Urbanowicz Lab

Publications

​​​​​​2020

• Zhang, R.F., Urbanowicz, R.J. A Scikit-learn Compatible Learning Classifier System. Proceedings of the Genetic and Evolutionary Computing Conference. ACM Press, pp. 1816-1823. (2020)
• Verma, S., Borole, P., Urbanowicz, R.J. Evolving genetic programming trees in a rule-based learning framework. Proceedings of the Genetic and Evolutionary Computing Conference. ACM Press, pp. 233-234. (2020)
• ​​​​​​Liu, Y., Huang, J., Urbanowicz, R.J., Chen, K., Manduchi, E., Greene, C.S., Moore, J.H., Scheet, P. and Chen, Y., Embracing study heterogeneity for finding genetic interactions in large‐scale research consortia. Genetic epidemiology, 44(1), pp.52-66. (2020)
• Sipper, M., Moore J.H., Urbanowicz, R.J. New Pathways in Coevolutionary Computation. Genetic Programming Theory and Practice XVII. Springer, pp. 295-305 (2020)
• [ABSTACT] J. Minhas, D. Appleby, R. McClelland, J. Holmes, R. Urbanowicz, C. Burwell, C. L. Archer-Chicko, S. Pugliese, J. A. Mazurek, A.Smith, J. S. Fritz, H. Palevsky, S. Kawut , N. Al-Naamani. Obesity and its relationship with exercise capacity and hemodynamics in pulmonary arterial hypertension. In A56. LEFT HEART DISEASE, METABOLISM AND OBESITY IN PULMONARY HYPERTENSION: CLINICAL STUDIES, pp. A2080-A2080. American Thoracic Society, (2020)

​​​​​​2019​​​​​​

• Moore, J.H., Boland M.R., Camara, P.G., Chervitz, H., Gonzalez, G., Himes, B.B., Kim, D., Mowery D.L., Ritchie, M.D., Shen, L., Urbanowicz, R.J., Holmes, J.H. Preparing next-generation scientists for biomedical big data: Artificial intelligence approaches. Personalized Medicine (0) (2019)
• Sipper, M., Moore J.H., Urbanowicz, R.J. Solution and Fitness Evolution (SAFE): Coevolving Solutions and Their Objective Functions. European Conference on Genetic Programming. Springer. 146-161 (2019)
• Lo, Y., Lynch, S.F., Urbanowicz, R.J., Olson, R.S., Ritter, A.Z., Whitehouse, C.R., O'Connor, M., Keim, S.K., McDonald, M., Moore, J.H. and Bowles, K.H., Using Machine Learning on Home Health Care Assessments to Predict Fall Risk. Studies in health technology and informatics, 264, pp.684-688. (2019)
• Sipper, M., Moore J.H., Urbanowicz, R.J. Solution and Fitness Evolution (SAFE): A Study of Multiobjective Problems. IEEE Congress on Evolutionary Computation (CEC) 1868-1874, (2019)
• [ABSTRACT] Mazzotti, D.R., Keenan, B.T., Urbanowicz, R. and Pack, A.I., 0832 Evaluating Supervised Machine Learning Models for Cardiovascular Disease Prediction Using Conventional Risk Factors, Apnea-Hypopnea Index and Epworth Sleepiness Scale. Sleep, 42(Supplement_1), pp.A334-A334. (2019)
• [ABSTRACT] Lo, Y., Lynch, S., Urbanowicz, R.J., Olson, R.S., Ritter, A.Z., Whitehouse, C.R., Connor, M.O., Keim, S.K., McDonald, M., Moore, J.H., Bowles, K.H. Using Machine Learning on Home Health Care Assessments to Predict Fall Risk. Studies in health technology and informatics, 264, 684-688, (2019)
• [ABSTRACT] Wojcieszynski, A., La Cava, W., Urbanowicz R.J., Ying, X., Metz, J., Lin, A., Lukens, J., Fotouhi Ghiam, A.,Swisher-McClure, S., Moore, J.M., Baumann, B. Machine learning to predict toxicity in head and neck cancer patients treated with definitive chemoradiation. American Society for Radiation Oncology (ASTRO) (2019)

2018

• Sipper, Moshe, Ryan J. Urbanowicz, and Jason H. Moore. "To know the objective is not (necessarily) to know the objective function." BioData Mining. (2018): 21.
• Urbanowicz, Ryan J., Melissa Meeker, William La Cava, Randal S. Olson, and Jason H. Moore. "Relief-based feature selection: introduction and review." Journal of biomedical informatics (2018). (In Press)
• Urbanowicz, Ryan J., Randal S. Olson, Peter Schmitt, Melissa Meeker, and Jason H. Moore. "Benchmarking relief-based feature selection methods for bioinformatics data mining." Journal of biomedical informatics 85 (2018): 168-188.
• Urbanowicz, Ryan J., Christopher Lo, John H. Holmes, and Jason H. Moore. "Attribute tracking: strategies towards improved detection and characterization of complex associations." In Proceedings of the Genetic and Evolutionary Computation Conference, pp. 553-560. ACM, 2018.
• Urbanowicz, Ryan J., Ben Yang, and Jason H. Moore. "Problem Driven Machine Learning by Co-evolving Genetic Programming Trees and Rules in a Learning Classifier System." In Genetic Programming Theory and Practice XV, pp. 55-71. Springer, Cham, 2018.
• Verma, S.S., Lucas, A., Zhang, X., Veturi, Y., Dudek, S., Li, B., Li, R., Urbanowicz, R., Moore, J.H., Kim, D. and Ritchie, M.D., 2018. Collective feature selection to identify crucial epistatic variants. BioData mining, 11(1), p.5.
• Le, Trang T., Ryan J. Urbanowicz, Jason H. Moore, and Brett A. McKinney. "Statistical Inference Relief (STIR) feature selection." bioRxiv (2018): 359224.
• Cole, B.S., Hall, M.S., Urbanowicz, R.J., Gilbert-Diamond, D., Moore, J.H. Analysis of Gene-Gene Interactions. Current Protocols of Human Genetics (2018)

2017

• Olson, Randal S., William La Cava, Patryk Orzechowski, Ryan J. Urbanowicz, and Jason H. Moore. "PMLB: a large benchmark suite for machine learning evaluation and comparison." BioData mining 10, no. 1 (2017): 36.
• Olson, Randal S., Moshe Sipper, William La Cava, Sharon Tartarone, Steven Vitale, Weixuan Fu, Patryk Orzechowski, Ryan J. Urbanowicz, John H. Holmes, and Jason H. Moore. "A system for accessible artificial intelligence." arXiv preprint arXiv:1705.00594 (2017).
• Urbanowicz, R.J., Browne, W. Introduction to learning classifier systems. Springer, New York, NY (2017)

2016

• Olson, R.S, Urbanowicz, R.J., Moore, J.H., Evaluation of a tree-based pipeline optimization tool for automating data science. Proceedings of the Genetic and Evolutionary Computing Conference. ACM Press, 485-492 (2016).
• Urbanowicz, R.J., Olson, R.S, Moore, J.H., Pareto inspired multi-objective rule fitness for noise-adaptive rule-based machine learning. Springer Lecture Notes in Computer Science 9921, 514-524 (2016).
• Olson, R.S, Urbanowicz, R.J., Moore, J.H., Automating biomedical data science through tree-based pipeline optimization. Springer Lecture Notes in Computer Science 9597, 123-137 (2016).

2015

• Urbanowicz, R.J., Moore, J.H., Retooling fitness for noisy problems in a supervised Michigan-style learning classifier system. Proceedings of the Genetic and Evolutionary Computing Conference. ACM Press, 591-598 (2015).
• Urbanowicz, R.J., Ramanand, N., Moore, J.H., Continuous endpoint data mining with ExSTraCS: A supervised learning classifier system. Proceedings of the Genetic and Evolutionary Computing Conference. ACM Press, 1029-1036 (2015).
• Urbanowicz, R.J., Moore, J.H. ExSTraCS 2.0: Description and evaluation of a scalable learning classifier system. Evolutionary Intelligence. 8(2-3), 89-116 (2015). 

2014

• Urbanowicz, R.J., Bertasius, G., Moore, J.H. An extended michigan-style learning classifier system for flexible supervised learning, classification, and data mining. Springer Lecture Notes in Computer Science 8672, 211-221 (2014).
• Urbanowicz, R.J., Granizo-Mackenzie, A., Kiralis, J., Moore, J.H. A classification and characterization of two-locus pure, strict epistatic models for simulation and detection. BioData Mining. 7(1), 8 (2014).
• Urbanowicz, R.J., Moore, J.H. Learning classifier systems: The rise of genetics-based machine learning in biomedical data mining. In. Sarkar, N., (Eds.) Methods in Biomedical Informatics, 1st Edition, Elsevier. (2014).

2013

• Rudd, J., Moore, J.H., Urbanowicz, R.J. A multi-core parallelization strategy for statistical significance testing in learning classifier systems. Evolutionary Intelligence. 6(2), 127-134 (2013).
• Tan, J., Moore, J.H., Urbanowicz, R.J. Rapid rule compaction for knowledge discovery in a supervised learning classifier system. Advances in Artificial Life, 12. 110-117 (2013).
• Rudd, J., Moore, J.H., Urbanowicz, R.J. A simple multi-core parallelization strategy for learning classifier system evaluations. Proceedings of the Genetic and Evolutionary Computing Conference. ACM Press, 1259-1266 (2013).
• Urbanowicz, R.J., Andrew, A.S., Karagas, M.R., Moore, J.H. The role of genetic heterogeneity and epistasis in bladder cancer susceptibility and outcome: A learning classifier system approach. Journal of the American Medical Informatics Association, 20(4), 603-612 (2013).

2012

• Urbanowicz, R.J., Kiralis, J., Fisher, J.M., Moore, J.H. Predicting the difficulty of pure, strict, epistatic models: Metrics for simulated model selection. BioData Mining 5, 15 (2012).
• Urbanowicz, R.J., Kiralis, J., Sinnott-Armstrong, N.A., Heberling, T., Fisher, J.M., Moore, J.H. GAMETES: A fast, direct algorithm for generating pure, strict, epistatic models with random architectures. BioData Mining 5, 16 (2012).
• Urbanowicz, R.J., Granizo-Mackenzie, A., Moore, J.H. Using expert knowledge to guide covering and mutation in a Michigan-style learning classifier system to detect epistasis and heterogeneity. Springer Lecture Notes in Computer Science 7491, 266-275 (2012).
• Urbanowicz, R.J., Granizo-Mackenzie, A., Moore, J.H. Instance-linked attribute tracking and feedback for Michigan-style supervised learning classifier systems.  Proceedings of the Genetic and Evolutionary Computing Conference. ACM Press, pp. 927-934 (2012).
• Urbanowicz, R.J., Granizo-Mackenzie, A., Moore, J.H. An analysis pipeline with statistical and visualization-guided knowledge discovery for Michigan-style learning classifier systems. Computational Intelligence Magazine 7, 35-45 (2012).
• Urbanowicz, R.J. The detection and characterization of epistasis and heterogeneity: a learning classifier system approach. Moore, J.H., Whitfield, M.L., Eppstein M.J., Gross, R.H., Thornton-Wells, T.A. (Eds.) Genetics PhD Thesis, Dartmouth College. (2012).

2011

• Urbanowicz, R.J., Sinnott-Armstrong, N., Moore, J.H. Random artificial incorporation of noise in a learning classifier system environment.  Proceedings of the Genetic and Evolutionary Computing Conference. ACM Press, pp. 369-374 (2011).

2010

• Urbanowicz, R.J., Moore, J.H. The application of Pittsburgh-style learning classifier systems to address genetic heterogeneity and epistasis in association studies. Springer Lecture Notes in Computer Science pp. 195-202 (2010).
• Urbanowicz, R.J., Moore, J.H. The application of Michigan-style learning classifier systems to address genetic heterogeneity and epistasis in association studies. Proceedings of the Genetic and Evolutionary Computing Conference. ACM Press, pp. 195-202 (2010)

2009

• Urbanowicz, R.J., Moore, J.H. Learning classifier systems: A complete introduction, review and roadmap. Journal of Artificial Evolution and Applications 2009, 1-25 (2009).

2008

• Urbanowicz, R.J., White, B.C., Barney, N., Moore, J.H. Mask functions for the symbolic modeling of epistasis using genetic programming. Proceedings of the Genetic and Evolutionary Computing Conference. ACM Press, pp. 339-346 (2008).

2005

• Urbanowicz, R.J. Reassessment of a ganglioside-liposome biosensor for the detection of biological toxins. Durst, R.A, Baeumner, A.J. (Eds.) Masters Dissertation, Cornell University. (2005).