- Data Sets
- Training Resources
Results for: Training
CPCP 2017 Retreat: Cellular Phenotyping and Drug Discovery Symposium Video
Talk by Scott Wildman
CPCP 2017 Retreat: Improving Docking by Boosting Consensus Scoring Symposium Video
Talk by Spencer Ericksen
CPCP 2017 Retreat: Improving Target-Ligand Activity Predictions by Combining Multiple Docking Scores Symposium Video
Talk by Michael Newton
CPCP 2017 Retreat: Virtual Drug Screening using Neural Networks Symposium Video
Talk by Moayad Alnammi
CPCP 2017 Retreat: MetaSRA - Normalized Sample-Specific Metadata for the Sequence Read Archive Symposium Video
Talk by Matt Bernstein
CPCP 2017 Retreat: Chromatin State and Long-Range Interaction Dynamics in Development and Disease Symposium Video
Talk by Sushmita Roy
CPCP Seminar: Big Data in Behavioral Medicine Seminar Video
Complex chronic diseases are creating a growing burden on society. This burden affects the quality of life for many individuals in addition to the financial burden associated with treatment. Every year a large percentage of deaths in the United States are caused by poor diet, physical inactivity or substance abuse (primarily tobacco). These problems are fundamentally behavioral in nature. In addition, developmental disorders such as autism are diagnosed by qualification of behaviors. Dr. James Rehg talks about the role of Big Data on these types of behavioral health disorders. Dr. Rehg and his coleagues work with the new types of sensors that are becoming increasingly available to measure behavioral patterns. They have developed a number of computational models to improve the analysis of these measurements. These models allow them to make quantitative statements about what types of therapies have the greatest affects on behavior.
CPCP Privacy Symposium 2016: Privacy Preserving Federated Biomedical Data Analysis Symposium Video
Learn about the challenges associated with the technical approaches for utilizing data from multiple sources to build more accurate machine learning algorithms from Dr. Xiaoquian Jiang. We know that having more types of data and data from distributed sources provides a stronger platform for research and discovering with machine learning. To address privacy in this context, Dr. Jiang proposes a privacy-preserving distributed data framework and describes various models implemented to solve the such problems. Dr. Jiang's research group has produced versions of this framework in R and Java as well as an online web-service. All version of this framework are available for other researchers to use for their own analysis.
CPCP Privacy Symposium 2016: Privacy is an Essentially Contested Concept Symposium Video
What does privacy mean in the context of Big Data? Dr. Deirdre Mulligan discusses various definitions of privacy in law, philosophy and computer science. Traditional approaches to privacy in data place most of the responsibility for the control of private information flow on the individual with mechanisms such as consent. This idea, known as informational actualization, has limitations that have been exposed by machine learning on big data. These limitations cause violations of privacy such as uncovering identity of individuals where it has been withheld or unexpected inferences made from data that have been intentionally disclosed. Dr. Mulligan suggests new ways of viewing privacy that evolve as social life and technology change.
CPCP Privacy Symposium 2016: Proving that Programs Do Not Discriminate Symposium Video
As the field of Artificial Intelligence (AI) continues to advance, an increasing number of prediction are made by computer programs about humans. These predictions affect decisions made about humans in a wide variety for areas including decisions about: who should get the job, the bank loan, or early release from prison. As we increasingly rely on AI programs to help make decisions about peoples lives, it becomes vitally important that we are able to ensure the programs we are depending on do not have an unfairly biased against certain groups of people. Dr. Aws Albarghouthi of the University of Wisconsin - Madison Computer Sciences department uses his expertise in programming languages to address this issue of fairness.