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Publikováno v:
In Computers and Operations Research 2010 37(8):1419-1426
Publikováno v:
In Computers and Mathematics with Applications 2008 55(5):900-911
Publikováno v:
In Decision Support Systems 2008 45(1):95-109
Autor:
Weitschek Emanuel, Lo Presti Alessandra, Drovandi Guido, Felici Giovanni, Ciccozzi Massimo, Ciotti Marco, Bertolazzi Paola
Publikováno v:
Virology Journal, Vol 9, Iss 1, p 58 (2012)
Abstract Background Differences in genomic sequences are crucial for the classification of viruses into different species. In this work, viral DNA sequences belonging to the human polyomaviruses BKPyV, JCPyV, KIPyV, WUPyV, and MCPyV are analyzed usin
Externí odkaz:
https://doaj.org/article/6bed524196504911a38e5b39b4e79eb1
Publikováno v:
BMC Bioinformatics, Vol 11, Iss 1, p 488 (2010)
Abstract Background A relevant problem in drug design is the comparison and recognition of protein binding sites. Binding sites recognition is generally based on geometry often combined with physico-chemical properties of the site since the conformat
Externí odkaz:
https://doaj.org/article/9433766e4fc243e0ac02276af9a5f2a8
Publikováno v:
In Theoretical Computer Science 2002 270(1):341-359
Publikováno v:
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
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
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=cnr_________::47f06a8915b500f84c51dd23ee0bdcd6
https://publications.cnr.it/doc/441171
https://publications.cnr.it/doc/441171
Publikováno v:
DEXA Workshops
26th International Workshop on Database and Expert Systems Applications-DEXA 2015, pp. 31–35, 2015
info:cnr-pdr/source/autori:Weitschek Emanuel (1,2), Fiscon Giulia (2,3), Felici Giovanni (2), Bertolazzi Paola (2)/titolo:GELA: a software tool for the analysis of gene expression data./titolo_volume:26th International Workshop on Database and Expert Systems Applications-DEXA 2015/curatori_volume:/editore:/anno:2015
26th International Workshop on Database and Expert Systems Applications-DEXA 2015, pp. 31–35, 2015
info:cnr-pdr/source/autori:Weitschek Emanuel (1,2), Fiscon Giulia (2,3), Felici Giovanni (2), Bertolazzi Paola (2)/titolo:GELA: a software tool for the analysis of gene expression data./titolo_volume:26th International Workshop on Database and Expert Systems Applications-DEXA 2015/curatori_volume:/editore:/anno:2015
Leveraging advances in transcriptome profiling technologies (RNA-seq), biomedical scientists are collecting everincreasing gene expression profiles data with low cost and high throughput. Therefore, automatic knowledge extraction methods are becoming
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ad5f2fd42a83b161faf80716d9d765e7
https://doi.org/10.1109/dexa.2015.26
https://doi.org/10.1109/dexa.2015.26
Autor:
Weitschek, Emanuel, Lauro, Silvia Di, Cappelli, Eleonora, Bertolazzi, Paola, Felici, Giovanni
Publikováno v:
BMC Bioinformatics; 10/15/2018, Vol. 19 Issue 10, p245-256, 12p, 4 Color Photographs, 1 Diagram, 7 Charts