PMC:1459173 / 6896-8426 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/1459173","sourcedb":"PMC","sourceid":"1459173","source_url":"https://www.ncbi.nlm.nih.gov/pmc/1459173","text":"The figure of compression achieved by our algorithm shows good sensitivity in telling apart veritable families of proteins from spurious blends. This sets forth an approach to classification that does away with alignment. The data used for the test consists of protein sequences, which are known to be hardly compressible at all [6]. The experiment reported below uses three different families which were picked at random from the PROSITE repository: AP endonucleases (acnucl), G-protein coupled receptors (gprot) and Succinyl-CoA ligases (succ). Table 2 summarizes the results of lossy and lossless compression for various values of the parameters. The artificial groups are marked \"-mix\", the last column shows the lossless compression ratio of fake over faithful families, when using motifs with the same parameter values. In all cases, the artificial families show compression ratios that are poorer by 10/20%, and the superiority of the lossy variants manifests itself throughout. The experiments thus verify the discrimination potential of data compression by extensible motifs. It seems thus meaningful to build a classifier on top of this measure. Compressibility by extensible motifs may be used to set up a similarity measure on sequences to be used in the inference of phylogeny. The measure could be extended into a metric distance, along the lines of [7]. Specifically, we denote by Off-line(z) the output size obtained when compressing a string z using the lossless variant of our paradigm, and compute the quantity:","tracks":[]}