%0 Journal Article
%T A Survey of Computational Intelligence Techniques in Protein Function Prediction
%A Arvind Kumar Tiwari
%A Rajeev Srivastava
%J International Journal of Proteomics
%D 2014
%I Hindawi Publishing Corporation
%R 10.1155/2014/845479
%X During the past, there was a massive growth of knowledge of unknown proteins with the advancement of high throughput microarray technologies. Protein function prediction is the most challenging problem in bioinformatics. In the past, the homology based approaches were used to predict the protein function, but they failed when a new protein was different from the previous one. Therefore, to alleviate the problems associated with homology based traditional approaches, numerous computational intelligence techniques have been proposed in the recent past. This paper presents a state-of-the-art comprehensive review of various computational intelligence techniques for protein function predictions using sequence, structure, protein-protein interaction network, and gene expression data used in wide areas of applications such as prediction of DNA and RNA binding sites, subcellular localization, enzyme functions, signal peptides, catalytic residues, nuclear/G-protein coupled receptors, membrane proteins, and pathway analysis from gene expression datasets. This paper also summarizes the result obtained by many researchers to solve these problems by using computational intelligence techniques with appropriate datasets to improve the prediction performance. The summary shows that ensemble classifiers and integration of multiple heterogeneous data are useful for protein function prediction. 1. Introduction Protein function prediction is a very important and challenging task in bioinformatics. Protein is the most important molecule in our life. It is responsible for structuring the organs, catalysis of biochemical reaction for metabolism, and maintenance of cellular components. The knowledge of the functionality of a protein is very important to develop new approaches in any biological process. The experiment based protein function prediction required a huge experimental and human effort to analyze a single gene or protein. So to remove this drawback a number of very high throughput experimental procedures have been invented to investigate the methods that are used in function prediction. These procedures have generated a variety of data, such as protein sequences, protein structures, protein interaction network, and gene expression data used in function prediction. There are many databases to maintain these data, such as SWISS-PROT [1], DIP [2], NCBI [3], STRING [4], and PDB [5]. The homology based methods used the structure of a protein and it identifies the protein with most similar structure using structural alignment techniques. The global and local sequence
%U http://www.hindawi.com/journals/ijpro/2014/845479/