Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Minoo Aminian"'
Autor:
Louis Grandjean, Tomotada Iwamoto, Anna Lithgow, Robert H Gilman, Kentaro Arikawa, Noriko Nakanishi, Laura Martin, Edith Castillo, Valentina Alarcon, Jorge Coronel, Walter Solano, Minoo Aminian, Claudia Guezala, Nalin Rastogi, David Couvin, Patricia Sheen, Mirko Zimic, David A J Moore
Publikováno v:
PLoS ONE, Vol 10, Iss 5, p e0126271 (2015)
The comparison of Mycobacterium tuberculosis bacterial genotypes with phenotypic, demographic, geospatial and clinical data improves our understanding of how strain lineage influences the development of drug-resistance and the spread of tuberculosis.
Externí odkaz:
https://doaj.org/article/3a1ae4f1003944d188632b43a07f1f2e
Autor:
Kristin P. Bennett, Minoo Aminian, David Couvin, Nalin Rastogi, Scott Vandenberg, Amina Shabbeer, Kane Hadley
Publikováno v:
BioMed Research International, Vol 2014 (2014)
BioMed Research International
BioMed Research International
We develop a novel approach for incorporating expert rules into Bayesian networks for classification ofMycobacterium tuberculosiscomplex (MTBC) clades. The proposed knowledge-based Bayesian network (KBBN) treats sets of expert rules as prior distribu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5d0625107de0a909869056c697c0bc89
Autor:
Kane Hadley, Cagri Ozcaglar, Amina Shabbeer, Scott Vandenberg, Minoo Aminian, Kristin P. Bennett
Publikováno v:
BCB
We develop a novel knowledge-based Bayesian network (KBBN) that models our knowledge of the Mycobacterium tuberculosis complex (MTBC) obtained from expert-defined rules and large DNA fingerprint databases to classify strains of MTBC into fifty-one ge
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9a7832b476e59846904cd6b7f39de759
https://doi.org/10.1145/2147805.2147827
https://doi.org/10.1145/2147805.2147827
Publikováno v:
BIBM
—We present a novel Bayesian network (BN) to classify strains of Mycobacterium tuberculosis Complex (MTBC) into six major genetic lineages using mycobacterial interspersed repetitive units (MIRUs), a high-throughput biomarker. MTBC is the causative
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6c4fbad08000a49a3ff9c7016a250713
https://pubmed.ncbi.nlm.nih.gov/20953280
https://pubmed.ncbi.nlm.nih.gov/20953280
Autor:
Minoo Aminian
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783540228813
IDEAL
IDEAL
We study classification when the majority of data is unlabeled, and only a small fraction is labeled: the so-called semi-supervised learning situation. Blum and Mitchell’s co-training is a popular semi-supervised algorithm [1] to use when we have m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::119c8dc7aa1a958eef314f1356338f62
https://doi.org/10.1007/978-3-540-28651-6_114
https://doi.org/10.1007/978-3-540-28651-6_114
Publikováno v:
BMC Bioinformatics
Background We present a novel conformal Bayesian network (CBN) to classify strains of Mycobacterium tuberculosis Complex (MTBC) into six major genetic lineages based on two high-throuput biomarkers: mycobacterial interspersed repetitive units (MIRU)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fb869d9ab051c04aeb9b2b4e4ad55b2c
http://europepmc.org/articles/PMC2863063
http://europepmc.org/articles/PMC2863063