Abstract
A challenging Pattern Recognition problem in Bioinformatics concerns the detection of a functional relation between two proteins even when they show very low sequence similarity – this is the so-called Protein Remote Homology Detection (PRHD) problem. In this paper we propose a novel approach to PRHD, which casts the problem into a Multiple Instance Learning (MIL) framework, which seems very suitable for this context. Experiments on a standard benchmark show very competitive performances, also in comparison with alternative discriminative methods.
M. Bicego and P. Lovato were partially supported by the University of Verona through the program “Bando di Ateneo per la Ricerca di Base 2015”.
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Notes
- 1.
Along the text we will refer equivalently to K-mers or N-grams.
- 2.
Available at http://noble.gs.washington.edu/proj/svm-pairwise/.
- 3.
Available at http://bioinformatics.hitsz.edu.cn/main/~binliu/remote.
- 4.
Downloadable from http://www.chibi.ubc.ca/gist/ [14].
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Mensi, A., Bicego, M., Lovato, P., Loog, M., Tax, D.M.J. (2018). Protein Remote Homology Detection Using Dissimilarity-Based Multiple Instance Learning. In: Bai, X., Hancock, E., Ho, T., Wilson, R., Biggio, B., Robles-Kelly, A. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2018. Lecture Notes in Computer Science(), vol 11004. Springer, Cham. https://doi.org/10.1007/978-3-319-97785-0_12
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