Shamim Ahmad

TL;DR: This work has built two models for the classification purpose, one is based on Support Vector Machines (SVM) and the other is Random Forests (RF), and Experimental results show that either classifier is effective.

TL;DR: Results show that the Random Forest based proposed approach can select most important and relevant features useful for classification, which reduces not only the number of input features and time but also increases the classification accuracy.

TL;DR: This study aimed to develop an efficient multi-label protein subcellular localization prediction system, named as MKLoc, by introducing multiple kernel learning (MKL) based SVM, and results indicate that MKLoc not only achieves higher accuracy than a single kernel based S VM system but also shows significantly better results than those obtained from other top systems.

TL;DR: A novel computational tool termed predCar-Site has been developed to predict protein carbonylation sites by incorporating the sequence-coupled information into the general pseudo amino acid composition and constructing a predictor using support vector machine as classifier.

TL;DR: Gen association and diseasome networks identified significant gene ontology and molecular pathways that could enhance the understanding of the mechanisms of AD progression by suggesting new therapeutic approaches to affect the development of AD.