How to infer gene networks from expression profiles
Inferring, or ‘reverse‐engineering’, gene networks can be defined as the process of identifying gene interactions from experimental data through computational analysis. Gene expression data from microarrays are typically used for this purpose. Here we compared different reverse‐engineering algorithm...
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| Veröffentlicht in: | Molecular systems biology 2007, Vol.3 (1), p.78-n/a |
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| creator | Bansal, Mukesh Belcastro, Vincenzo Ambesi‐Impiombato, Alberto di Bernardo, Diego |
| description | Inferring, or ‘reverse‐engineering’, gene networks can be defined as the process of identifying gene interactions from experimental data through computational analysis. Gene expression data from microarrays are typically used for this purpose. Here we compared different reverse‐engineering algorithms for which ready‐to‐use software was available and that had been tested on experimental data sets. We show that reverse‐engineering algorithms are indeed able to correctly infer regulatory interactions among genes, at least when one performs perturbation experiments complying with the algorithm requirements. These algorithms are superior to classic clustering algorithms for the purpose of finding regulatory interactions among genes, and, although further improvements are needed, have reached a discreet performance for being practically useful. |
| doi_str_mv | 10.1038/msb4100120 |
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| subjects | Algorithms Clustering Computational Biology Computer applications DNA microarrays EMBO10 EMBO26 Engineering Experimental data Experiments Gene expression Gene Expression Profiling - methods Gene Expression Regulation gene network gene regulation Gene Regulatory Networks Genes Oligonucleotide Array Sequence Analysis Perturbation Proteins reverse-engineering Review Software Systems Biology - methods transcriptional regulation |
| title | How to infer gene networks from expression profiles |
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