Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Suzanne Renick Gallagher"'
Publikováno v:
PLoS Computational Biology, Vol 7, Iss 10, p e1002244 (2011)
Computer science has become ubiquitous in many areas of biological research, yet most high school and even college students are unaware of this. As a result, many college biology majors graduate without adequate computational skills for contemporary
Externí odkaz:
https://doaj.org/article/eb0e9942337743e789113c3143c63e93
Autor:
Ali Sharifi-Zarchi, S. Cenk Sahinalp, Akash Shrestha, Suzanne Renick Gallagher, Ali Ebrahimpour Boroojeny, Hamidreza Chitsaz
Publikováno v:
J Comput Biol
Many problems in applied machine learning deal with graphs (also called networks), including social networks, security, web data mining, protein function prediction, and genome informatics. The kernel paradigm beautifully decouples the learning algor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6d54a846819f8bc70c66e48290134172
https://doi.org/10.1089/cmb.2019.0511
https://doi.org/10.1089/cmb.2019.0511
Autor:
Suzanne Renick Gallagher, Ali Ebrahimpour Boroojeny, Akash Shrestha, Süleyman Cenk Sahinalp, Hamidreza Chitsaz, Ali Sharifi-Zarchi
Publikováno v:
J Comput Biol
Graph Traversal Edit Distance (GTED) is a measure of distance (or dissimilarity) between two graphs introduced. This measure is based on the minimum edit distance between two strings formed by the edge labels of respective Eulerian traversals of the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f861902c5e41f736c68366aa36dd05e
https://pubmed.ncbi.nlm.nih.gov/32160033
https://pubmed.ncbi.nlm.nih.gov/32160033
Autor:
Ali Ebrahimpour Boroojeny, Ali Sharifi-Zarchi, Suzanne Renick Gallagher, S. Cenk Sahinalp, Akash Shrestha, Hamidreza Chitsaz
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783319899282
RECOMB
RECOMB
Many problems in applied machine learning deal with graphs (also called networks), including social networks, security, web data mining, protein function prediction, and genome informatics. The kernel paradigm beautifully decouples the learning algor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c2a3bba6e54c01c1c8f75ebffa3ad2d8
https://doi.org/10.1007/978-3-319-89929-9_3
https://doi.org/10.1007/978-3-319-89929-9_3
Publikováno v:
BCB
The edge (vertex) connectivity of a graph is the minimum number of edges (vertices) that must be removed to disconnect the graph. Connectivity is an important property of a graph but has seldom been used in the study of protein-protein interaction (P
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::85c2f0f4ebbe9a9b15e0034aafc28fe7
https://doi.org/10.1145/2649387.2649416
https://doi.org/10.1145/2649387.2649416
Publikováno v:
BCB
Disease-gene networks are bipartite networks that connect diseases to the genes with which they are associated. Due to the absence of tools to analyze bipartite networks, however, they are usually analyzed by "projecting" the bipartite network into a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::09e759ef664f203c49635281b0a44ed4
https://doi.org/10.1145/2649387.2660817
https://doi.org/10.1145/2649387.2660817
Publikováno v:
BCB
Modeling protein interaction data with graphs (networks) is insufficient for some common types of experimentally generated interaction data. For example, in affinity purification experiments, one protein is pulled out of the cell along with other pro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ae40b4b5248f89eeafaf40ffb868b8bb
https://doi.org/10.1145/2506583.2506635
https://doi.org/10.1145/2506583.2506635
Publikováno v:
F1000Research
Many protein complexes are densely packed, so proteins within complexes often interact with several other proteins in the complex. Steric constraints prevent most proteins from simultaneously binding more than a handful of other proteins, regardless
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a7c362c5ba056252bdbdb965159c6790
https://doi.org/10.12688/f1000research.2-172.v1
https://doi.org/10.12688/f1000research.2-172.v1