This talk describes an instance-specific causal Bayesian network (CBN) learning method that searches the space of CBNs to build a causal model that is specific to an instance (e.g., a patient). The search is guided by attributes of the given instance (e.g., patient symptoms, signs, lab results, and genotype). We describe the results of applying the method to molecular cancer data to estimate the gene alterations (e.g., gene mutations) that are driving the cancerous behavior of individual tumors, which are the instances in this application.
Asthma is a major cause of healthcare costs in children, particularly in the high-risk subgroup, Puerto Ricans. We aim to identify susceptibility genes for asthma in Puerto Ricans. We conducted GWAS and EWAS of asthma. We also conducted mQTL, eQTL and eQTM analyses to test for association between the top SNPs and DNA methylation and gene expression in nasal epithelium. We identified multiple SNPs, CpG sites and expressed genes associated with asthma.
I plan to make the argument that we should use behavioral science principles when building quality improvement tools for physicians. I will discuss my research on the problem of trauma triage (an archetypal time-sensitive problem that occurs under conditions of uncertainty), with a brief digression to provide a primer on behavioral science, and sharing our efforts to use theoretically-based video games to recalibrate physician heuristics (intuitive judgments) in trauma.
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The process of matching pathway-targeted drugs to tumor mutational profile regardless of cancer type is critical in the development of targeted therapies. However, actionable mutations interact with distinct gene regulatory programs and signaling networks and can occur against different tumor-specific genetic backgrounds.
The aims of the project were to: (1) Evaluate the effect of pharmacist-led medication management service using patient-centered telemedicine on adverse drug events (ADE) for residents receiving high-risk drugs, and (2) Evaluate patient reported outcomes.
Genomics studies revealed numerous antibiotics-encoding genes across a wide range of bacterial and fungal species, including various species in the human microbiome. However, little is known about the hundreds of antibiotics produced by microorganisms in the human, soil and other host-oriented/environmental microbiomes. Deep exploration of this meta-antibiome critically depends on a transition from the current one-off process of antibiotics analysis to a high-throughput antibiotics sequencing.
This talk will provide a glimpse into the emerging science of human profiling from voice. The human voice is a powerful bio-parametric indicator. It carries information that can be linked to the current (referring to the time of recording of the voice) physical, physiological, demographic, medical, environmental and myriad other bio-relevant characteristics of the speaker.