A novel feature selection method to extract multiple adjacent solutions for viral genomic sequences classification
| Autor: | Fiscon, Giulia, Weitschek, Emanuel, Ciccozzi, Massimo, Bertolazzi, Paola, Felici, Giovanni |
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| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: | |
| Zdroj: | BMC bioinformatics 17 (2016): 207–208. info:cnr-pdr/source/autori:Fiscon, Giulia; Weitschek, Emanuel; Ciccozzi, Massimo; Bertolazzi, Paola; Felici, Giovanni/titolo:A novel feature selection method to extract multiple adjacent solutions for viral genomic sequences classification/doi:/rivista:BMC bioinformatics/anno:2016/pagina_da:207/pagina_a:208/intervallo_pagine:207–208/volume:17 |
| Popis: | Leveraging improvements of next generation technologies, genome sequencing of several samples in different conditions led to an exponential growth of biological sequences. However, these collections are not easily treatable by biologists to obtain a thorough data characterization and require a high cost-time investment. Therefore, computing strategies and specifically automatic knowledge extraction methods that optimize the analysis focusing on what data are meaningful and should be sequenced are essential. |
| Databáze: | OpenAIRE |
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