Data-driven theory refinement algorithms for bioinformatics

Jihoon Yang; Rajesh Parekh; Vasant Honavar; Drena Dobbs

Data-driven theory refinement algorithms for bioinformatics

Jihoon Yang, Rajesh Parekh, Vasant Honavar, Drena Dobbs

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Bioinformatics and related applications call for efficient algorithms for knowledge-intensive learning and data-driven knowledge refinement. Knowledge based artificial neural networks offer an attractive approach to extending or modifying incomplete knowledge bases or domain theories. We present results of experiments with several such algorithms for data-driven knowledge discovery and theory refinement in some simple bioinformatics applications. Results of experiments on the ribosome binding site and promoter site identification problems indicate that the performance of KBDistAl and Tiling-Pyramid algorithms compares quite favorably with those of substantially more computationally demanding techniques.

Original language	English (US)
Title of host publication	Proceedings of the International Joint Conference on Neural Networks
Publisher	IEEE
Pages	4064-4068
Number of pages	5
Volume	6
State	Published - 1999
Event	International Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA Duration: Jul 10 1999 → Jul 16 1999

Other

Other	International Joint Conference on Neural Networks (IJCNN'99)
City	Washington, DC, USA
Period	7/10/99 → 7/16/99

All Science Journal Classification (ASJC) codes

Software
Artificial Intelligence

Cite this

@inproceedings{987c5160de5542268d3c2e039182f609,

title = "Data-driven theory refinement algorithms for bioinformatics",

abstract = "Bioinformatics and related applications call for efficient algorithms for knowledge-intensive learning and data-driven knowledge refinement. Knowledge based artificial neural networks offer an attractive approach to extending or modifying incomplete knowledge bases or domain theories. We present results of experiments with several such algorithms for data-driven knowledge discovery and theory refinement in some simple bioinformatics applications. Results of experiments on the ribosome binding site and promoter site identification problems indicate that the performance of KBDistAl and Tiling-Pyramid algorithms compares quite favorably with those of substantially more computationally demanding techniques.",

author = "Jihoon Yang and Rajesh Parekh and Vasant Honavar and Drena Dobbs",

year = "1999",

language = "English (US)",

volume = "6",

pages = "4064--4068",

booktitle = "Proceedings of the International Joint Conference on Neural Networks",

publisher = "IEEE",

note = "International Joint Conference on Neural Networks (IJCNN'99) ; Conference date: 10-07-1999 Through 16-07-1999",

}

TY - GEN

T1 - Data-driven theory refinement algorithms for bioinformatics

AU - Yang, Jihoon

AU - Parekh, Rajesh

AU - Honavar, Vasant

AU - Dobbs, Drena

PY - 1999

Y1 - 1999

N2 - Bioinformatics and related applications call for efficient algorithms for knowledge-intensive learning and data-driven knowledge refinement. Knowledge based artificial neural networks offer an attractive approach to extending or modifying incomplete knowledge bases or domain theories. We present results of experiments with several such algorithms for data-driven knowledge discovery and theory refinement in some simple bioinformatics applications. Results of experiments on the ribosome binding site and promoter site identification problems indicate that the performance of KBDistAl and Tiling-Pyramid algorithms compares quite favorably with those of substantially more computationally demanding techniques.

AB - Bioinformatics and related applications call for efficient algorithms for knowledge-intensive learning and data-driven knowledge refinement. Knowledge based artificial neural networks offer an attractive approach to extending or modifying incomplete knowledge bases or domain theories. We present results of experiments with several such algorithms for data-driven knowledge discovery and theory refinement in some simple bioinformatics applications. Results of experiments on the ribosome binding site and promoter site identification problems indicate that the performance of KBDistAl and Tiling-Pyramid algorithms compares quite favorably with those of substantially more computationally demanding techniques.

UR - http://www.scopus.com/inward/record.url?scp=0033324997&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0033324997&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0033324997

VL - 6

SP - 4064

EP - 4068

BT - Proceedings of the International Joint Conference on Neural Networks

PB - IEEE

T2 - International Joint Conference on Neural Networks (IJCNN'99)

Y2 - 10 July 1999 through 16 July 1999

ER -

Data-driven theory refinement algorithms for bioinformatics

Abstract

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All Science Journal Classification (ASJC) codes

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