Affiliation:
1. College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
Abstract
Background:
Protein function is closely related to its location within the cell. Determination
of protein subcellular location is helpful in uncovering its functions. However, traditional biological
experiments to determine the subcellular location are of high cost and low efficiency, which
cannot meet today’s needs. In recent years, many computational models have been set up to identify
the subcellular location of proteins. Most models use features derived from protein sequences. Recently,
features extracted from the protein-protein interaction (PPI) network have become popular in
studying various protein-related problems.
Objective:
A novel model with features derived from multiple PPI networks was proposed to predict
protein subcellular location.
Methods:
Protein features were obtained by a newly designed network embedding algorithm,
Mnode2vec, which is a generalized version of the classic Node2vec algorithm. Two classic classification
algorithms: support vector machine and random forest, were employed to build the model.
Results:
Such model provided good performance and was superior to the model with features extracted
by Node2vec. Also, this model outperformed some classic models. Furthermore, Mnode2vec
was found to produce powerful features when the path length was small.
Conclusion:
The proposed model can be a powerful tool to determine protein subcellular location,
and Mnode2vec can efficiently extract informative features from multiple networks.
Publisher
Bentham Science Publishers Ltd.
Subject
Molecular Biology,Biochemistry
Cited by
11 articles.
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