Abstract
With the explosive growth in the number of protein sequences generated in the postgenomic age, research into identifying cytokines from proteins and detecting their biochemical mechanisms becomes increasingly important. Unfortunately, the identification of cytokines from proteins is challenging due to a lack of understanding of the structure space provided by the proteins and the fact that only a small number of cytokines exists in massive proteins. In view of fact that a proteins sequence is conceptually similar to a mapping of words to meaning, n-gram, a type of probabilistic language model, is explored to extract features for proteins. The second challenge focused on in this work is genetic algorithms, a search heuristic that mimics the process of natural selection, that is utilized to develop a classifier for overcoming the protein imbalance problem to generate precise prediction of cytokines in proteins. Experiments carried on imbalanced proteins data set show that our methods outperform traditional algorithms in terms of the prediction ability.
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Xiangxiang Zeng received his BS degree in automation from Hunan University, China in 2005, and his PhD in systems engineering from Huazhong University of Science and Technology, China in 2011. From 2010 to 2011 he spent one year working in the group of natural computing in Seville University, Spain. Currently, he is an assistant professor in the Department of Computer Science, Xiamen University, China. His main research interests include membrane computing, neural computing and automaton theory.
Sisi Yuan is a Master student of the Department of Computer Science at Xiamen University, China. She received her BS degree in software engineering from Hangzhou Dianzi University, China. Her research interests include data mining and bioinformatics.
Xianxian Huang is an undergraduate student of the Department of Computer Science at Xiamen University, China. His main research interests are data mining and bioinformatics.
Quan Zou is an associate professor of computer science at Xiamen University, China. He received his PhD degree from Harbin Institute of Technology, China in 2009. His research is in the areas of bioinformatics, machine learning and parallel computing. Now his focus is on genome assembly, annotation, and functional analysis from next generation sequencing data with parallel computing methods. Several related works have been published in Briefings in Bioinformatics, Bioinformatics, PLOS ONE, and IEEE/ACMTransactions on Computational Biology and Bioinformatics. He serves on many impactful journals and the National Natural Science Foundation of China.
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Zeng, X., Yuan, S., Huang, X. et al. Identification of cytokine via an improved genetic algorithm. Front. Comput. Sci. 9, 643–651 (2015). https://doi.org/10.1007/s11704-014-4089-3
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DOI: https://doi.org/10.1007/s11704-014-4089-3