IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v276y2019i1d10.1007_s10479-018-2891-2.html
   My bibliography  Save this article

Massive datasets and machine learning for computational biomedicine: trends and challenges

Author

Listed:
  • Anton Kocheturov

    (University of Florida)

  • Panos M. Pardalos

    (University of Florida
    National Research University Higher School of Economics)

  • Athanasia Karakitsiou

    (Technological Educational Institute of Central Macedonia)

Abstract

This survey paper attempts to cover a broad range of topics related to computational biomedicine. The field has been attracting great attention due to a number of benefits it can provide the society with. New technological and theoretical advances have made it possible to progress considerably. Traditionally, problems emerging in this field are challenging from many perspectives. In this paper, we considered the influence of big data on the field, problems associated with massive datasets in biomedicine and ways to address these problems. We analyzed the most commonly used machine learning and feature mining tools and several new trends and tendencies such as deep learning and biological networks for computational biomedicine.

Suggested Citation

  • Anton Kocheturov & Panos M. Pardalos & Athanasia Karakitsiou, 2019. "Massive datasets and machine learning for computational biomedicine: trends and challenges," Annals of Operations Research, Springer, vol. 276(1), pages 5-34, May.
    • Handle: RePEc:spr:annopr:v:276:y:2019:i:1:d:10.1007_s10479-018-2891-2
    DOI: 10.1007/s10479-018-2891-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-018-2891-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-018-2891-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. John P A Ioannidis, 2005. "Why Most Published Research Findings Are False," PLOS Medicine, Public Library of Science, vol. 2(8), pages 1-1, August.
    2. Matthew F. Glasser & Timothy S. Coalson & Emma C. Robinson & Carl D. Hacker & John Harwell & Essa Yacoub & Kamil Ugurbil & Jesper Andersson & Christian F. Beckmann & Mark Jenkinson & Stephen M. Smith , 2016. "A multi-modal parcellation of human cerebral cortex," Nature, Nature, vol. 536(7615), pages 171-178, August.
    3. Réka Albert & Hawoong Jeong & Albert-László Barabási, 1999. "Diameter of the World-Wide Web," Nature, Nature, vol. 401(6749), pages 130-131, September.
    4. ,, 1998. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 14(1), pages 151-159, February.
    5. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    6. Crookston, Nicholas L. & Finley, Andrew O., 2008. "yaImpute: An R Package for kNN Imputation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 23(i10).
    7. Po-Ssu Huang & Scott E. Boyken & David Baker, 2016. "The coming of age of de novo protein design," Nature, Nature, vol. 537(7620), pages 320-327, September.
    8. Michael Eisenstein, 2015. "Big data: The power of petabytes," Nature, Nature, vol. 527(7576), pages 2-4, November.
    9. Ximing Wang & Neng Fan & Panos M. Pardalos, 2018. "Robust chance-constrained support vector machines with second-order moment information," Annals of Operations Research, Springer, vol. 263(1), pages 45-68, April.
    10. ,, 1998. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 14(5), pages 687-698, October.
    11. ,, 1998. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 14(3), pages 381-386, June.
    12. H. Jeong & B. Tombor & R. Albert & Z. N. Oltvai & A.-L. Barabási, 2000. "The large-scale organization of metabolic networks," Nature, Nature, vol. 407(6804), pages 651-654, October.
    13. ,, 1998. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 14(4), pages 525-537, August.
    14. Trivellore E. Raghunathan & David S. Siscovick, 1996. "A Multiple‐Imputation Analysis of a Case‐Control Study of the Risk of Primary Cardiac Arrest Among Pharmacologicallytreated Hypertensives," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 45(3), pages 335-352, September.
    15. Davi D. Bock & Wei-Chung Allen Lee & Aaron M. Kerlin & Mark L. Andermann & Greg Hood & Arthur W. Wetzel & Sergey Yurgenson & Edward R. Soucy & Hyon Suk Kim & R. Clay Reid, 2011. "Network anatomy and in vivo physiology of visual cortical neurons," Nature, Nature, vol. 471(7337), pages 177-182, March.
    16. ,, 1998. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 14(2), pages 285-292, April.
    17. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bram Janssens & Matthias Bogaert & Mathijs Maton, 2023. "Predicting the next Pogačar: a data analytical approach to detect young professional cycling talents," Annals of Operations Research, Springer, vol. 325(1), pages 557-588, June.
    2. Marina Johnson & Abdullah Albizri & Serhat Simsek, 2022. "Artificial intelligence in healthcare operations to enhance treatment outcomes: a framework to predict lung cancer prognosis," Annals of Operations Research, Springer, vol. 308(1), pages 275-305, January.
    3. Matteo Cinelli & Valerio Ficcadenti & Jessica Riccioni, 2021. "The interconnectedness of the economic content in the speeches of the US Presidents," Annals of Operations Research, Springer, vol. 299(1), pages 593-615, April.
    4. Matteo Cinelli & Valerio Ficcadenti & Jessica Riccioni, 2020. "The interconnectedness of the economic content in the speeches of the US Presidents," Papers 2002.07880, arXiv.org.
    5. Erfan Mehmanchi & Andrés Gómez & Oleg A. Prokopyev, 2021. "Solving a class of feature selection problems via fractional 0–1 programming," Annals of Operations Research, Springer, vol. 303(1), pages 265-295, August.
    6. Viswanath Venkatesh, 2022. "Adoption and use of AI tools: a research agenda grounded in UTAUT," Annals of Operations Research, Springer, vol. 308(1), pages 641-652, January.
    7. Xiaotong Sun & Wei Xu & Hongxun Jiang & Qili Wang, 2021. "A deep multitask learning approach for air quality prediction," Annals of Operations Research, Springer, vol. 303(1), pages 51-79, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marcin Chlebus & Zuzanna Osika, 2020. "Comparison of tree-based models performance in prediction of marketing campaign results using Explainable Artificial Intelligence tools," Working Papers 2020-15, Faculty of Economic Sciences, University of Warsaw.
    2. Allen C. Goodman & Miron Stano, 2000. "Hmos and Health Externalities: A Local Public Good Perspective," Public Finance Review, , vol. 28(3), pages 247-269, May.
    3. Bettina Campedelli & Andrea Guerrina & Giulia Romano & Chiara Leardini, 2014. "La performance della rete ospedaliera pubblica della regione Veneto. L?impatto delle variabili ambientali e operative sull?efficienza," MECOSAN, FrancoAngeli Editore, vol. 2014(92), pages 119-142.
    4. Penn Loh & Zoë Ackerman & Joceline Fidalgo & Rebecca Tumposky, 2022. "Co-Education/Co-Research Partnership: A Critical Approach to Co-Learning between Dudley Street Neighborhood Initiative and Tufts University," Social Sciences, MDPI, vol. 11(2), pages 1-17, February.
    5. O'Brien, Raymond & Patacchini, Eleonora, 2003. "Testing the exogeneity assumption in panel data models with "non classical" disturbances," Discussion Paper Series In Economics And Econometrics 0302, Economics Division, School of Social Sciences, University of Southampton.
    6. YongSeog Kim & W. Nick Street & Gary J. Russell & Filippo Menczer, 2005. "Customer Targeting: A Neural Network Approach Guided by Genetic Algorithms," Management Science, INFORMS, vol. 51(2), pages 264-276, February.
    7. Yanling Li & Zita Oravecz & Shuai Zhou & Yosef Bodovski & Ian J. Barnett & Guangqing Chi & Yuan Zhou & Naomi P. Friedman & Scott I. Vrieze & Sy-Miin Chow, 2022. "Bayesian Forecasting with a Regime-Switching Zero-Inflated Multilevel Poisson Regression Model: An Application to Adolescent Alcohol Use with Spatial Covariates," Psychometrika, Springer;The Psychometric Society, vol. 87(2), pages 376-402, June.
    8. Oscar J. Cacho & Robyn L. Hean & Russell M. Wise, 2003. "Carbon‐accounting methods and reforestation incentives," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 47(2), pages 153-179, June.
    9. Walter M. Cadette, 1999. "Financing Long-Term Care: Options for Policy," Economics Working Paper Archive wp_283, Levy Economics Institute.
    10. Eggli, Yves & Halfon, Patricia & Chikhi, Mehdi & Bandi, Till, 2006. "Ambulatory healthcare information system: A conceptual framework," Health Policy, Elsevier, vol. 78(1), pages 26-38, August.
    11. M. A. Noor & E.A. Al-Said, 2002. "Finite-Difference Method for a System of Third-Order Boundary-Value Problems," Journal of Optimization Theory and Applications, Springer, vol. 112(3), pages 627-637, March.
    12. Yong He & Zhiyi Tan, 2002. "Ordinal On-Line Scheduling for Maximizing the Minimum Machine Completion Time," Journal of Combinatorial Optimization, Springer, vol. 6(2), pages 199-206, June.
    13. Henderson, James E. & Dunn, Michael A., 2007. "Investigating the Potential of Fee-Based Recreation on Private Lands in the Lower Mississippi River Delta," 2007 Annual Meeting, February 4-7, 2007, Mobile, Alabama 34822, Southern Agricultural Economics Association.
    14. Eike Quilling & Birgit Babitsch & Kevin Dadaczynski & Stefanie Kruse & Maja Kuchler & Heike Köckler & Janna Leimann & Ulla Walter & Christina Plantz, 2020. "Municipal Health Promotion as Part of Urban Health: A Policy Framework for Action," Sustainability, MDPI, vol. 12(16), pages 1-10, August.
    15. Haeringer, Guillaume & Klijn, Flip, 2009. "Constrained school choice," Journal of Economic Theory, Elsevier, vol. 144(5), pages 1921-1947, September.
    16. Alireza Nili & Mary Tate & David Johnstone, 2019. "The process of solving problems with self-service technologies: a study from the user’s perspective," Electronic Commerce Research, Springer, vol. 19(2), pages 373-407, June.
    17. Chein-Shan Liu & Zhuojia Fu & Chung-Lun Kuo, 2017. "Directional Method of Fundamental Solutions for Three-dimensional Laplace Equation," Journal of Mathematics Research, Canadian Center of Science and Education, vol. 9(6), pages 112-123, December.
    18. Ali Akgül & Esra Karatas Akgül & Dumitru Baleanu & Mustafa Inc, 2018. "New Numerical Method for Solving Tenth Order Boundary Value Problems," Mathematics, MDPI, vol. 6(11), pages 1-9, November.
    19. Li, Haitao & Womer, Norman K., 2015. "Solving stochastic resource-constrained project scheduling problems by closed-loop approximate dynamic programming," European Journal of Operational Research, Elsevier, vol. 246(1), pages 20-33.
    20. José Sánchez Maldonado & Salvador Gómez Sala, 2006. "The Reform of Indirect Taxation in Spain: VAT and Excise," International Center for Public Policy Working Paper Series, at AYSPS, GSU paper0607, International Center for Public Policy, Andrew Young School of Policy Studies, Georgia State University.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:276:y:2019:i:1:d:10.1007_s10479-018-2891-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.