Advertisement

Machine Learning-as-a-Service and Its Application to Medical Informatics

  • Ahmad P. Tafti
  • Eric LaRose
  • Jonathan C. Badger
  • Ross Kleiman
  • Peggy Peissig
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10358)

Abstract

Machine learning as an advanced computational technology has been around for several years in discovering patterns from diverse biomedical data sources and providing excellent capabilities ranging from gene annotation to predictive phenotyping. However, machine learning strategies remain underused in small and medium-scale biomedical research labs where they have been collaboratively providing a reasonable amount of scientific knowledge. While most machine learning algorithms are complicated in code, theses labs and individual researchers could accomplish iterative data analysis using different machine learning techniques if they had access to highly available machine learning components and powerful computational infrastructures. In this contribution, we provide a comparison of several state-of-the-art Machine Learning-as-a-Service platforms along with their capabilities in medical informatics. In addition, we performed several analyses to examine the qualitative and quantitative attributes of two Machine Learning-as-a-Service environments namely “BigML” and “Algorithmia”.

References

  1. 1.
  2. 2.
    Alpaydin, E.: Introduction to Machine Learning. MIT Press, Cambridge (2014)MATHGoogle Scholar
  3. 3.
    Michalski, R.S., Carbonell, J.G., Mitchell, T.M.: Machine Learning: An Artificial Intelligence Approach. Springer, Heidelberg (2013)MATHGoogle Scholar
  4. 4.
    Pop, D.: Machine learning and cloud computing: survey of distributed and SaaS solutions. arXiv preprint arXiv:1603.08767 (2016)
  5. 5.
    Kumar, A., Kiran, M., Prathap, B.R.: Verification and validation of mapreduce program model for parallel k-means algorithm on hadoop cluster. In: 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), pp. 1–8. IEEE (2013)Google Scholar
  6. 6.
    Nguyen, T.: Machine learning on the cloud for pattern recognition (2016)Google Scholar
  7. 7.
    Cano, I., Weimer, M., Mahajan, D., Curino, C., Fumarola, G.M.: Towards geo-distributed machine learning. arXiv preprint arXiv:1603.09035 (2016)
  8. 8.
    Kraska, T., Talwalkar, A., Duchi, J.C., Griffith, R., Franklin, M.J., Jordan, M.I.: MLbase: a distributed machine-learning system. In: CIDR, vol. 1, pp. 1–2 (2013)Google Scholar
  9. 9.
    Gillick, D., Faria, A., DeNero, J.: Mapreduce: distributed computing for machine learning, Berkley, 18 December 2006Google Scholar
  10. 10.
    Xing, E.P., Ho, Q., Dai, W., Kim, J.K., Wei, J., Lee, S., Zheng, X., Xie, P., Kumar, A., Yu, Y.: Petuum: a new platform for distributed machine learning on big data. IEEE Trans. Big Data 1(2), 49–67 (2015)CrossRefGoogle Scholar
  11. 11.
    Herath, D.H., Wilson-Ing, D., Ramos, E., Morstyn, G.: Assessing the natural language processing capabilities of IBM Watson for oncology using real Australian lung cancer cases. In: ASCO Annual Meeting Proceedings, vol. 34, p. e18229 (2016)Google Scholar
  12. 12.
    Guidi, G., Miniati, R., Mazzola, M., Iadanza, E.: Case study: IBM Watson analytics cloud platform as analytics-as-a-service system for heart failure early detection. Future Internet 8(3), 32 (2016)CrossRefGoogle Scholar
  13. 13.
    Agrawal, H.: CloudCV: deep learning and computer vision on the cloud. Ph.D. thesis, Virginia Tech (2016)Google Scholar
  14. 14.
    Evani, U.S., Challis, D., Jin, Y., Jackson, A.R., Paithankar, S., Bainbridge, M.N., Jakkamsetti, A., Pham, P., Coarfa, C., Milosavljevic, A., et al.: Atlas2 cloud: a framework for personal genome analysis in the cloud. BMC Genomics 13(6), 1 (2012)Google Scholar
  15. 15.
    Li, C., Tan, Y., Wang, D., Ma, P.: Research on 3D face recognition method in cloud environment based on semi supervised clustering algorithm. Multimed. Tools Appl. 1–19 (2016)Google Scholar
  16. 16.
    Jiang, G., Fan, M., Li, L.: A cloud platform for remote diagnosis of breast cancer in mammography by fusion of machine and human intelligence. In: SPIE Medical Imaging, p. 97890S. International Society for Optics and Photonics (2016)Google Scholar
  17. 17.
    Chang, Y.-S., Hung, S.-H., Wang, N.J.C., Lin, B.-S.: CSR: a cloud-assisted speech recognition service for personal mobile device. In: 2011 International Conference on Parallel Processing, pp. 305–314. IEEE (2011)Google Scholar
  18. 18.
    Assefi, M., Wittie, M., Knight, A.: Impact of network performance on cloud speech recognition. In: 2015 24th International Conference on Computer Communication and Networks (ICCCN), pp. 1–6. IEEE (2015)Google Scholar
  19. 19.
    Assefi, M., Liu, G., Wittie, M.P., Izurieta, C.: An experimental evaluation of Apple Siri and Google speech recognition. In: Proceedings of the 2015 ISCA SEDE (2015)Google Scholar
  20. 20.
    Surveillance, Epidemiology, and End Results (SEER) Program (www.seer.cancer.gov) Research Data (1973–2012): National Cancer Institute. DCCPS, Surveillance Research Program, Surveillance Systems Branch, April 2015. Based on the November 2014 submission
  21. 21.
    Alcalá, J., Fernández, A., Luengo, J., Derrac, J., García, S., Sánchez, L., Herrera, F.: Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J. Mult. Valued Log. Soft Comput. 17(2–3), 255–287 (2010)Google Scholar
  22. 22.
    Hayes, W.S., Borodovsky, M.: How to interpret an anonymous bacterial genome: machine learning approach to gene identification. Genome Res. 8(11), 1154–1171 (1998)CrossRefGoogle Scholar
  23. 23.
    Rätsch, G., Sonnenburg, S., Srinivasan, J., Witte, H., Müller, K.-R., Sommer, R.-J., Schölkopf, B.: Improving the caenorhabditis elegans genome annotation using machine learning. PLoS Comput. Biol. 3(2), e20 (2007)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Baldi, P., Brunak, S.: Bioinformatics: The Machine Learning Approach. MIT Press, Cambridge (2001)MATHGoogle Scholar
  25. 25.
    Zhou, G., Zhang, J., Jian, S., Shen, D., Tan, C.: Recognizing names in biomedical texts: a machine learning approach. Bioinformatics 20(7), 1178–1190 (2004)CrossRefGoogle Scholar
  26. 26.
    Singhal, A., Simmons, M., Lu, Z.: Text mining for precision medicine: automating disease-mutation relationship extraction from biomedical literature. J. Am. Med. Inform. Assoc. ocw041 (2016)Google Scholar
  27. 27.
    Ashley, D.M., Gupta, S., Tran, T., Wei, L., Lorgelly, P.K., Thomas, D.M., Fox, S.B., Venkatesh, S.: Machine-learning prediction of cancer survival: a prospective study examining the impact of combining clinical and genomic data. In: ASCO Annual Meeting Proceedings, vol. 33, p. 6521 (2015)Google Scholar
  28. 28.
    Käll, L., Canterbury, J.D., Weston, J., Noble, W.S., MacCoss, M.J.: Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nature Methods 4(11), 923–925 (2007)CrossRefGoogle Scholar
  29. 29.
    Wu, J., Roy, J., Stewart, W.F.: Prediction modeling using ehr data: challenges, strategies, and a comparison of machine learning approaches. Med. Care 48(6), S106–S113 (2010)CrossRefGoogle Scholar
  30. 30.
    Peissig, P.L., Costa, V.S., Caldwell, M.D., Rottscheit, C., Berg, R.L., Mendonca, E.A., Page, D.: Relational machine learning for electronic health record-driven phenotyping. J. Biomed. Inform. 52, 260–270 (2014)CrossRefGoogle Scholar
  31. 31.
    Bardosi, Z., Granata, D., Lugos, G., Tafti, A.P., Saxena, S: Metacarpal bones localization in x-ray imagery using particle filter segmentation. arXiv preprint arXiv:1412.8197 (2014)
  32. 32.
    de Bruijne, M.: Machine learning approaches in medical image analysis: from detection to diagnosis (2016)Google Scholar
  33. 33.
    Malakooti, M.V., Tafti, A.P., Naji, H.R.: An efficient algorithm for human cell detection in electron microscope images based on cluster analysis and vector quantization techniques. In: 2012 Second International Conference on Digital Information and Communication Technology and it’s Applications (DICTAP), pp. 125–129. IEEE (2012)Google Scholar
  34. 34.
    Tafti, A.P., Holz, J.D., Baghaie, A., Owen, H.A., He, M.M., Yu, Z.: 3DSEM++: adaptive and intelligent 3D SEM surface reconstruction. Micron 87, 33–45 (2016)CrossRefGoogle Scholar
  35. 35.
    Patel, K.G., Welch, M., Gustafsson, C.: Leveraging gene synthesis, advanced cloning techniques, and machine learning for metabolic pathway engineering. In: Van Dien, S. (ed.) Metabolic Engineering for Bioprocess Commercialization, pp. 53–71. Springer, Cham (2016)CrossRefGoogle Scholar
  36. 36.
    Brown, M.P.S., Grundy, W.N., Lin, D., Cristianini, N., Sugnet, C.W., Furey, T.S., Ares, M., Haussler, D.: Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc. Natl. Acad. Sci. 97(1), 262–267 (2000)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Biomedical Informatics Research CenterMarshfield Clinic Research InstituteMarshfieldUSA
  2. 2.Computation and Informatics in Biology and MedicineUniversity of Wisconsin-MadisonMadisonUSA

Personalised recommendations