Biomedical Data Mining for Information Retrieval. Группа авторов

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      104. Patlewicz, G., Jeliazkova, N., Safford, R.J., Worth, A.P., Aleksiev, B., An evaluation of the implementation of the Cramer classification scheme in the Toxtree software. SAR QSAR Environ. Res., 19, 5–6, 495–524, 2008.

      105. Agrahari, R., Foroushani, A., Docking, T.R. et al., Applications of Bayesian network models in predicting types of hematological malignancies. Sci. Rep., 8, 6951, 2018, https://doi.org/10.1038/s41598-018-24758-5.

      106. Ahmed, A., Abdo, A., Salim, N., Ligand-based virtual screening using Bayesian inference network and reweighted fragments. Sci. World J., Drug Discovery Today, 01 Jun 2002, 7(11):597–598, 410914, 2012, https://doi.org/10.1016/s1359-6446(02)02316-4.

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      108. Hinselmann, G., Rosenbaum, L., Jahn, A., Fechner, N., Ostermann, C., and Zell, A., Large-scale learning of structure–activity relationships using a linear support vector machine and problem-specific metrics. J. Chem. Inf. Model., 51, 2, 203–213, 2011.

      109. Mahé, P. and Vert, J., Graph kernels based on tree patterns for molecules. Mach. Learn., 75, 3–35, 2009, https://doi.org/10.1007/s10994-008-5086-2.

      110. Byvatov, E., Fechner, U., Sadowski, J., Schneider, G., Comparison of support vector machine and artificial neural network systems for drug/non-drug classification. J. Chem. Inf. Comput. Sci., 43, 6, 1882–1889, 2003, https://doi.org/10.1021/ci0341161.

      111. Sakiyama, Y., Yuki, H., Moriya, T. et al., Predicting human liver microsomal stability with machine learning techniques. J. Mol. Graph. Model., 26, 6, 907–915, 2008.

112. Wang, C. and Zhang, Y., Improving scoring-docking-screening powers of protein–ligand scoring functions using random forest. J. Comput. Chem., 38, 3, 169–177, 2017.

      113. Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., Blaschke, T., The rise of deep learning in drug discovery. Drug Discovery Today, 23, 6, 1241–1250, 2018.

      114. Marini, F., Roncaglioni, A., Novic, M., Variable selection and interpretation in structure-affinity correlation modeling of estrogen receptor binders. J. Chem. Inf. Model., 45, 6, 1507–1519, 2005.

      115. Kazius, J., Nijssen, S., Kok, J.N., Bäck, T., IJzerman, A.P., Substructure Mining Using Elaborate Chemical Representation. J. Chem. Inf. Model., 46, 2, 597– 605, 2006.

      116. Raschka, S., Scott, A.M., Huertas, M., Li, W., Kuhn, L.A., Automated Inference of Chemical Discriminants of Biological Activity. Methods Mol. Biol., 1762, 307–338, 2018.

      117. Ramraj, T. and Prabhakar, R., Frequent Subgraph Mining Algorithms—A Survey. Proc. Comput. Sci., 47, 197–204, 2015, https://doi.org/10.1016/j.procs.2015.03.198.

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      1 *Corresponding author: [email protected]