Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Mert Erden"'
Autor:
Kapil Devkota, Henri Schmidt, Matt Werenski, James M Murphy, Mert Erden, Victor Arsenescu, Lenore J Cowen
Publikováno v:
Bioinformatics. 38:3395-3406
Motivation Protein function prediction, based on the patterns of connection in a protein–protein interaction (or association) network, is perhaps the most studied of the classical, fundamental inference problems for biological networks. A highly su
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9ea7ef6ae386e649c3d4100ea930c9ac
https://doi.org/10.1093/bioinformatics/btac322
https://doi.org/10.1093/bioinformatics/btac322
Autor:
Mert Erden, Megan Gelement, Sarrah Hakimjee, Kyla Levin, Mary-Joy Sidhom, Kapil Devkota, Lenore J. Cowen
Publikováno v:
Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ac8a2c7cef71d83cbe5c569a6e6df5e3
https://doi.org/10.1145/3535508.3545542
https://doi.org/10.1145/3535508.3545542
Autor:
Polina Shpilker, Lenore J. Cowen, Mert Erden, Victor Arsenescu, Matthew Werenski, Kapil Devkota
Publikováno v:
Bioinformatics Advances. 2
Motivation Leveraging cross-species information in protein function prediction can add significant power to network-based protein function prediction methods, because so much functional information is conserved across at least close scales of evoluti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::36d893d5c948871e275c769352a2aa5a
https://doi.org/10.1093/bioadv/vbab025
https://doi.org/10.1093/bioadv/vbab025
Publikováno v:
BCB
A method to improve protein function prediction for sparsely annotated PPI networks is introduced. The method extends the DSD majority vote algorithm introduced by Cao et al. to give confidence scores on predicted labels and to use predictions of hig
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5b6251dd4cb7514f07b0958ebc3a6a3d
https://pubmed.ncbi.nlm.nih.gov/33591921
https://pubmed.ncbi.nlm.nih.gov/33591921