Multi-task Deep Neural Networks in Protein Function Prediction

Date

2017-05-01

Author

Rifaioğlu, Ahmet Süreyya
Doğan, Tunca
Martin, Maria Jesus
Atalay, Rengül
Atalay, Mehmet Volkan

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In recent years, deep learning algorithms have outperformed the state-of-the art methods in several areas thanks to the efficient methods for training and for preventing overfitting, advancement in computer hardware, the availability of vast amount data. The high performance of multi-task deep neural networks in drug discovery has attracted the attention to deep learning algorithms in bioinformatics area. Here, we proposed a hierarchical multi-task deep neural network architecture based on Gene Ontology (GO) terms as a solution to protein function prediction problem and investigated various aspects of the proposed architecture by performing several experiments. First, we showed that there is a positive correlation between performance of the system and the size of training datasets. Second, we investigated whether the level of GO terms on GO hierarchy related to their performance. We showed that there is no relation between the depth of GO terms on GO hierarchy and their performance. In addition, we included all annotations to the training of a set of GO terms to investigate whether including noisy data to the training datasets change the performance of the system. The results showed that including less reliable annotations in training of deep neural networks increased the performance of the low performed GO terms, significantly. We evaluated the performance of the system using hierarchical evaluation method. Mathews correlation coefficient was calculated as 0.75, 0.49 and 0.63 for molecular function, biological process and cellular component categories, respectively. We showed that deep learning algorithms have a great potential in protein function prediction area. We plan to further improve the DEEPred by including other types of annotations from various biological data sources. We plan to construct DEEPred as an open access online tool.

Subject Keywords

Gene Ontology, Multi-task deep neuralnetworks, Deep Learning, Protein Function Prediction

URI

https://arxiv.org/abs/1705.04802
https://hdl.handle.net/11511/86536
https://arxiv.org/pdf/1705.04802.pdf

Journal

arXiv

Collections

Graduate School of Informatics, Article

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Citation Formats

A. S. Rifaioğlu, T. Doğan, M. J. Martin, R. Atalay, and M. V. Atalay, “Multi-task Deep Neural Networks in Protein Function Prediction,” arXiv, pp. 1–19, 2017, Accessed: 00, 2021. [Online]. Available: https://arxiv.org/abs/1705.04802.