Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Sugeerth Murugesan"'
Autor:
Sugeerth Murugesan, Kristofer Bouchard, Edward Chang, Max Dougherty, Bernd Hamann, Gunther H. Weber
Publikováno v:
BMC Bioinformatics, Vol 18, Iss S6, Pp 1-15 (2017)
Abstract Background There exists a need for effective and easy-to-use software tools supporting the analysis of complex Electrocorticography (ECoG) data. Understanding how epileptic seizures develop or identifying diagnostic indicators for neurologic
Externí odkaz:
https://doaj.org/article/7e947c30d7ac4b0fa75f07c066eb85fe
Publikováno v:
Cluster Computing, vol 25, iss 4
Understanding flow traffic patterns in networks, such as the Internet or service provider networks, is crucial to improving their design and building them robustly. However, as networks grow and become more complex, it is increasingly cumbersome and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0a912c0f8df41b6968dc98978e2554a4
https://escholarship.org/uc/item/8nz5w51s
https://escholarship.org/uc/item/8nz5w51s
Autor:
Kristofer E. Bouchard, Mariam Kiran, Jesse A. Brown, Bernd Hamann, Gunther H. Weber, Sugeerth Murugesan, Daniel J. Lurie
Publikováno v:
Information Visualization. 19:96-113
We introduce an approach for the interactive visual analysis of weighted, dynamic networks. These networks arise in areas such as computational neuroscience, sociology, and biology. Network analysis remains challenging due to complex time-varying net
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e7389509e05f85cb16c743a8d3432747
https://doi.org/10.1177/1473871619882019
https://doi.org/10.1177/1473871619882019
Publikováno v:
IEEE computer graphics and applications. 39(5)
Deep learning models have become the state-of-the-art for many tasks, from text sentiment analysis to facial image recognition. However, understanding why certain models perform better than others or how one model learns differently than another is o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8801366b52984e01c9c799342ca97e8b
https://pubmed.ncbi.nlm.nih.gov/31144628
https://pubmed.ncbi.nlm.nih.gov/31144628
Autor:
Andrew Trujillo, Kristopher Bouchard, Gunther H. Weber, Bernd Hamann, Sugeerth Murugesan, Jesse A. Brown, William W. Seeley
Publikováno v:
IEEE/ACM transactions on computational biology and bioinformatics, vol 14, iss 4
Murugesan, S; Bouchard, K; Brown, JA; Hamann, B; Seeley, WW; Trujillo, A; et al.(2017). Brain Modulyzer: Interactive Visual Analysis of Functional Brain Connectivity. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 14(4), 805-818. doi: 10.1109/TCBB.2016.2564970. Lawrence Berkeley National Laboratory: Retrieved from: http://www.escholarship.org/uc/item/3xt3702n
Murugesan, S; Bouchard, K; Brown, JA; Hamann, B; Seeley, WW; Trujillo, A; et al.(2017). Brain Modulyzer: Interactive Visual Analysis of Functional Brain Connectivity. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 14(4), 805-818. doi: 10.1109/TCBB.2016.2564970. Lawrence Berkeley National Laboratory: Retrieved from: http://www.escholarship.org/uc/item/3xt3702n
© 2017 IEEE. We present Brain Modulyzer, an interactive visual exploration tool for functional magnetic resonance imaging (fMRI) brain scans, aimed at analyzing the correlation between different brain regions when resting or when performing mental t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::28111c1fea69b5af378b50cf6bc02442
https://escholarship.org/uc/item/3xt3702n
https://escholarship.org/uc/item/3xt3702n
Autor:
Bernd Hamann, Kristofer E. Bouchard, Sugeerth Murugesan, Edward F. Chang, Max Dougherty, Gunther H. Weber
Publikováno v:
Murugesan, S; Bouchard, K; Chang, E; Dougherty, M; Hamann, B; & Weber, GH. (2016). Hierarchical spatio-temporal visual analysis of cluster evolution in electrocorticography data. ACM-BCB 2016-7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, 630-639. doi: 10.1145/2975167.2985688. Lawrence Berkeley National Laboratory: Retrieved from: http://www.escholarship.org/uc/item/8sx7v5k6
BCB
BCB
Copyright 2016 ACM. We present ECoG ClusterFlow, a novel interactive visual analysis tool for the exploration of high-resolution Electrocorticography (ECoG) data. Our system detects and visualizes dynamic high-level structures, such as communities, u
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4a2bd57de4af5f3aa739fda70724a745
http://www.escholarship.org/uc/item/8sx7v5k6
http://www.escholarship.org/uc/item/8sx7v5k6