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International Conference on Intelligent Computing

ICIC 2015: Intelligent Computing Theories and Methodologies pp 379–389Cite as

Sequence-Based Random Projection Ensemble Approach to Identify Hotspot Residues from Whole Protein Sequence

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9226))

Abstract

Hot spot residues of proteins are key to performing specific functions in many biological processes. However the identification of hot spots by experimental methods is costly and time-consuming. Computational method is an alternative to identify hot spots by using sequential and structural information. However, structural information of protein is not always available. In this paper, the issue of identifying hot spots is addressed by using statistically physicochemical properties of amino acids only. Firstly, 34 relatively independent physicochemical properties are extracted from the 544 properties in AAindex1. Since the hot spots data set is extremely imbalanced, the ratio of the number of hot spots to that of non-hot spots is about 1.4 %, the hot spot set and a set of non-hot spot subset with roughly the number of that hot spots forms an initial input matrix. Random projection on the matrix achieves an input to a REPTree classifier. Several random projections and different sets of non-hot spots build an ensemble REPTree system. Experimental results showed that although our method performed worse it is a complement to the experiments on hot spot determination, on the commonly used hot spot benchmark sets.

This work was supported by the National Natural Science Foundation of China (Nos. 61300058, 61271098 and 61472282).

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Author information

Authors and Affiliations

  1. Institute of Health Sciences, Anhui University, Hefei, 230601, Anhui, China

    Peng Chen & ShanShan Hu

  2. School of Electronics and Information Engineering, Tongji University, Shanghai, 804201, China

    Bing Wang

  3. College of Electrical Engineering and Automation, Anhui University, Hefei, 230601, Anhui, China

    Jun Zhang

Authors
  1. Peng Chen
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  2. ShanShan Hu
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  3. Bing Wang
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  4. Jun Zhang
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Corresponding author

Correspondence to Peng Chen .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Liverpool John Moores University, Liverpool, United Kingdom

    Abir Hussain

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© 2015 Springer International Publishing Switzerland

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Chen, P., Hu, S., Wang, B., Zhang, J. (2015). Sequence-Based Random Projection Ensemble Approach to Identify Hotspot Residues from Whole Protein Sequence. In: Huang, DS., Jo, KH., Hussain, A. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9226. Springer, Cham. https://doi.org/10.1007/978-3-319-22186-1_37

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  • DOI: https://doi.org/10.1007/978-3-319-22186-1_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22185-4

  • Online ISBN: 978-3-319-22186-1

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