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Assistant Professor of Psychiatry and Biomedical Engineeringjacobemail@example.com(319) 335-8066
Jake received his PhD in computational biology from the Technische Universität Dresden in Dresden, Germany. The focus of his doctoral dissertation was the use of machine learning methods, specifically random forests, to predict transcriptome-wide gene expression levels using genotype data (expression quantitative trait loci or eQTL). After completing his doctoral research, he returned to the U.S. and did his postdoctoral research in the lab of Jonathan Sebat at UC San Diego, where he used whole-genome sequencing data from families with autistic children to build models of genome mutability. During this time he also developed machine learning-based tools for the detection of copy number variants (CNVs) in whole-genome sequencing data. Jake joined the faculty at the University of Iowa in the summer of 2013, and his current research interests include leveraging statistical learning approaches while investigating transcriptional regulation in animal models of neurodevelopmental conditions, as well as human genetics studies of neuropsychiatric disorders. He is especially interested in conditions than manifest themselves in childhood, such as autism spectrum disorders (ASDs), attention deficit hyperactivity disorder (ADHD), specific language impairment (SLI), and developmental coordination disorder (DCD).
The Michaelson lab investigates how variation in the genome affects the development and function of the mind. Their experience in genome informatics and statistical learning enables them to develop predictive models of gene-phenotype relationships based on high-throughput biological data sets, including whole genome sequencing, ChIP-seq, and RNA-seq. The aim of these predictive models is both to improve diagnostic capabilities and to further illuminate the biological mechanisms that underlie psychiatric conditions.