Recent Advances on Prediction of Protein Subcellular Localization

Title:Recent Advances on Prediction of Protein Subcellular Localization

Volume: 12 Issue: 6

Author(s): Yuhua Yao, Huimin Xu, Pingan He and Qi Dai

Affiliation:

Keywords: Gene ontology mode, K-nearest neighbor, multiple-label prediction, PseAAC, sequential evolution mode, support vector machine.

Abstract: Prediction of protein subcellular location is a meaningful task which attracts much attention in recent years. Particularly, the number of new protein sequences yielded by the high-throughput sequencing technology in the post genomic era has increased explosively. Facing such an avalanche of new protein sequences, it is both challenging and indispensable to develop an automated method for fast and accurately annotating the subcellular attributes of uncharacterized proteins. In fact, many efforts have been undertaken to predict the protein subcellular locations in silico during the last two decades. According to the recent studies, we found that there are different forms of PseAAC models for the feature representation of proteins. Based on evolutionary information and gene ontology, many researchers expanded them into different feature representation which is one of the key contents in this review. Another important content is classifier algorithms, and prediction algorithms of multiple sites are emerging. This review will discuss the key steps of protein subcellular location.

Cite this article as:

Yao Yuhua, Xu Huimin, He Pingan and Dai Qi, Recent Advances on Prediction of Protein Subcellular Localization, Mini-Reviews in Organic Chemistry 2015; 12 (6) . https://dx.doi.org/10.2174/1570193X13666151218191932