Protein subcellular localization prediction based on profile alignment and Gene Ontology

Wan, Shibiao; Mak, Man‐Wai; Kung, Sun‐Yuan

doi:10.1109/mlsp.2011.6064613

Cited by 5 publications

(1 citation statement)

References 20 publications

(26 reference statements)

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“…The Gene Ontology (GO) is a major bioinformatics initiative for joining all the representations of the genes and their products and unifying them in one dataset. GO dataset contains: (a) cellular components referring to the place in cell where the gene is active; (b) molecular function; the biochemical activity of a gene product, and (c) biological process; the proceedings or collection of the molecular events with a specific beginning and the end (34). In this work, we just used the biological process and molecular function of a gene.…”

Section: Gene Ontology Informationmentioning

confidence: 99%

Prediction of Protein Sub-Mitochondria Locations Using Protein Interaction Networks

Hoseini

Mirzarezaee

2018

ijbiotech

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BackgroundAs a subcellular organelle in the eukaryotic cells, mitochondrion plays an important role in the process of energy generation and energy metabolism (1).It was proved that mitochondria contribute to many complex biological processes like programmed cell death and ionic homeostasis. Many diseases are related to the incorrect mitochondria function; thus the function of proteins in the mitochondria as a vital organelle of the cells is extremely important (1).A mitochondrion, in general, can be divided into four distinct parts; i.e. the outer membrane, inter-membrane space, inner membrane, and the matrix. Proteins in each of these compartments have their own biological role. Understanding protein functions are experimentally very time consuming and costly (2). Therefore, some computational systems were developed for the protein subcellular location prediction (3-15).Background: Prediction of the protein localization is among the most important issues in the bioinformatics that is used for the prediction of the proteins in the cells and organelles such as mitochondria. In this study, several machine learning algorithms are applied for the prediction of the intracellular protein locations. These algorithms use the features extracted from protein sequences. In contrast, protein interactions have been less investigated. Objectives: As protein interactions usually occur in the same or adjacent places, using this feature to find the location would be efficient and impressive. This study did not aim at increasing the total accuracy of the conducted research. The study has focused on the features of the proteins' interaction and their employment which lead to a higher accuracy. Materials and Methods: In this study, we have examined the protein interaction network as one of the features for prediction of the protein localization and its effects on the prediction results. In this regards, we have gathered some of the most common features including Amino Acid Composition, Dipeptide Compositions, Pseudo Amino Acid Compositions (PseAAC), Position Specific Scoring Matrix (PSSM), Functional Domain, Gene Ontology information, and the Pair-wise sequence alignment. The results of the classification are compared to the ones using protein interactions. For achieving this goal different machine learning algorithms were tested. Results: The best-obtained results of using single feature set obtained using SVM classifier for PseAAC feature. The accuracy of combining all features with PPI data, using the Decision Tree and Random Forest classifiers, was 82.49% and 83.35%, respectively. In another experiment, using just protein interaction data with the different cutting points resulted in obtaining an accuracy of 93.035% for the protein location prediction. Conclusion: In total, it was shown that protein(s) interaction has a significant impact on the prediction of the mitochondrial proteins' location. This feature can separately distinguish the locations well. Using this feature the accuracy of the results is raised up to 5%.

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Section: Gene Ontology Informationmentioning

confidence: 99%

Prediction of Protein Sub-Mitochondria Locations Using Protein Interaction Networks

Hoseini

Mirzarezaee

2018

ijbiotech

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An ensemble classifier with random projection for predicting multi-label protein subcellular localization

Wan

Mak

Zhang

et al. 2013

2013 IEEE International Conference on Bioinformatics and Biomedicine

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GOASVM: Protein subcellular localization prediction based on Gene ontology annotation and SVM

Wan

Mak

Kung

2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Protein subcellular localization is an essential step to annotate proteins and to design drugs. This paper proposes a functionaldomain based method-GOASVM-by making full use of Gene Ontology Annotation (GOA) database to predict the subcellular locations of proteins. GOASVM uses the accession number (AC) of a query protein and the accession numbers (ACs) of homologous proteins returned from PSI-BLAST as the query strings to search against the GOA database. The occurrences of a set of predefined GO terms are used to construct the GO vectors for classification by support vector machines (SVMs). The paper investigated two different approaches to constructing the GO vectors. Experimental results suggest that using the ACs of homologous proteins as the query strings can achieve an accuracy of 94.68%, which is significantly higher than all published results based on the same dataset. As a userfriendly web-server, GOASVM is freely accessible to the public at

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Protein subcellular localization prediction based on profile alignment and Gene Ontology

Cited by 5 publications

References 20 publications

Prediction of Protein Sub-Mitochondria Locations Using Protein Interaction Networks

Prediction of Protein Sub-Mitochondria Locations Using Protein Interaction Networks

An ensemble classifier with random projection for predicting multi-label protein subcellular localization

GOASVM: Protein subcellular localization prediction based on Gene ontology annotation and SVM

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