Measurement of gene expression is an indispensable tool for
understanding biological systems. Analysis of gene expression from modern
genomic sequencing technologies, such as RNA-seq, requires the use of
sophisticated software which typically have a large number of user-settable
parameters that influence how the analysis algorithm performs. Scientists,
biologists, and clinical researchers must often tune these parameters by hand
or through other ad hoc means. The goal of this project is to automate this
process by designing and implementing a framework for automatically learning
high-performing parameters for gene expression analysis software.