Mining Combinatorial Effects on Quantitative Traits from Protein Expression Data

Authors:
Takuya Yoshihiro;Etsuko Inoue;Masaru Nakagawa
Affiliations:
Faculty of Systems Engineering, Wakayama University, 930 Sakaedani Wakayama, 640-8510, Japan. E-mail: {tac,etsuko,nakagawa}@sys.wakayama-u.ac.jp.;Faculty of Systems Engineering, Wakayama University, 930 Sakaedani Wakayama, 640-8510, Japan. E-mail: {tac,etsuko,nakagawa}@sys.wakayama-u.ac.jp.;Faculty of Systems Engineering, Wakayama University, 930 Sakaedani Wakayama, 640-8510, Japan. E-mail: {tac,etsuko,nakagawa}@sys.wakayama-u.ac.jp.
Venue:
Proceedings of the 2008 conference on Knowledge-Based Software Engineering: Proceedings of the Eighth Joint Conference on Knowledge-Based Software Engineering
Year:
2008

Citing 4
Cited 1

Identification of genetic networks by strategic gene disruptions and gene overexpressions under a boolean model

Theoretical Computer Science - Selected papers in honour of Setsuo Arikawa
Bayesian Network and Nonparametric Heteroscedastic Regression for Nonlinear Modeling of Genetic Network

CSB '02 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Accurate Cancer Classification Using Expressions of Very Few Genes

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Interactive Semisupervised Learning for Microarray Analysis

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)

Prediction of combinatorial protein-protein interaction networks from expression data using statistics on conditional probability

KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we proposed a data mining method to find interesting relation among the small number of proteins and a quantitative trait from protein expression data. It is practically important to find small number of proteins which in combination effects on some characteristics of samples because it motivates further experiments to investigate the function of the proteins. Further, treating quantitative trait rather than categorical data is also practically important since considerable part of characteristics of samples is represented by quantitative values. Our data mining method apply discriminant analysis into protein expression data and quantitative trait data to derive interesting relations among them.