Publications

@inproceedings{Liou2018,

title = {FFANEprot: Predicting Protein Functions using a Weight-sharing Multitask Neural Network Optimized by a Firefly Algorithm with Natural Enemy Strategy},

author = {Yi-Fan Liou and Po-Jung Tsai and Zi-Yu Huang and Po-Chin Chiou and Hsiao-Wei Chu and Li-Ping Ciou and Torbjörn E. M. Nordling},

url = {http://www.incob2018.org/},

year  = {2018},

date = {2018-09-01},

booktitle = {17th International Conference on Bioinformatics (INCoB-2018)},

publisher = {Asia Pacific Bioinformatics Network (APBioNet)},

address = {New Delhi, India},

abstract = {Background: The prediction of multiple functions of several proteins at once from the protein sequence alone is essential, but difficult. To solve this problem, we composed a dataset of 81,267 proteins and 1,169 Gene Ontology (GO) terms of the molecular function (MF) from the Swiss-Prot database, and used weight-sharing and multi-task learning to create FFANEprot. Results: The architecture of FFANEprot was optimised by a Firefly algorithm with a natural enemy strategy (FFANE), i.e. periodic reversals. The training and test Matthews correlation coefficients (accuracies) are 0.52 (98.84%) and 0.49 (98.67%), respectively. When analysing the trained networks, we found many completely inhibitory neurons, which typically have a small kernel size and occupy approximately 30% of the CNNs. Conclusion: FFANEprot can predict GO MF terms with high accuracy from sequence alone. Our FFANEnet source code is available at http://ffanenet.nordlinglab.org.},

keywords = {Convolutional neural network, deep learning, Evolutionary algorithm, Firefly algorithm, Inhibitory neurons, Natural enemy strategy},

pubstate = {published},

tppubtype = {inproceedings}

}