Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Amira Djebbari"'
Autor:
Vladimir Gianullin, Leonardo Hagmann, Kevin Arvai, Amira Djebbari, Christopher L. Nobles, Larson Hogstrom, Mael Manesse, Vuna Fa, Fanglei Zhuang, Xi Chen, Viatcheslav E. Katerov, Jorge Garces, Hatim T. Allawi, Abigail McElhinny, Frank Diehl, Gustavo C Cerqueira
Publikováno v:
Cancer Prevention Research. 16:P041-P041
Background: A multi-analyte blood test has the potential to maximize performance for early detection across different cancer stages and types. Improvements in early-stage cancer detection might be achieved using multi-component tests with high sensit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fce1736ce024516cb4412a95e9443bc5
https://doi.org/10.1158/1940-6215.precprev22-p041
https://doi.org/10.1158/1940-6215.precprev22-p041
Autor:
Vladimir Gianullin, Leonardo Hagmann, Kevin Arvai, Amira Djebbari, Christopher L. Nobles, Larson Hogstrom, Mael Manesse, Vuna Fa, Fanglei Zhuang, Xi Chen, Viatcheslav E. Katerov, Jorge Garces, Hatim T. Allawi, Abigail McElhinny, Frank Diehl, Gustavo C Cerqueira
Publikováno v:
Cancer Prevention Research. 16:IA023-IA023
Background: A multi-analyte blood test has the potential to maximize performance for early detection across different cancer stages and types. Improvements in early-stage cancer detection might be achieved using multi-component tests with high sensit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2becf25f5b81158c46f97472d5580b6c
https://doi.org/10.1158/1940-6215.precprev22-ia023
https://doi.org/10.1158/1940-6215.precprev22-ia023
Autor:
Fadi Towfic, Joel S. Parker, Amira Djebbari, Alberto Risueño, Celia Fontanillo, Suzana Couto, Anita Gandhi, Matthew William Burnell Trotter, Chung-Wein Lee, Patrick Hagner, Matthew J. Maurer, Michael Pourdehnad, Yan Ren, Grzegorz S. Nowakowski, Maria Wang, Clifton Drew, Xin Wei, James R. Cerhan
Publikováno v:
Blood. 135(13)
Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease, commonly described by cell-of-origin (COO) molecular subtypes. We sought to identify novel patient subgroups through an unsupervised analysis of a large public dataset of gene expressi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7a80d2199e362e63de26bd0ebb054f6f
https://pubmed.ncbi.nlm.nih.gov/31977005
https://pubmed.ncbi.nlm.nih.gov/31977005
Publikováno v:
Bioinformatics. 21:3324-3326
Summary: MeSHer uses a simple statistical approach to identify biological concepts in the form of Medical Subject Headings (MeSH terms) obtained from the PubMed database that are significantly overrepresented within the identified gene set relative t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e518b34a41f0826c08b2a9c89ad53db4
https://doi.org/10.1093/bioinformatics/bti503
https://doi.org/10.1093/bioinformatics/bti503
Autor:
Amira Djebbari, Catharina Olsen, John Quackenbush, Gianluca Bontempi, Christopher Bouton, Benjamin Haibe-Kains, Mick Correll
Publikováno v:
Nucleic acids research, 40 (Database issue
Nucleic Acids Research
Nucleic Acids Research
Genomics provided us with an unprecedented quantity of data on the genes that are activated or repressed in a wide range of phenotypes. We have increasingly come to recognize that defining the networks and pathways underlying these phenotypes require
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4581731941b691eb85d31364f7b9be6a
http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/145205
http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/145205
Autor:
Kristen Fortney, David Otasek, Igor Jurisica, Muhammad Ali, Serene Wong, Max Kotlyar, Anthony Hrvojic, Amira Djebbari
Publikováno v:
Internet Math. 7, no. 4 (2011), 314-347
Network visualization tools offer features enabling a variety of analyses to satisfy diverse requirements. Considering complexity and diversity of data and tasks, there is no single best layout, no single best file format or visualization tool: one s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fe59c1057be98890f6c3e4242412d646
http://projecteuclid.org/euclid.im/1323367283
http://projecteuclid.org/euclid.im/1323367283
Autor:
Aurélie Labbe, Amira Djebbari
Publikováno v:
BMC Bioinformatics
BMC Bioinformatics, Vol 10, Iss 1, p 410 (2009)
BMC Bioinformatics, Vol 10, Iss 1, p 410 (2009)
Background In high density arrays, the identification of relevant genes for disease classification is complicated by not only the curse of dimensionality but also the highly correlated nature of the array data. In this paper, we are interested in the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::026a1c30fb919b2f9f63d52b1a9c5c23
https://nrc-publications.canada.ca/eng/view/object/?id=bea02f18-9141-4780-bd9f-93ee97bfebe1
https://nrc-publications.canada.ca/eng/view/object/?id=bea02f18-9141-4780-bd9f-93ee97bfebe1
Publikováno v:
Encyclopedia of Artificial Intelligence
Biological systems can be viewed as information management systems, with a basic instruction set stored in each cell’s DNA as “genes.” For most genes, their information is enabled when they are transcribed into RNA which is subsequently transla
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::52f8c9ba0ee8aca7ae08107dd34bb11f
https://doi.org/10.4018/978-1-59904-849-9.ch010
https://doi.org/10.4018/978-1-59904-849-9.ch010
Autor:
John Quackenbush, Amira Djebbari
Publikováno v:
BMC Systems Biology
BMC Systems Biology, Vol 2, Iss 1, p 57 (2008)
BMC Systems Biology, Vol 2, Iss 1, p 57 (2008)
Background DNA microarrays and other genomics-inspired technologies provide large datasets that often include hidden patterns of correlation between genes reflecting the complex processes that underlie cellular metabolism and physiology. The challeng
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::523c76b5f6d23bf5e5abd645c38fc951
https://doi.org/10.1186/1752-0509-2-57
https://doi.org/10.1186/1752-0509-2-57
Current breast cancer predictive signatures are not unique. Can we use this fact to our advantage to improve prediction? From the machine learning perspective, it is well known that combining multiple classifiers can improve classification performanc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::296cf3cbaa43adf0947c32d799aaa93c
https://nrc-publications.canada.ca/eng/view/object/?id=8144431a-396f-4818-b3f7-9eaf614d1011
https://nrc-publications.canada.ca/eng/view/object/?id=8144431a-396f-4818-b3f7-9eaf614d1011