Deep feature selection for Identification of Essential Proteins of Learning and Memory in Mouse Model of Down Syndrome

Abstract

AbstractDown syndrome is a chromosomal abnormality related to intellectual disabilities that affects 0.1% of live births worldwide. It occurs when an individual has a full or partial extra copy of chromosome 21. This chromosome trisomy results in the overexpression of genes that is believed to be sufficient to interfere normal pathways and normal responses to stimulation, causing learning and memory deficiency. Therefore, by studying these proteins and the disturbance in pathways that are involved in learning and memory, we can consider drugs that would correct the observed perturbations, and therefore assist in enhancing the memory and learning. Here, from genes based on an earlier study that identified 77 proteins differentially expressed in normal and trisomic wild mice exposed to context fear conditioning (CFC), we provide a quantitative protein selection based on different feature selection techniques to select the most important proteins related to learning and memory. These techniques include Fisher score, Chi score, and correlation-based subset. In addition, a deep feature selection is utilized to extract high order proteins using deep neural networks. Three main experiments are carried out:studying the control mice’s response, studying the trisomy mice’s response, and studying the control-trisomy mice’s response. In each experiment, support vector machine classifier is used to assess these selected proteins ability to distinguish between learned and not-learned mice to the fear conditioning event. By applying the deep feature selection, fifteen proteins were selected in control mice, nine in trisomy mice, and seven in control-trisomy mice achieving distinguishing accuracies of 93%, 99%, 84% respectively compared to 74%, 78%, and 71% average accuracies of other selection methods. Some of these proteins have important biological function in learning such as CaNA, NUMb, and NOS.