Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Gökmen Altay"'
Publikováno v:
Evolutionary Bioinformatics, Vol 2014, Iss 10, Pp 1-9 (2014)
Externí odkaz:
https://doaj.org/article/39cf0194bf154877a8778c49b6bbad0d
BackgroundGene network inference (GNI) methods have the potential to reveal functional relationships between different genes and their products. Most GNI algorithms have been developed for microarray gene expression datasets and their application to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1f405b29b0e8242ca34504bd6d074868
https://doi.org/10.1101/2023.01.02.522518
https://doi.org/10.1101/2023.01.02.522518
Autor:
Cecilia S. Lindestam Arlehamn, Ian T. Mathews, Alexander Y. Andreyev, Gökmen Altay, Alessandro Sette, Yulia Kushnareva, Sonia Sharma, Mohit Jain, Bjoern Peters, Vijayanand Pandurangan, Roland Nilsson
Publikováno v:
J Immunol
CCR6+CXCR3+CCR4−CD4+ memory T cells, termed Th1*, are important for long-term immunity to Mycobacterium tuberculosis and the pathogenesis of autoimmune diseases. Th1* cells express a unique set of lineage-specific transcription factors characterist
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::12eeecddd298c6facc386a2f29a6b3e9
https://doi.org/10.4049/jimmunol.2000672
https://doi.org/10.4049/jimmunol.2000672
Autor:
Gökmen Altay
In this study, we first present a Tensorflow based Deep Learning (DL) model that provides high performances in predicting the binding of peptides to major histocompatibility complex (MHC) class I protein. Second, we provide the necessary Python codes
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::827bf082db91f3d180bb3295f1c89ab6
We present an R software package that performs at genome-wide level differential network analysis and infers only disease-specific molecular interactions between two different cell conditions. This helps revealing the disease mechanism and predicting
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::92fde1ee8120a8d8cfddd7e130a74197
https://doi.org/10.1101/129742
https://doi.org/10.1101/129742
Gene network inference algorithms (GNI) are popular in bioinformatics area. In almost all GNI algorithms, the main process is to estimate the dependency (association) scores among the genes of the dataset.We present a bioinformatics tool, DepEst (Dep
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b73a50f16d6cf8ac4f044187063fc3f0
https://doi.org/10.1101/102871
https://doi.org/10.1101/102871
Publikováno v:
Bioinformatics. 30:2142-2149
Motivation: Gene network inference (GNI) algorithms enable the researchers to explore the interactions among the genes and gene products by revealing these interactions. The principal process of the GNI algorithms is to obtain the association scores
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::35f22694f3497299335e2ba66ffc8562
https://doi.org/10.1093/bioinformatics/btu182
https://doi.org/10.1093/bioinformatics/btu182
Autor:
Graham McVicker, Gökmen Altay, Brendan Ha, Bjoern Peters, Jose Zapardiel-Gonzalo, Mitchell Kronenberg, Grégory Seumois, Jason A. Greenbaum, Anjana Rao, Benjamin J Schmiedel, Divya Singh, Brandie White, Alan G. Valdovino-Gonzalez, Pandurangan Vijayanand, Ariel Madrigal
Publikováno v:
The Journal of Immunology. 202:182.27-182.27
While many genetic variants have been associated with risk for human diseases, how these variants affect gene expression in various cell types remains largely unknown. To address this gap, the DICE (database of immune cell expression, expression quan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::853cadaf0872741396caebbb1fcb584e
https://doi.org/10.4049/jimmunol.202.supp.182.27
https://doi.org/10.4049/jimmunol.202.supp.182.27
Autor:
Gökmen, Altay, Onur, Mendi
Publikováno v:
Methods in molecular biology (Clifton, N.J.). 1526
The inference of gene regulatory networks is an important process that contributes to a better understanding of biological and biomedical problems. These networks aim to capture the causal molecular interactions of biological processes and provide va
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::6587d46c151094a3efe7eec74ff0fcee
https://pubmed.ncbi.nlm.nih.gov/27896738
https://pubmed.ncbi.nlm.nih.gov/27896738
Autor:
Gökmen Altay, Onur Mendi
Publikováno v:
Methods in Molecular Biology ISBN: 9781493966110
The inference of gene regulatory networks is an important process that contributes to a better understanding of biological and biomedical problems. These networks aim to capture the causal molecular interactions of biological processes and provide va
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9690de3206d43076e0e8ec86175d7f02
https://doi.org/10.1007/978-1-4939-6613-4_6
https://doi.org/10.1007/978-1-4939-6613-4_6