Massively parallel symbolic induction of protein structure/function relationships

Author

Lathrop, Richard H. ; Webster, Teresa A. ; Smith, Temple F. ; Winston, Patrick H.

Author_Institution

Artificial Intelligence Lab., MIT, Cambridge, MA, USA

Volume

i

fYear

1991

fDate

8-11 Jan 1991

Firstpage

585

Abstract

Reports the development and implementation of efficient algorithms for several symbolic machine learning induction operators on a massively parallel computer. The authors invoke these operators as hardware induction subroutines under the control of a higher-level front-end LISP program. For them, the key contribution of this work is its demonstration of the scalability of the algorithms involved. The time complexity of the induction algorithms is essentially independent of the total size of the instance data pool, with essentially linear space (hardware) complexity. Everything described has been implemented in Common LISP or PARIS. The PARIS portion runs on a CM-2 Connection Machine. The system (ARIEL) has been applied to the DNA polymerases and to the transcriptional activators by domain experts

Keywords

learning systems; macromolecular configurations; parallel algorithms; proteins; DNA polymerases; computational complexity; hardware induction; induction operators; machine learning; protein structure; symbolic induction; time complexity; transcriptional activators; Artificial intelligence; Biology computing; Computational biology; Concurrent computing; Hardware; Laboratories; Machine learning; Machine learning algorithms; Matched filters; Protein engineering;

fLanguage

English

Publisher

ieee

Conference_Titel

System Sciences, 1991. Proceedings of the Twenty-Fourth Annual Hawaii International Conference on

Conference_Location

Kauai, HI

Type

conf

DOI

10.1109/HICSS.1991.183931

Filename

183931