Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Zurab Bzhalava"'
Publikováno v:
BMC Bioinformatics, Vol 19, Iss 1, Pp 1-11 (2018)
Abstract Background Detection of highly divergent or yet unknown viruses from metagenomics sequencing datasets is a major bioinformatics challenge. When human samples are sequenced, a large proportion of assembled contigs are classified as “unknown
Externí odkaz:
https://doaj.org/article/9a4752f23439408093c3101a5ad8d59d
Publikováno v:
PLoS ONE, Vol 14, Iss 9, p e0222271 (2019)
Despite its clinical importance, detection of highly divergent or yet unknown viruses is a major challenge. When human samples are sequenced, conventional alignments classify many assembled contigs as "unknown" since many of the sequences are not sim
Externí odkaz:
https://doaj.org/article/06fc58a675884fbea7c811de7e833f3b
Publikováno v:
PLoS ONE, Vol 13, Iss 1, p e0190938 (2018)
When human samples are sequenced, many assembled contigs are "unknown", as conventional alignments find no similarity to known sequences. Hidden Markov models (HMM) exploit the positions of specific nucleotides in protein-encoding codons in various m
Externí odkaz:
https://doaj.org/article/8e717056c9954243ba4982f259eb2f37
Autor:
Laila Sara Arroyo Mühr, Maria Hortlund, Zurab Bzhalava, Sara Nordqvist Kleppe, Davit Bzhalava, Emilie Hultin, Joakim Dillner
Publikováno v:
PLoS ONE, Vol 12, Iss 3, p e0172308 (2017)
Studies investigating presence of viruses in cancer often analyze case series of cancers, resulting in detection of many viruses that are not etiologically linked to the tumors where they are found. The incidence of virus-associated cancers is greatl
Externí odkaz:
https://doaj.org/article/8f70084eb2d341b0942ba2a4e632f63f
Publikováno v:
Vaccine. 38:4066-4070
Some head and neck cancers are caused by human papillomavirus (HPV). As HPV vaccination can prevent infection, an estimation of which HPV types have an active viral oncogene transcription in what proportion of tumors might allow estimation of the pro
Autor:
Sven Törnberg, Karin Sundström, Agneta Carlsten Thor, Klara Miriam Elfström, Zurab Bzhalava, Carina Eklund, Sonia Andersson, Daniel Öhman, Joakim Dillner, Zohra Gzoul, Helena Lamin
Publikováno v:
International Journal of Cancer. 145:3033-3039
High screening participation in the population is essential for optimal prevention of cervical cancer. Offering a high-risk human papillomavirus (HPV) self-test has previously been shown to increase participation. In this randomized health services s
Publikováno v:
Bioinformatics. 34(6):928-935
Motivation Next Generation Sequencing (NGS) technology enables identification of microbial genomes from massive amount of human microbiomes more rapidly and cheaper than ever before. However, the traditional sequential genome analysis algorithms, too
Autor:
Sara Nordqvist Kleppe, Maria Hortlund, Joakim Dillner, Emilie Hultin, Davit Bzhalava, Zurab Bzhalava, Laila Sara Arroyo Mühr, Camilla Lagheden
Publikováno v:
International Journal of Cancer. 141:2498-2504
Most cancer forms known to be caused by viruses are increased among the immunosuppressed, but several cancer forms without established viral etiology are also increased, notably nonmelanoma skin carcinoma (NMSC). We followed all 13,429 solid organ tr
Autor:
Ágúst Ingi Ágústsson, Maiju Pankakoski, Joakim Dillner, Ahti Anttila, Stefan Lönnberg, Svanhildur Thorsteinsdottir, Veli-Matti Partanen, Gry Baadstrand Skare, Ameli Tropé, Piret Veerus, Klara Miriam Elfström, Zurab Bzhalava, Tytti Sarkeala, Liisa Koreinik, Sirpa Heinävaara
Introduction: Quality assurance and improvement of cancer screening programs require up-to-date monitoring systems and evidence-based indicators. National quality reports exist but the definition and calculation of indicators vary making comparisons
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6f9f842d2d884129560a2129cefbaa00
Publikováno v:
PLoS ONE
PLoS ONE, Vol 13, Iss 1, p e0190938 (2018)
PLoS ONE, Vol 13, Iss 1, p e0190938 (2018)
When human samples are sequenced, many assembled contigs are “unknown”, as conventional alignments find no similarity to known sequences. Hidden Markov models (HMM) exploit the positions of specific nucleotides in protein-encoding codons in vario