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Joint Learning Using Multiple Types of Data and Knowledge

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Medical Informatics

Part of the book series: Integrated Series in Information Systems ((ISIS,volume 8))

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Chapter Overview

This chapter discusses joint learning research in biomedical domains. A brief review of the field of joint learning research is given, with emphases on the large-scale data and knowledge resources used for learning and the central biological questions involved. Two representative joint learning case studies are presented with algorithmic details. The two case studies involved two representative joint learning tasks, protein function classification and regulatory network learning, and two important algorithmic frameworks for joint learning, the kernel-based framework and probabilistic graphical models. A wide range of biological data and existing knowledge was also involved in these two studies.

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Authors and Affiliations

  1. Artificial Intelligence Lab, Department of Management Information Systems, Eller College of Management, University of Arizona, Tucson, AZ, 85721

    Zan Huang, Hua Su & Hsinchun Chen

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  1. Zan Huang
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  2. Hua Su
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Editor information

Editors and Affiliations

  1. The University of Arizona, USA

    Hsinchun Chen

  2. University of Washington, USA

    Sherrilynne S. Fuller

  3. Columbia University, USA

    Carol Friedman

  4. Oregon Health & Science Univ., USA

    William Hersh

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Huang, Z., Su, H., Chen, H. (2005). Joint Learning Using Multiple Types of Data and Knowledge. In: Chen, H., Fuller, S.S., Friedman, C., Hersh, W. (eds) Medical Informatics. Integrated Series in Information Systems, vol 8. Springer, Boston, MA. https://doi.org/10.1007/0-387-25739-X_21

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  • DOI: https://doi.org/10.1007/0-387-25739-X_21

  • Publisher Name: Springer, Boston, MA

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