Protein Subcellular Classi�Cation Using Machine Learning Approaches

Subcellular localization of proteins is one of the most signiFicant characteristics of living cells that may reveal plentiful information regarding the working of a cell.Subcellular localization property of proteins plays a key role in understanding numerous functions of proteins.The proteins, located in their respective compartments or localizations, are involved in their relevant cellular processes, which may include cell apoptosis, asymmetric cell division, cell cycle regulation, and spermatic morphogenesis. In fact, cells may not perform their regular operations well in case proteins are not found in their proper subcellular locations. Improper localization of proteins may lead to primary human liver tumors, breast cancer, and Bartter syndrome.Protein sequencing has observed rapid expansion due to the advancement in genomic sequencing technologies.This led the research community to recognize the functionalities of di�erent proteins. In this connection, microscopy imaging is providing protein images well in time with low cost compared to protein sequencing. However, automated systems are required for fast and reliable classification of these protein images. Comprehensive analysis of uorescence microscopy images is required in order to develop e�cient automated systems for accurate localization of various proteins For this purpose, representation of microscopy images with discriminative numerical descriptors has always been a challenge. This thesis focuses on the identification of discriminative feature extraction strategies e�ective for protein subcellular localization, the recognition capability of the prediction systems, and the reduction of classifier bias towards the majority class due to the imbalance present in data.The contributions of this thesis include (1) Analysis of di�erent spatial and transform domain features, (2) Development of a novel idea for GLCM construction in DWT domain, (3) Analysis of SMOTE oversampling in the feature space, (4) Analysis of GLCM in the spatial domain for capturing discriminative information from uorescence microscopy protein images along di�erent orientations, (5) Exploitation of Texton images for their capability of extracting discriminative information along di�erent orientations from uorescence microscopy protein images, (6) Development of the web based prediction systems that can be accessed freely by the academicians and researchers. Extensive simulations are performed in order to assess the e�ciency of the proposed predictions systems in discriminating di�erent subcellular structures from various datasets. xvi