بهبود پیش بینی پروتئین های ماتریکس خارج سلولی با استفاده از اطلاعات تکاملی از طریق یک مدل خاکستری و روش نامتقارن زیر نمونه برداری

Extracellular Matrix proteins (ECMP) play vigorous part in performing various biological functions including cell migration, adhesion, proliferation, differentiation. Furthermore, embryonic development, angiogenesis, gene expression, and tumor growth are also regulated by ECMP. In view of this incredible significance, precise and reliable identification of ECMP through computational techniques is highly requisite. Although, previous works made substantial improvement, however, accurately predicting ECMP from primary protein sequence is still at the infant stage due to the rapid growth of proteins samples in online databases. In the current study, a novel sequence-based prediction method called TargetECMP has been proposed, which is based on the evolutionary information extracted via a grey system model. It utilizes asymmetric under-sampling approach for splitting the benchmark dataset into eleven subsets in order to avoid class imbalance problem. Jackknife cross-validation test is performed with support vector machine (SVM) on each subset of data and then ensemble majority voting is utilized to integrate outputs of SVM against each subset. The experimental results achieved by TargetECMP outperformed the existing predictor on both benchmark dataset and independent dataset. Owning to best prediction results provided by TargetECMP, it is demonstrated that the analysis will provide novel insights into basic research, drug discovery and academia in general and function of extracellular matrix proteins in particular.