Autism spectrum disorder is a lifelong neurodevelopmental disorder that is typically diagnosed by 2-3 years of age. Despite the early age of clinical diagnosis, relatively few neuroimaging studies have focused on evaluating the neural basis of autism in very young infants and children. The identification of imaging markers of ASD that precede clinical diagnosis could have great impact in identifying infants at risk for ASD and initiating early interventions.
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.