In this talk, translational informatics methods will be presented for the drug interaction research. They include literature based drug interaction discovery, drug interaction signal data mining from health record databases, and pharmacokinetics model based drug interaction simulations. I will use drug interaction induced myopathy as an example to illustrate how the translational informatics are conducted and its clinical impact.
Comparative effectiveness research can be thought of as evaluating which treatment works best for whom and under what circumstances. While observational studies have become increasingly popular for CER; these studies must be carefully designed and analyzed in a way that controls for self-selection bias, where patients and/or physicians select who receives which treatment.
The explosively growing big biomedical data provides enormous opportunities to revolutionize the current clinical practices as well as the biomedical research if the accompanied challenges of heterogeneity in knowledge discovery on biomedical big data can be addressed with novel informatics technologies. Our team has been working on developing semantic technologies to normalize, integrate, query, and analyze the massive volumes of biomedical data as well as to infer new knowledge based on what is known. The core technologies we are developing are based on ontologies and the Semantic Web.
Non-coding gene regulatory loci are essential to transcription in mammalian cells. As a result, a large variety of experimental and computational strategies have been developed to identify cis-regulatory enhancer sequences. However, in practice, most studies consider enhancer candidates identified by a single method alone. Here we assess the robustness of conclusions based on such a paradigm by comparing enhancer sets identified by different strategies.
Machine learning allows uncovering and relating patterns of interest. This talk will highlight a set of research projects in our group that target mental health applications of machine learning using brain imaging and other phenotypic data. In recent years, we have witnessed an assortment of software and data that enables easy application of machine learning technology.
Causal modeling allows predicting a system’s behavior not only under observation but also under intervention. Computational causal discovery reverse-engineers causal models (networks) from observational data with limited or no interventions. In this work, I will present logic-based causal discovery, a new, versatile approach for learning causal networks from observations and interventions: based on standard causal assumptions, associative patterns in the data that constrain the search space of possible causal models are expressed as a logic formula.
Biomedical informaticians are inter-disciplinarians. This is no more evident than in the role of the CRIO, which requires engagement in and support to dozens of sub-fields across clinical and basic endeavors in the health sciences.
Machine learning is commonly described as a “field of study that gives computers the ability to learn without being explicitly programmed” (Simon, 2013). Despite this common claim, practitioners know that designing effective machine learning pipelines is often a tedious endeavor, and typically requires considerable experience with machine learning algorithms, expert knowledge of the problem domain, and brute force search to accomplish.
Saja Al-Alawneh abstract: Providing radiologists with feedback has been shown to improve their performance in mammography diagnosis. In 1992, the Mammography Quality Standards Act (MQSA) was enacted to improve the quality of mammography using audit and feedback procedures. However, no standard audit and feedback system for radiologists has been installed in the United States. Instead, auditing typically requires human effort to manually correlate radiology and pathology results.