LSL: A new measure to evaluate triclusters

Author

Gutierrez-Aviles, David ; Rubio-Escudero, Cristina

Author_Institution

Dept. of Comput. Sci., Univ. of Seville, Seville, Spain

fYear

2014

fDate

2-5 Nov. 2014

Firstpage

30

Lastpage

37

Abstract

Microarray technology has led to a great advance in biological studies due to its ability to monitorize the RNA levels of a vast amount of genes under certain experimental conditions. The use of computational techniques to mine hidden knowledge from these data is of great interest in research fields such as Data Mining and Bioinformatics. Finding patterns of genetic behavior not only taking into account the experimental conditions but also the time condition is a very challenging task nowadays. Clustering, biclustering and novel triclustering techniques offer a very suitable framework to solve the suggested problem. In this work we present LSL, a measure to evaluate the quality of triclusters found in 3D data.

Keywords

RNA; bioinformatics; data mining; genetics; genomics; lab-on-a-chip; RNA level monitoring; biclustering techniques; bioinformatics; computational techniques; data mining; genetic behavior; hidden knowledge mining; least square line; microarray technology; triclustering techniques; Correlation; Equations; Graphics; Mathematical model; Sociology; Trigeneration; behavior patterns; genetic algorithms; least square line; microarray data; triclustering;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioinformatics and Biomedicine (BIBM), 2014 IEEE International Conference on

Conference_Location

Belfast

Type

conf

DOI

10.1109/BIBM.2014.6999244

Filename

6999244