Internship: Protocol development for machine learning for phenotypic cellular analysis “A picture is worth 1000 words” expresses the concept that there is a lot of information within images. Using microscopy images of human cells in culture is a powerful tool for studying cell biogenesis and function. Image features often change when cells are treated with compounds or siRNAs. Variation among image features can be quantitated to better understand the effect of the treatment, but the changes in the features are not always obvious to the eye. Machine learning is an analysis approach to identify and quantify phenotypic differences among experimental cellular populations which can be applied to large imaging data sets to discover distinct patterns among experimental groups. Yale Center for Molecular Discovery is looking for a summer intern to develop our abilities in image quantification and machine learning for existing data sets and software. In this internship, comprehensive analysis of the available machine learning freeware (including CellProfilerAnalyst) will be performed. You will learn and use CellProfilerAnalyst to analyze and quantify image features in image data. Building on that, you will also develop machine learning techniques to make a list of morphology differences between the experimental groups. Finally, time permitting, this list will be used for Principal Component Analysis (PCA), a method which allows identifying the features that show the most variation. All developed protocols and results will be documented for future use by YCMD and Yale faculty. This is a great opportunity for a student focused on computational sciences. Some statistical background is helpful but not required for this internship.