@article{fdi:010079273,
  title = {{F}eature construction from synergic pairs to improve microarray-based classification},
  author = {{H}anczar, {B}. and {Z}ucker, {J}ean-{D}aniel and {H}enegar, {C}. and {S}aitta , {L}.},
  editor = {},
  language = {{ENG}},
  abstract = {{M}otivation :{M}icroarray  experiments  that  allow simultaneous expression profiling of thousands of genes in various conditions (tissues, cells or time) generate data whose analysis raises difficult problems. {I}n particular, there is a vast disproportion between the number of attributes (tens of thousands) and the number of examples (several tens). {D}imension reduction is therefore a keystep before applying classification approaches. {M}any methods have been proposed to this purpose, but only a few of them considered a direct quantification of transcriptional interactions. {W}e describe and experimentally validate a new dimension reduction and feature construction method, which assesses interactions between expression profiles to improve microarray-based classification accuracy. {R}esults : {O}ur approach relies on a mutual information measure that exposes some elementary constituents of the information contained in a pair of gene expression profiles. {W}e show that their analysis implies a term that represents the information of the interaction between the two genes. {T}he principle of our method, called {F}eat{KNN}, is to exploit the information provided by highly synergic gene pairs to improve classification accuracy. {F}irst, a heuristic search selects the most informative gene pairs. {T}hen, for each selected pair, a new feature, representing the classification margin of a {KNN} classifier in the gene pairs space, is constructed. {W}e show experimentally that the interactional information has a degree of significance comparable to that of the gene expression profiles considered separately. {O}ur method has been tested with different classifiers and yielded significant improvements in accuracy on several public micro array databases. {M}oreover, a synthetic assessment of the biological significance of the concept of synergic gene pairs suggested its ability to uncover relevant mechanisms underlying interactions among various cellular processes.},
  keywords = {},
  booktitle = {},
  journal = {{B}ioinformatics},
  volume = {23},
  numero = {21},
  pages = {2866--2872},
  ISSN = {1367-4803},
  year = {2007},
  DOI = {10.1093/bioinformatics/btm429},
  URL = {https://www.documentation.ird.fr/hor/fdi:010079273},
}