Gene interaction networks boost genetic algorithm performance in biomarker discovery

In recent years, the advent of high-throughput techniques led to significant acceleration of biomarker discovery. In the same time, the popularity of machine learning methods grown in the field, mostly due to inherit analytical problems associated with the data resulting from these massively paralle...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM) S. 144 - 149
Hauptverfasser: Moschopoulos, Charalampos, Popovic, Dusan, Langone, Rocco, Suykens, Johan, De Moor, Bart, Moreau, Yves
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.12.2014
Schlagworte:
Biological cells
biomarker discovery
Cancer
Classification algorithms
Gene expression
gene interaction network
genetic algorithm
Genetic algorithms
microarray gene expression datasets
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In recent years, the advent of high-throughput techniques led to significant acceleration of biomarker discovery. In the same time, the popularity of machine learning methods grown in the field, mostly due to inherit analytical problems associated with the data resulting from these massively parallelized experiments. However, learning algorithms are very often utilized in their basic form, hence sometimes failing to consider interactions that are present between biological subjects (i.e. genes). In this context, we propose a new methodology, based on genetic algorithms, that integrates prior information through a novel genetic operator. In this particular application, we rely on a biological knowledge that is captured by the gene interaction networks. We demonstrate the advantageous performance of our method compared to a simple genetic algorithm by testing it on several microarray datasets containing samples of tissue from cancer patients. The obtained results suggest that inclusion of biological knowledge into genetic algorithm in the form of this operator can boost its effectiveness in the biomarker discovery problem.
DOI:10.1109/MCDM.2014.7007200