Zobrazeno 1 - 6
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pro vyhledávání: '"Richard C. Tillquist"'
Publikováno v:
Discrete Applied Mathematics. 320:150-169
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1105cde0232acda570f3bd93f57e41a4
https://doi.org/10.1016/j.dam.2022.04.021
https://doi.org/10.1016/j.dam.2022.04.021
Autor:
Richard C. Tillquist
Publikováno v:
BCB
Systems of interest in bioinformatics and computational biology tend to be large, complex, interdependent, and stochastic. As our ability to collect sequence data at finer resolutions improves, we can better understand and predict system behavior und
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::19a6630f37dae7990c7d010b3586755e
https://doi.org/10.1145/3307339.3343174
https://doi.org/10.1145/3307339.3343174
Publikováno v:
BCB
The symbolic nature of biological sequence data greatly complicates its analysis. As many of the most powerful, widely used analysis techniques work exclusively in the context of Euclidean spaces, methods of embedding DNA, RNA, and protein sequences
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::54f89a0dfa9158ac11a56113d0dfebc2
https://doi.org/10.1145/3307339.3342620
https://doi.org/10.1145/3307339.3342620
A subset of vertices in a graph is called resolving when the geodesic distances to those vertices uniquely distinguish every vertex in the graph. Here, we characterize the resolvability of Hamming graphs in terms of a constrained linear system and de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7541f3b739dc7079e9401b43d7519567
http://arxiv.org/abs/1907.05974
http://arxiv.org/abs/1907.05974
In this manuscript, we provide a concise review of the concept of metric dimension for both deterministic as well as random graphs. Algorithms to approximate this quantity, as well as potential applications, are also reviewed. This work has been part
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9a21d55cc94373a90ad8a037cd7e3027
Publikováno v:
BCB
Many machine learning techniques such as k-nearest neighbors (KNNs) and support vector machines (SVMs) require examples to be mapped to numerical feature vectors. Principal coordinate analysis (PCoA) accomplishes this by mapping a set of n examples t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6b5d0c1acca204dea1b1c087dbf783c0
https://doi.org/10.1145/2975167.2985641
https://doi.org/10.1145/2975167.2985641