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Massively parallel symbolic induction of protein structure/function relationships

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Machine Learning: From Theory to Applications

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 661))

Abstract

We have described a running system that embodies efficient parallel implementations of several symbolic machine learning induction operators. It functions as an “Induction Assistant” to a domain expert. First we developed an efficient, noise-tolerant, similarity-based parallel matching algorithm. This should apply to other graph-based representations of domains possessing an embedding in which the low-level features (relations or groupings) are mostly local. It was used as infrastructure to construct efficient parallel implementations of several symbolic machine learning induction operators. Finally, the induction operators were sandwiched together with sets of filters (both syntactic and empirical) to compose a crude form of induction scripts, which are invoked by a domain expert. The matching algorithm has very attractive scaling properties as the size of the problem and/or the number of processors increases. Hardware usage is efficient. The results reported in this article were obtained on an 8K CM-2 Connection Machine. The implemented system was used to discover something previously unknown to the domain expert [47].

For us, the key contribution of this work is its demonstration of the scalability of the algorithms involved: The time complexity of every algorithm reported here is nearly independent of the size of the data, provided sufficient parallel hardware is available (subject to discussion about the instance-ID and characteristic set bit-vectors).

Reprinted, with permission, from Proceedings of the 24th Annual Hawaii International Conf. on System Sciences (HICSS'24), Kauai, Hawaii, Jan 8–11, 1991, pp. 585–594.

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Author information

Authors and Affiliations

  1. Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 02138, Cambridge, Massachusetts

    Richard H. Lathrop & Patrick H. Winston

  2. Computational Development, ARRIS Pharmaceutical Corporation, South San Francisco, California

    Teresa A. Webster

  3. Molecular Biology Computer Research Resource, Dana Farber Cancer Institute, Harvard School of Public Health, Boston, Massachusetts

    Temple F. Smith

Authors
  1. Richard H. Lathrop
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  2. Teresa A. Webster
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  3. Temple F. Smith
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  4. Patrick H. Winston
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Stephen José Hanson Werner Remmele Ronald L. Rivest

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© 1993 Springer-Verlag Berlin Heidelberg

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Lathrop, R.H., Webster, T.A., Smith, T.F., Winston, P.H. (1993). Massively parallel symbolic induction of protein structure/function relationships. In: Hanson, S.J., Remmele, W., Rivest, R.L. (eds) Machine Learning: From Theory to Applications. Lecture Notes in Computer Science, vol 661. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56483-7_30

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  • DOI: https://doi.org/10.1007/3-540-56483-7_30

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