Caused by an extra copy of the human chromosome21 (Hsa21), Down syndrome produces an intellectual disability that is still unknown and requires further research in order to have a better perception. One research conducted in this area of study has analysed different protein levels of the Ts65Dn mouse model of DS. Many researchers are trying to find the critical proteins that categorize the mice classes accurately by using machine learning. In this study, we expand the problem by trying to find the critical proteins that affect different types of learning. The protein subsets are found using forward feature selection method, ReliefF respectively and four different supervised learning algorithms are used. The experimental results are compared with previous related work, and demonstrated that the proposed method outperforms, or is comparable to, its competitors in term of accuracy. Then, a thorough analysis is done to identify the critical proteins for each learning case, by lowering the number to 9 critical proteins that can help in a better categorization of the mice. We hope that our work withhelp the scientists on their further research on finding a treatment that may help the learning process and ease the intellectual disability caused by Down Syndrome.