Predicting Protein Interactions Using a Deep Learning Method-Stacked Sparse Autoencoder Combined with a Probabilistic Classification Vector Machine

Joint Authors

Jiang, Tonghai
Wang, Yanbin
You, Zhuhong
Li, Liping
Cheng, Li
Zhou, Xi
Zhang, Libo
Li, Xiao

Source

Complexity

Issue

Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-12, 12 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2018-12-10

Country of Publication

Egypt

No. of Pages

12

Main Subjects

Philosophy

Abstract EN

Protein-protein interactions (PPIs), as an important molecular process within cells, are of pivotal importance in the biochemical function of cells.

Although high-throughput experimental techniques have matured, enabling researchers to detect large amounts of PPIs, it has unavoidable disadvantages, such as having a high cost and being time consuming.

Recent studies have demonstrated that PPIs can be efficiently detected by computational methods.

Therefore, in this study, we propose a novel computational method to predict PPIs using only protein sequence information.

This method was developed based on a deep learning algorithm-stacked sparse autoencoder (SSAE) combined with a Legendre moment (LM) feature extraction technique.

Finally, a probabilistic classification vector machine (PCVM) classifier is used to implement PPI prediction.

The proposed method was performed on human, unbalanced-human, H.

pylori, and S.

cerevisiae datasets with 5-fold cross-validation and yielded very high predictive accuracies of 98.58%, 97.71%, 93.76%, and 96.55%, respectively.

To further evaluate the performance of our method, we compare it with the support vector machine- (SVM-) based method.

The experimental results indicate that the PCVM-based method is obviously preferable to the SVM-based method.

Our results have proven that the proposed method is practical, effective, and robust.

American Psychological Association (APA)

Wang, Yanbin& You, Zhuhong& Li, Liping& Cheng, Li& Zhou, Xi& Zhang, Libo…[et al.]. 2018. Predicting Protein Interactions Using a Deep Learning Method-Stacked Sparse Autoencoder Combined with a Probabilistic Classification Vector Machine. Complexity،Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1134043

Modern Language Association (MLA)

Wang, Yanbin…[et al.]. Predicting Protein Interactions Using a Deep Learning Method-Stacked Sparse Autoencoder Combined with a Probabilistic Classification Vector Machine. Complexity No. 2018 (2018), pp.1-12.
https://search.emarefa.net/detail/BIM-1134043

American Medical Association (AMA)

Wang, Yanbin& You, Zhuhong& Li, Liping& Cheng, Li& Zhou, Xi& Zhang, Libo…[et al.]. Predicting Protein Interactions Using a Deep Learning Method-Stacked Sparse Autoencoder Combined with a Probabilistic Classification Vector Machine. Complexity. 2018. Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1134043

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1134043