1. Gregory Cooper Professor of Biomedical Informatics Director, Center for Causal Discovery Vice Chair Research, Department of Biomedical Informatics
Research involves the use of probability theory, decision theory, Bayesian statistics, machine learning, and artificial intelligence to address biomedical informatics problems.

2. Causal discovery of biomedical knowledge from big data
Problem: How to discover causal knowledge from big biomedical datasets?
Approach: Improve the efficiency of existing causal discovery algorithms and develop new algorithms. Make them readily available to biomedical scientists and easy to use.
Status: Ongoing applications in numerous biomedical areas, including cancer, lung disease, brain science, opioid epidemic, psychiatry, and infectious disease.
Funding: NIH BD2K U54HG (Cooper, Bahar) – NCE to 8/31/2019
Select
Data
Algorithm
Perform Causal Analysis
Query Models
View Models
Compare Models
Annotate Models
Store Models
Share Models
Causal
Models

3. Discovery of tumor-specific drivers and pathways of cancer
Problem: How to discover the genomic drivers and aberrant pathways in an individual tumor?
Approach: Use an instance-specific Bayesian causal discovery approach that learns a tumor-specific causal model between somatic genomic alterations (SGAs) and differential gene expression levels (DEGs).
Funding: NIH BD2K U54HG (Cooper, Bahar)
Status: Detect the drivers of immune tolerance (and susceptibility) and use them to predict sensitivity to immunotherapy in melanoma.
Funding: UPMC ITTC (Lu, Cooper)
Project lead: Xinghua Lu
tumor-specific
causal analysis
cancer data
(e.g., TCGA)
tumor-specific causal models

4. Prediction of cancer outcomes from a combination of clinical and omic data
Problem: How to accurately predict the clinical outcomes of a patient with cancer?
Approach: Automatically construct higher-level features (e.g., cell signaling pathways) from raw omic and clinical data. Use those features and machine learning to predict clinical outcomes.
Status: Current focus is on predicting recurrence and length of life in breast cancer.
Funding: PA DOH CURE grant (Cooper, Bar-Joseph) – NCE to 5/31/2019

5. Machine-learning-based clinical alerting
Problem: How to detect in real time a wide variety of medical errors from data in the EMR?
Approach: Use machine learning to construct a probabilistic model of usual care. If current care of a patient is highly unusual according to the model, raise an alert.
Status: A prospective evaluation of the method on ICU patient cases is scheduled to begin in September 2018.
Funding: NIH / NIGMS R01GM (Hauskrecht, Clermont, Cooper)
Project lead: Milos Hauskrecht

6. A Learning Electronic Medical Record (LEMR)
Problem: How to develop an EMR system that learns how to highlight the right information at the right time?
Approach: Use machine learning to construct a probabilistic model that predicts which information will be viewed in a given patient context. Use that model that estimate the right information to highlight in any given clinical record.
Status: An evaluation of a prototype LEMR system was recently conducted by Andy King and others.
Funding: NLM R01LM (Visweswaran)
Project lead: Shyam Visweswaran
(This figure is adapted from Andrew King, AMIA 2015)

7. Detection and characterization of disease outbreaks using probabilistic modeling
Problem: How to detect and characterize new outbreaks of infectious disease in the population?
Approach: Link a probabilistic epidemiological model of outbreak disease in the population to probabilistic models of patient disease in emergency departments that are capturing patient data electronically. Apply Bayesian inference.
Status: Current focus is on detecting outbreaks of un-modeled diseases.
Funding: NIH / NLM R01LM011370 (Wagner) –completed; competitive renewal under review
Project lead: Mike Wagner