Towards knowledge-based gene expression data mining.
The field of gene expression data analysis has grown in the past few years from being purely data-centric to integrative, aiming at complementing microarray analysis with data and knowledge from diverse available sources. In this review, we report on the plethora of gene expression data mining techniques and focus on their evolution toward knowledge-based data analysis approaches. In particular, we discuss recent developments in gene expression-based analysis methods used in association and classification studies, phenotyping and reverse engineering of gene networks.
Dipartimento di Informatica e Sistemistica, Università di Pavia, via Ferrata 1, I-27100 Pavia, Italy.
Riccardo
R
Bellazzi
Blaz
B
Zupan
D000465:Algorithms; D001185:Artificial Intelligence; D003628:Database Management Systems; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D005810:Multigene Family / Q000502:physiology*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
D020543:Proteome