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A New Sequential Forward Feature Selection (SFFS) Algorithm for Mining Best Topological and Biological Features to Predict Protein Complexes from Protein–Protein Interaction Networks (PPINs)

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Abstract

Protein–protein interaction plays an important role in the understanding of biological processes in the body. A network of dynamic protein complexes within a cell that regulates most biological processes is known as a protein–protein interaction network (PPIN). Complex prediction from PPINs is a challenging task. Most of the previous computation approaches mine cliques, stars, linear and hybrid structures as complexes from PPINs by considering topological features and fewer of them focus on important biological information contained within protein amino acid sequence. In this study, we have computed a wide variety of topological features and integrate them with biological features computed from protein amino acid sequence such as bag of words, physicochemical and spectral domain features. We propose a new Sequential Forward Feature Selection (SFFS) algorithm, i.e., random forest-based Boruta feature selection for selecting the best features from computed large feature set. Decision tree, linear discriminant analysis and gradient boosting classifiers are used as learners. We have conducted experiments by considering two reference protein complex datasets of yeast, i.e., CYC2008 and MIPS. Human and mouse complex information is taken from CORUM 3.0 dataset. Protein interaction information is extracted from the database of interacting proteins (DIP). Our proposed SFFS, i.e., random forest-based Brouta feature selection in combination with decision trees, linear discriminant analysis and Gradient Boosting Classifiers outperforms other state of art algorithms by achieving precision, recall and F-measure rates, i.e. 94.58%, 94.92% and 94.45% for MIPS, 96.31%, 93.55% and 96.02% for CYC2008, 98.84%, 98.00%, 98.87 % for CORUM humans and 96.60%, 96.70%, 96.32% for CORUM mouse dataset complexes, respectively.

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Authors and Affiliations

  1. School of Professional Advancement, University of Management and Technology, Lahore, Pakistan

    Haseeb Younis

  2. Department of Computer Science, COMSATS University Islamabad, Lahore, Pakistan

    Haseeb Younis, Muhammad Waqas Anwar & Usama Ijaz Bajwa

  3. Department of Computer Science and Engineering, University of Engineering and Technology, Lahore, Pakistan

    Muhammad Usman Ghani Khan

  4. Govt. Girls Post Graduate College No.1 Abbottabad, Abbottabad, Pakistan

    Aisha Sikandar

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  1. Haseeb Younis
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  2. Muhammad Waqas Anwar
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Correspondence to Muhammad Waqas Anwar.

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Younis, H., Anwar, M.W., Khan, M.U.G. et al. A New Sequential Forward Feature Selection (SFFS) Algorithm for Mining Best Topological and Biological Features to Predict Protein Complexes from Protein–Protein Interaction Networks (PPINs). Interdiscip Sci Comput Life Sci 13, 371–388 (2021). https://doi.org/10.1007/s12539-021-00433-8

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