, software, and tools Train data scientists and biomedical investigators in the use of CCD tools Train data scientists and biomedical investigators to collaborate in the discovery of causality Led by Drs. Gregory Cooper, Ivet Bahar, and Clark Glymour, the Center represents a partnership among data scientists from the University of Pittsburgh (Pitt), Carnegie Mellon University (CMU), and the Pittsburgh Supercomputing Center (PSC) who will develop the algorithms, software, and system architecture needed...
Early, reliable detection of outbreaks of disease, whether natural (e.g., West Nile virus) or bioterrorist-induced (e.g., anthrax and smallpox), is a critical problem today. It is important to detect outbreaks early in order to provide the best possible medical response and treatment, as well as to improve the chances of identifying the source. A primary goal of this project has been to develop new Bayesian models and inference algorithms that then are applied to monitor electronically available healthcare data to achieve early, reliable detection of outbreaks. The scientific challenge of monitoring for outbreaks within an entire population creates major computational challenges in building and applying Bayesian models that are orders of magnitude larger than those developed previously. The project applied and extended state-of-the-art probabilistic inference methods to achieve efficient inference. If inference indicates that an outbreak is likely, an alert is raised automatically...
National Mesothelioma Virtual Bank (NMVB)
The National Mesothelioma Virtual Bank (NMVB) is a virtual biospecimen registry designed to support and facilitate basic science, clinical, and translational research that will advance understanding of mesothelioma pathophysiology with the goal of expediting the discovery of preventive measures, novel therapeutic interventions, and ultimately, cures for mesothelioma. The current participants in the virtual bank are University of Pittsburgh, University of Pennsylvania, NYU Langone Medical Center, Roswell Park Cancer Institute and University of Maryland. This work is funded by grant U24 OH010873 from NIOSH, NIH.
Neptune is a research data warehouse that has been established to provide electronic medical record data for research data marts and for clinical and translational research. Neptune consists of four major components that include 1) an operational data store, 2) an atomic data store, 3) a map store, and 4) an integrated view. This work is funded by grant UL1 TR001857 from NCATS, NIH.
PaTH Clinical Data Research Network
PaTH is a Patient Centered Outcomes Research Institute (PCORI) Clinical Data Research Network project which is focused on building a Learning Health System (LHS) for the Mid-Atlantic region. It is comprised of Geisinger Health System, Johns Hopkins University, Johns Hopkins Health System, Penn State College of Medicine, Penn State Milton S. Hershey Medical Center, Temple Health System, Lewis Katz School of Medicine at Temple University, the University of Pittsburgh, UPMC, the University of Utah, and University of Utah Health Care. the University of Pittsburgh leads the informatics component of PaTH. This work is funded by grant CDRN 1306-04912 from PCORI.
The PRoBE Lab Mission and Goals: To harness prior knowledge for effective knowledge discovery from biomedical data. To design and develop novel machine learning algorithms using symbolic, probabilistic and hybrid approaches to solve bioinformatics problems of clinical importance such as biomarker discovery and disease classification. To develop complex pattern recognition tools that can be plugged into computer-aided diagnostic systems to facilitate evidence combination from heterogeneous sources such as data from imaging, de-identified clinical information and biochemical profiling.