Effectiveness of Information Extraction, Multi-Relational, and Semi-Supervised Learning for Predicting Functional Properties of Genes

I
ICDM
2003
Third IEEE International Conference on Data Mining (ICDM'03)
Abstract - Effectiveness of Information Extraction, Multi-Relational, and Semi-Supervised Learning for Predicting Functional Properties of Genes

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	Similar Articles Articles by Mark-A. Krogel Articles by Tobias Scheffer

Third IEEE International Conference on Data Mining (ICDM'03)

Melbourne, Florida

November 19-November 22

ISBN: 0-7695-1978-4

	ASCII Text	x
	Mark-A. Krogel, Tobias Scheffer, "Effectiveness of Information Extraction, Multi-Relational, and Semi-Supervised Learning for Predicting Functional Properties of Genes," Data Mining, IEEE International Conference on, pp. 569, Third IEEE International Conference on Data Mining (ICDM'03), 2003.

	BibTex	x
	@article{ 10.1109/ICDM.2003.1250979, author = {Mark-A. Krogel and Tobias Scheffer}, title = {Effectiveness of Information Extraction, Multi-Relational, and Semi-Supervised Learning for Predicting Functional Properties of Genes}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2003}, isbn = {0-7695-1978-4}, pages = {569}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1250979}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - Effectiveness of Information Extraction, Multi-Relational, and Semi-Supervised Learning for Predicting Functional Properties of Genes SN - 0-7695-1978-4 SP EP A1 - Mark-A. Krogel, A1 - Tobias Scheffer, PY - 2003 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

Mark-A. Krogel, University of Magdeburg, Germany

Tobias Scheffer, Humboldt University, Berlin, Germany

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1250979

We focus on the problem of predicting functional properties of the proteins corresponding to genes in the yeast genome. Our goal is to study the effectiveness of approaches that utilize all data sources that are available in this problem setting, including unlabeled and relational data, and abstracts of research papers. We study transduction and co-training for using unlabeled data. We investigate a propositionalization approach which uses relational gene interaction data. We study the benefit of information extraction for utilizing a collection of scientific abstracts. The studied tasks are KDD Cup tasks of 2001 and 2002. The solutions which we describe achieved the highest score for task 2 in 2001, the fourth rank for task 3 in 2001, the highest score for one of the two subtasks and the third place for the overall task 2 in 2002.

Citation:

Mark-A. Krogel, Tobias Scheffer, "Effectiveness of Information Extraction, Multi-Relational, and Semi-Supervised Learning for Predicting Functional Properties of Genes," icdm, pp.569, Third IEEE International Conference on Data Mining (ICDM'03), 2003

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