A support vector machine approach to identification of proteins relevant to learning in a mouse model of Down Syndrome

Tara Eicher, Kaushik Sinha

Abstract:

Down Syndrome is a common disorder which
causes intellectual disability among other symptoms. To date, no
treatment exists for the learning difficulties associated with Down
Syndrome. However, the pharmaceutical drug memantine has
been shown to improve learning ability in a Down Syndrome
model of mice (Ts65Dn) exposed to Context Fear Conditioning
(CFC), an existing technique used in determining the extent of
learning capability of mice. While the effect of memantine on
learning capability in Ts65Dn mice is significant, the biological
mechanism responsible for restoration of learning capability by
memantine is poorly understood. One possible way to
characterize this mechanism is by analyzing the neural protein
profile data of normal and Down Syndrome mice with and
without memantine treatment. In this work, we use a series of
linear support vector machines to model the differential
expression of 77 proteins obtained from the nuclear cortex of
normal and Ts65Dn mice, with and without memantine
treatment and with and without CFC stimulation. We use feature
selection by weight threshold to select those proteins which play a
significant role in characterizing each model. Per our findings,
these subsets of proteins can be used to build more accurate
classification models of the data than those subsets chosen using
unsupervised learning or statistical analyses in previous studies.
We recommend that the subsets of proteins selected using our
proposed method be utilized in further biological study aiming to
understand the effects of memantine on learning restoration.