Filter feature selectors in the development of binary QSAR models

References

• C. Helma, T. Cramer, S. Kramer, and L. De Raedt, Data mining and machine learning techniques for the identification of mutagenicity inducing substructures and structure activity relationships of noncongeneric compounds, J. Chem. Inf. Comput. Sci. 44 (2004), pp. 1402–1411.
   PubMedGoogle Scholar
• G. Cerruela García, N. García-Pedrajas, I. Luque Ruiz, and M.Á. Gómez-Nieto, Molecular activity prediction by means of supervised subspace projection based ensembles of classifiers, SAR QSAR Environ. Res. 29 (2018), pp. 187–212.
   Google Scholar
• M.K. Qasim, Z.Y. Algamal, and H.T.M. Ali, A binary QSAR model for classifying neuraminidase inhibitors of influenza A viruses (H1N1) using the combined minimum redundancy maximum relevancy criterion with the sparse support vector machine, SAR QSAR Environ. Res. 29 (2018), pp. 517–527.
   Google Scholar
• M. Goodarzi, B. Dejaegher, and Y.V. Heyden, Feature selection methods in QSAR studies, J. AOAC Int. 95 (2012), pp. 636–651.
   Google Scholar
• Z.Y. Algamal and M.H. Lee, A new adaptive L1-norm for optimal descriptor selection of high-dimensional QSAR classification model for anti-hepatitis C virus activity of thiourea derivatives, SAR QSAR Environ. Res. 28 (2017), pp. 75–90.
   Google Scholar
• Z.Y. Algamal, M.K. Qasim, and H.T.M. Ali, A QSAR classification model for neuraminidase inhibitors of influenza A viruses (H1N1) based on weighted penalized support vector machine, SAR QSAR Environ. Res. 28 (2017), pp. 415–426.
   Google Scholar
• X. Liu and H. Wang, A discretization algorithm based on a heterogeneity criterion, IEEE Trans. Knowl. Data Eng. 17 (2005), pp. 1166–1173.
   Web of Science ®Google Scholar
• S.-U. Guan, J. Liu, and Y. Qi, An incremental approach to contribution-based feature selection, Int. J. Intell. Syst. 13 (2004), pp. 15–42.
   Google Scholar
• R.K. Sivagaminathan and S. Ramakrishnan, A hybrid approach for feature subset selection using neural networks and ant colony optimization, Expert Syst. Appl. 33 (2007), pp. 49–60.
   Google Scholar
• D. Newby, A.A. Freitas, and T. Ghafourian, Pre-processing feature selection for improved C&RT models for oral absorption, J. Chem Inf. Model. 53 (2013), pp. 2730–2742.
   PubMed Web of Science ®Google Scholar
• Y. Liu, A comparative study on feature selection methods for drug discovery, J. Chem. Inf. Comput. Sci. 44 (2004), pp. 1823–1828.
   PubMedGoogle Scholar
• J. Weston, F. Pérez-Cruz, O. Bousquet, O. Chapelle, A. Elisseeff, and B. Schölkopf, Feature selection and transduction for prediction of molecular bioactivity for drug design, Bioinformatics 19 (2003), pp. 764–771.
   Google Scholar
• M.A. Demel, A.G.K. Janecek, W.N. Gansterer, and G.F. Ecker, Comparison of contemporary feature selection algorithms: Application to the classification of ABC‐transporter substrates, QSAR Comb. Sci. 28 (2009), pp. 1087–1091.
   Web of Science ®Google Scholar
• T.A. Alhaj, M.M. Siraj, A. Zainal, H.T. Elshoush, and F. Elhaj, Feature selection using information gain for improved structural-based alert correlation, PLoS One 11 (2016), p. e0166017.
   Google Scholar
• A.M. Wassermann, B. Nisius, M. Vogt, and J. Bajorath, Identification of descriptors capturing compound class-specific features by mutual information analysis, J. Chem Inf. Model. 50 (2010), pp. 1935–1940.
   PubMed Web of Science ®Google Scholar
• J.W. Godden and J. Bajorath, An information-theoretic approach to descriptor selection for database profiling and QSAR modeling, QSAR Comb. Sci. 22 (2003), pp. 487–497.
   Web of Science ®Google Scholar
• J.R. Vergara and P.A. Estévez, A review of feature selection methods based on mutual information, Neural Comput. Appl. 24 (2014), pp. 175–186.
   Google Scholar
• D.C. Whitley, M.G. Ford, and D.J. Livingstone, Unsupervised forward selection: A method for eliminating redundant variables, J. Chem. Inf. Comput. Sci. 40 (2000), pp. 1160–1168.
   PubMedGoogle Scholar
• D.W. Salt, L. Maccari, M. Botta, and M.G. Ford, Variable selection and specification of robust QSAR models from multicollinear data: Arylpiperazinyl derivatives with affinity and selectivity for α2-adrenoceptors, J. Comput.-Aided Mol. Des. 18 (2004), pp. 495–509.
   Web of Science ®Google Scholar
• I. Kononenko, Estimating attributes: analysis and extensions of RELIEF, European Conference on Machine Learning, Catania, Italy, April 6–8, 1994.
   Google Scholar
• A.S. Reddy, S. Kumar, and R. Garg, Hybrid-genetic algorithm based descriptor optimization and QSAR models for predicting the biological activity of Tipranavir analogs for HIV protease inhibition, J. Mol. Graph. 28 (2010), pp. 852–862.
   Google Scholar
• M.A. Hall, Correlation-based feature selection of discrete and numeric class machine learning, Working Paper, Department of Computer Science, University of Waikato, New Zealand, 2000.
   Google Scholar
• L. Yu and H. Liu, Efficient feature selection via analysis of relevance and redundancy, J. Mach. Learn. Res. 5 (2004), pp. 1205–1224.
   Web of Science ®Google Scholar
• D. Veltri, U. Kamath, and A. Shehu, Improving recognition of antimicrobial peptides and target selectivity through machine learning and genetic programming, IEEE-ACM Trans. Comput. Biol. Bioinform. 14 (2017), pp. 300–313.
   Google Scholar
• P. Mitra, C. Murthy, and S.K. Pal, Unsupervised feature selection using feature similarity, IEEE Trans. Pattern Anal. Mach. Intell. 24 (2002), pp. 301–312.
   Web of Science ®Google Scholar
• Chembench repository. Carolina Exploratory Center for Cheminformatics Research, Chapel Hill, NC, 2016; software available at https://chembench.mml.unc.edu.
   Google Scholar
• S.-Y. Choi, J.H. Shin, C.K. Ryu, K.-Y. Nam, K.T. No, and H.-Y. Park Choo, The development of 3D-QSAR study and recursive partitioning of heterocyclic quinone derivatives with antifungal activity, Bioorg. Med. Chem. 14 (2006), pp. 1608–1617.
   Web of Science ®Google Scholar
• V. Schattel, G. Hinselmann, A. Jahn, A. Zell, and S. Laufer, Modeling and benchmark data set for the inhibition of c-jun N-terminal kinase-3, J. Chem Inf. Model. 51 (2011), pp. 670–679.
   PubMed Web of Science ®Google Scholar
• F. Hammann, C. Suenderhauf, and J. Huwyler, A binary ant colony optimization classifier for molecular activities, J. Chem Inf. Model. 51 (2011), pp. 2690–2696.
   PubMed Web of Science ®Google Scholar
• S. Ekins, R. Pottorf, R.C. Reynolds, A.J. Williams, A.M. Clark, and J.S. Freundlich, Looking back to the future: Predicting in vivo efficacy of small molecules versus Mycobacterium tuberculosis, J. Chem Inf. Model. 54 (2014), pp. 1070–1082.
   PubMed Web of Science ®Google Scholar
• J. Mohr, B. Jain, A. Sutter, A.T. Laak, T. Steger-Hartmann, N. Heinrich, and K. Obermayer, A maximum common subgraph kernel method for predicting the chromosome aberration test, J. Chem Inf. Model. 50 (2010), pp. 1821–1838.
   PubMed Web of Science ®Google Scholar
• C.L. Russom , C.R. Williams, T.W. Stewart, A.E. Swank, and R. Am, DSSTox EPA Fathead Minnow Acute Toxicity Database (EPAFHM): SDF files and documentation, version: EPAFHM_v4b_617_15Feb2008, 2008; software available at www.epa.gov/ncct/dsstox/sdf_epafhm.html.
   Google Scholar
• F. Fontaine, M. Pastor, I. Zamora, and F. Sanz, Anchor−GRIND: Filling the gap between standard 3D QSAR and the GRid-INdependent descriptors, J. Med. Chem. 48 (2005), pp. 2687–2694.
   Web of Science ®Google Scholar
• B. Gaüzère, L. Brun, and D. Villemin, Two new graphs kernels in chemoinformatics, Pattern Recognit. Lett. 33 (2012), pp. 2038–2047.
   Google Scholar
• G. Subramanian, B. Ramsundar, V. Pande, and R.A. Denny, Computational modeling of β-secretase 1 (BACE-1) inhibitors using ligand based approaches, J. Chem Inf. Model. 56 (2016), pp. 1936–1949.
   PubMed Web of Science ®Google Scholar
• I.F. Martins, A.L. Teixeira, L. Pinheiro, and A.O. Falcao, A Bayesian approach to in silico blood–brain barrier penetration modeling, J. Chem Inf. Model. 52 (2012), pp. 1686–1697.
   PubMed Web of Science ®Google Scholar
• K.M. Gayvert, N.S. Madhukar, and O. Elemento, A data-driven approach to predicting successes and failures of clinical trials, Cell Chem. Biol. 23 (2016), pp. 1294–1301.
   Google Scholar
• V. Poongavanam, N. Haider, and G.F. Ecker, Fingerprint-based in silico models for the prediction of P-glycoprotein substrates and inhibitors, Bioorg. Med. Chem. 20 (2012), pp. 5388–5395.
   Web of Science ®Google Scholar
• A. Dalby, J.G. Nourse, W.D. Hounshell, A.K.I. Gushurst, D.L. Grier, B.A. Leland, and J. Laufer, Description of several chemical structure file formats used by computer programs developed at Molecular Design Limited, J. Chem. Inf. Comput. Sci. 32 (1992), pp. 244–255.
   Google Scholar
• Open-Source Cheminformatics Software (RDKit). 2019. Available at https://www.rdkit.org/docs/.
   Google Scholar
• Chemistry Development Kit (CDK). 2019. Available at https://cdk.github.io/.
   Google Scholar
• K. Varmuza, P. Filzmoser, M. Hilchenbach, H. Krüger, and J. Silén, KNN classification — Evaluated by repeated double cross validation: Recognition of minerals relevant for comet dust, Chemom. Intell. Lab. Syst. 138 (2014), pp. 64–71.
   Google Scholar
• H. Ishibuchi and Y. Nojima, Repeated double cross-validation for choosing a single solution in evolutionary multi-objective fuzzy classifier design, Knowledge-Based Syst. 54 (2013), pp. 22–31.
   Google Scholar
• P. Filzmoser, B. Liebmann, and K. Varmuza, Repeated double cross validation, J. Chemom. 23 (2009), pp. 160–171.
   Web of Science ®Google Scholar
• A.P. Bradley, The use of the area under the ROC curve in the evaluation of machine learning algorithms, Pattern Recogn. 30 (1997), pp. 1145–1159.
   Google Scholar
• J.A. Hanley and B.J. McNeil, The meaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology 143 (1982), pp. 29–36.
   Google Scholar
• Q. Song, J. Ni, and G. Wang, A fast clustering-based feature subset selection algorithm for high-dimensional data, IEEE Trans. Knowl. Data Eng. 25 (2013), pp. 1–14.
   Web of Science ®Google Scholar
• X. He, D. Cai, and P. Niyogi, Laplacian score for feature selection, Proceedings of the 18th International Conference on Neural Information Processing Systems, Vancouver, Canada, 2005.
   Google Scholar
• C. Huertas and R. Juárez-Ramírez, Filter feature selection performance comparison in high-dimensional data: A theoretical and empirical analysis of most popular algorithms, 17th International Conference on Information Fusion, Salamanca, Spain, 2014.
   Google Scholar
• H. Almuallim and T.G. Dietterich, Learning with many irrelevant features, AAAI Conference, Anaheim, California, 1991.
   Google Scholar
• H. Almuallim and T. Dietterich, Efficient algorithms for identifying relevant features, 9th Canadian Conference on Artificial Intelligence, Vancouver, Canada, 1992.
   Google Scholar
• Y. Yang and J.O. Pedersen, A comparative study on feature selection in text categorization, ICML, Nashville, Tennessee, 1997.
   Google Scholar
• H. Liu and R. Setiono, Scalable feature selection for large sized databases, Proceedings of the Fourth World Congress on Expert Systems, Mexico City, Mexico, 1998.
   Google Scholar
• T.M. Cover and J.A. Thomas, Elements of Information Theory, John Wiley & Sons, New Jersey, 2012.
   Google Scholar
• M. Dash, Feature selection via set cover, Knowledge and Data Engineering Exchange Workshop, Newport Beach, CA, 1997.
   Google Scholar
• D.S. Johnson, Approximation algorithms for combinatorial problems, J. Comput. Syst. Sci. 9 (1974), pp. 256–278.
   Web of Science ®Google Scholar
• C.-C. Chang, and C.-J. Lin, LIBSVM: A library for support vector machines, ACM Trans. Intell. Syst. Technol. 2 (2011), pp. 1–27.
   Web of Science ®Google Scholar
• J.R. Quinlan, Improved use of continuous attributes in C4.5, J. Artif. Intell. Res. 4 (1996), pp. 77–90.
   Google Scholar
• J. Demšar, Statistical comparisons of classifiers over multiple data sets, J. Mach. Learn. Res. 7 (2006), pp. 1–30.
   Web of Science ®Google Scholar
• R.L. Iman and J.M. Davenport, Approximations of the critical region of the fbietkan statistic, Commun. Stat.-Theory Meth. 9 (1980), pp. 571–595.
   Web of Science ®Google Scholar
• S. Holm, A simple sequentially rejective multiple test procedure, Scand. J. Stat. (1979), pp. 65–70.
   Google Scholar
• P.B. Nemenyi, Distribution-Free Multiple Comparisons. Princeton University, New Jersey, 1963.
   Google Scholar