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2 - Artificial Intelligence Approaches to No-Boundary Thinking

Published online by Cambridge University Press:  14 September 2023

By
Jason H. Moore
Edited by
Xiuzhen Huang ,
Jason H. Moore  and
Yu Zhang
Xiuzhen Huang
Affiliation:
Cedars-Sinai Medical Center, Los Angeles
Jason H. Moore
Affiliation:
Cedars-Sinai Medical Center, Los Angeles
Yu Zhang
Affiliation:
Trinity University, Texas
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Summary

The goal of this chapter is to explore and review the role of artificial intelligence (AI) in scientific discovery from data. Specifically, we present AI as a useful tool for advancing a No-Boundary Thinking (NBT) approach to bioinformatics and biomedical informatics. NBT is an agnostic methodology for scientific discovery and education that accesses, integrates, and synthesizes data, information, and knowledge from all disciplines to define important problems, leading to innovative and significant questions that can subsequently be addressed by individuals or collaborative teams with diverse expertise. Given this definition, AI is uniquely poised to advance NBT as it has the potential to employ data science for discovery by using information and knowledge from multiple disciplines. We present three recent AI approaches to data analysis that each contribute to a foundation for an NBT research strategy by either incorporating expert knowledge, automating machine learning, or both. We end with a vision for fully automating the discovery process while embracing NBT.

Keywords

artificial intelligencemachine learningdata sciencedata analyticsNo-Boundary Thinkingautomated machine learninggenetic programming
Type
Chapter
Information
Integrative Bioinformatics for Biomedical Big Data
A No-Boundary Thinking Approach
 , pp. 5 - 24
Publisher: Cambridge University Press
Print publication year: 2023

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References

Ching, T, Himmelstein, DS, Beaulieu-Jones, BK, et al., 2018. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface, 15. https://doi.org/10.1098/rsif.2017.0387Google Scholar
Crevier, D, 1993. AI: The Tumultuous History of the Search for Artificial Intelligence. New York: Basic Books.Google Scholar
Esteva, A, Robicquet, A, Ramsundar, B, et al., 2019. A guide to deep learning in healthcare. Nature Medicine, 25:24–29. https://doi.org/10.1038/s41591–018-0316-zGoogle Scholar
Ferrucci, DA, 2012. Introduction to “This is Watson.” IBM Journal of Research and Development, 56(1):1–15. https://doi.org/10.1147/JRD.2012.2184356CrossRefGoogle Scholar
Feurer, M, Klein, A, Eggensperger, K, et al., 2015. Efficient and robust automated machine learning. In: Cortes, C, Lawrence, ND, Lee, DD, Sugiyama, M, Garnett, R. (eds.), Advances in Neural Information Processing Systems. Red Hook, NY: Curran Associates, Inc., pp. 2962–2970.Google Scholar
Geng, L, Hamilton, HJ, 2006. Interestingness measures for data mining: a survey. ACM Comput Surv, 38. https://doi.org/10.1145/1132960.1132963CrossRefGoogle Scholar
Greene, CS, Penrod, NM, Kiralis, J, Moore, JH, 2009. Spatially uniform reliefF (SURF) for computationally-efficient filtering of gene–gene interactions. BioData Min, 2:5. https://doi.org/10.1186/1756-0381-2-5Google Scholar
Hersh, W, 2009. Information Retrieval: A Health and Biomedical Perspective, 3rd edn. New York: Springer-Verlag.CrossRefGoogle Scholar
Himmelstein, DS, Baranzini, SE, 2015. Heterogeneous network edge prediction: a data integration approach to prioritize disease-associated genes. PLoS Comput Biol, 11: e1004259. https://doi.org/10.1371/journal.pcbi.1004259Google Scholar
Himmelstein, DS, Lizee, A, Hessler, C, et al., 2017. Systematic integration of biomedical knowledge prioritizes drugs for repurposing. Elife, 6. https://doi.org/10.7554/eLife.26726Google Scholar
Hinton, GE, Salakhutdinov, RR, 2006. Reducing the dimensionality of data with neural networks. Science, 313:504–507. https://doi.org/10.1126/science.1127647Google Scholar
Huang, X, Bruce, B, Buchan, A, et al., 2013. No-boundary thinking in bioinformatics research. BioData Min, 6:19. https://doi.org/10.1186/1756-0381-6-19Google Scholar
Huang, X, Jennings, SF, Bruce, B, et al., 2015. Big data: a 21st century science Maginot Line? No-boundary thinking: shifting from the big data paradigm. BioData Min, 8:7. https://doi.org/10.1186/s13040-015-0037-5CrossRefGoogle ScholarPubMed
Hutter, F, Kotthoff, L, Vanschoren, J (eds.), 2019. Automated Machine Learning: Methods, Systems, Challenges. New York: Springer.Google Scholar
La Cava, W, Williams, H, Fu, W, Moore, JH, 2020. Evaluating recommender systems for AI-driven data science. Bioinformatics. https://doi.org/10.1093/bioinformatics/btaa698CrossRefGoogle Scholar
Le, TT, Savitz, J, Suzuki, H, et al., 2018. Identification and replication of RNA-Seq gene network modules associated with depression severity. Transl Psychiatry, 8:180. https://doi.org/10.1038/s41398–018-0234-3CrossRefGoogle ScholarPubMed
Le, TT, Fu, W, Moore, JH, 2020. Scaling tree-based automated machine learning to biomedical big data with a feature set selector. Bioinformatics, 36:250–256. https://doi.org/10.1093/bioinformatics/btz470CrossRefGoogle ScholarPubMed
Moore, JH, 2015. Epistasis analysis using ReliefF. Methods Mol Biol, 1253:315–325. https://doi.org/10.1007/978-1-4939-2155-3_17Google Scholar
Moore, JH, Parker, JS, Olsen, NJ, Aune, TM, 2002. Symbolic discriminant analysis of microarray data in autoimmune disease. Genet Epidemiol, 23:57–69. https://doi.org/10.1002/gepi.1117Google Scholar
Moore, JH, Barney, N, Tsai, C-T, et al., 2007. Symbolic modeling of epistasis. Hum Hered, 63:120–133. https://doi.org/10.1159/000099184CrossRefGoogle ScholarPubMed
Moore, JH, Andrews, PC, Barney, N, White, BC, 2008. Development and evaluation of an open-ended computational evolution system for the genetic analysis of susceptibility to common human diseases. In: Marchiori, E, Moore, JH (eds.), Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. Berlin: Springer, pp. 129–140. https://doi.org/10.1007/978-3-540-78757-0_12CrossRefGoogle Scholar
Moore, JH, Greene, CS, Andrews, PC, White, BC, 2009. Does complexity matter? Artificial evolution, computational evolution and the genetic analysis of epistasis in common human diseases. In: Genetic Programming Theory and Practice VI, Genetic and Evolutionary Computation. Boston, MA: Springer, pp. 1–19. https://doi.org/10.1007/978-0-387-87623-8_9Google Scholar
Moore, JH, Hill, DP, Fisher, JM, Lavender, N, Kidd, LC, 2011. Human–computer interaction in a computational evolution system for the genetic analysis of cancer. In: Riolo, R, Vladislavleva, E, Moore, JH (eds.), Genetic Programming Theory and Practice IX, Genetic and Evolutionary Computation. New York: Springer, pp. 153–171. https://doi.org/10.1007/978-1-4614-1770-5_9Google Scholar
Moore, JH, Hill, DP, Saykin, A, Shen, L, 2014. Exploring interestingness in a computational evolution system for the genome-wide genetic analysis of Alzheimer’s disease. In Riolo, R, Moore, JH, Kotanchek, M (eds.), Genetic Programming Theory and Practice XI, Genetic and Evolutionary Computation. New York: Springer, pp. 31–45. https://doi.org/10.1007/978-1-4939-0375-7_2Google Scholar
Moore, JH, Greene, CS, Hill, DP, 2015. Identification of novel genetic models of glaucoma using the “EMERGENT” genetic programming-based artificial intelligence system. In Riolo, R, Worzel, WP, Kotanchek, M (eds.), Genetic Programming Theory and Practice XII, Genetic and Evolutionary Computation. Cham: Springer, pp. 17–35. https://doi.org/10.1007/978-3-319-16030-6_2Google Scholar
Olson, RS, Moore, JH, 2019. TPOT: a tree-based pipeline optimization tool for automating machine learning. In: Hutter, F, Kotthoff, L, Vanschoren, J (eds.), Automated Machine Learning: Methods, Systems, Challenges. Cham: Springer, pp. 151–160. https://doi.org/10.1007/978-3-030-05318-5_8Google Scholar
Olson, RS, Bartley, N, Urbanowicz, RJ, Moore, JH, 2016a. Evaluation of a tree-based pipeline optimization tool for automating data science. In: Proceedings of the Genetic and Evolutionary Computation Conference 2016, GECCO’16. New York: ACM, pp. 485–492. https://doi.org/10.1145/2908812.2908918CrossRefGoogle Scholar
Olson, RS, Urbanowicz, RJ, Andrews, PC, et al., 2016b. Automating biomedical data science through tree-based pipeline optimization. In: Squillero, G, Burelli, P (eds.), Applications of Evolutionary Computation. Cham: Springer, pp. 123–137. https://doi.org/10.1007/978-3-319-31204-0_9Google Scholar
Olson, RS, La Cava, W, Orzechowski, P, Urbanowicz, RJ, Moore, JH, 2017. PMLB: a large benchmark suite for machine learning evaluation and comparison. BioData Min, 10:36. https://doi.org/10.1186/s13040–017-0154-4Google Scholar
Olson, RS, La Cava, W, Mustahsan, Z, Varik, A, Moore, JH, 2018a. Data-driven advice for applying machine learning to bioinformatics problems. Pac Symp Biocomput, 23:192–203.Google Scholar
Olson, RS, Sipper, M, La Cava, W, et al., 2018b. A system for accessible artificial intelligence. In: Banzhaf, W, Olson, RS, Tozier, W, Riolo, R (eds.), Genetic Programming Theory and Practice XV, Genetic and Evolutionary Computation. New York: Springer, pp. 121–134.CrossRefGoogle Scholar
Pattin, KA, Payne, JL, Hill, DP, et al., 2011. Exploiting expert knowledge of protein–protein interactions in a computational evolution system for detecting epistasis. In Riolo, R, McConaghy, T, Vladislavleva, E (eds.), Genetic Programming Theory and Practice VIII, Genetic and Evolutionary Computation. New York: Springer, pp. 195–210. https://doi.org/10.1007/978-1-4419-7747-2_12Google Scholar
Pedregosa, F, Varoquaux, G, Gramfort, A, et al., 2011. Scikit-learn: machine learning in Python. J Mach Learn Res, 12:2825−2830.Google Scholar
Ritchie, MD, Hahn, LW, Roodi, N, et al., 2001. Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet, 69:138–147. https://doi.org/10.1086/321276CrossRefGoogle ScholarPubMed
Sohn, A, Olson, RS, Moore, JH, 2017. Toward the automated analysis of complex diseases in genome-wide association studies using genetic programming. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO’17. New York: ACM, pp. 489–496. https://doi.org/10.1145/3071178.3071212Google Scholar
Strickland, E, 2019. IBM Watson, heal thyself: how IBM overpromised and underdelivered on AI health care. IEEE Spectrum, 56:24–31. https://doi.org/10.1109/MSPEC.2019.8678513Google Scholar
Sybrandt, J, Shtutman, M, Safro, I, 2017. MOLIERE: automatic biomedical hypothesis generation system. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’17. New York: ACM, pp. 1633–1642. https://doi.org/10.1145/3097983.3098057Google Scholar
Thornton, C, Hutter, F, Hoos, HH, Leyton-Brown, K, 2013. Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’13. New York: ACM, pp. 847–855. https://doi.org/10.1145/2487575.2487629CrossRefGoogle Scholar
Topol, EJ, 2019. High-performance medicine: the convergence of human and artificial intelligence. Nat Med, 25:44–56. https://doi.org/10.1038/s41591-018-0300-7Google Scholar
Urbanowicz, RJ, Meeker, M, La Cava, W, Olson, RS, Moore, JH, 2018. Relief-based feature selection: introduction and review. J Biomed Informat, 85:189–203. https://doi.org/10.1016/j.jbi.2018.07.014Google Scholar

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