Publications

@article{Alves2009,

title = {Gene association analysis: a survey of frequent pattern mining from gene expression data},

author = {R. Alves and D. Rodríguez-Baena and J. Aguilar-Ruiz},

url = {https://doi.org/10.1093/bib/bbp042

https://academic.oup.com/bib/article-pdf/11/2/210/573238/bbp042.pdf},

doi = {10.1093/bib/bbp042},

issn = {1467-5463},

year  = {2009},

date = {2009-10-08},

journal = {Briefings in Bioinformatics},

volume = {11},

number = {2},

pages = {210-224},

abstract = {Establishing an association between variables is always of interest in genomic studies. Generation of DNA microarray gene expression data introduces a variety of data analysis issues not encountered in traditional molecular biology or medicine. Frequent pattern mining (FPM) has been applied successfully in business and scientific data for discovering interesting association patterns, and is becoming a promising strategy in microarray gene expression analysis. We review the most relevant FPM strategies, as well as surrounding main issues when devising efficient and practical methods for gene association analysis (GAA). We observed that, so far, scalability achieved by efficient methods does not imply biological soundness of the discovered association patterns, and vice versa. Ideally, GAA should employ a balanced mining model taking into account best practices employed by methods reviewed in this survey. Integrative approaches, in which biological knowledge plays an important role within the mining process, are becoming more reliable.},

keywords = {Gene expression analysis, Pattern recognition},

pubstate = {published},

tppubtype = {article}

}