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- W2886756553 abstract "In this work, we propose the use of measures based on link analysis algorithms like the Hyperlink-Induced Topic Search (HITS) or PageRank for analyzing the properties of connectomes. While PageRank has been an algorithm that was utilized in past work and is known to be very informative, the HITS algorithm has not seen much application. To our knowledge, this works represents the first major study utilizing HITS and PageRank for use on the connectome of the C.Elegans worm. Our work shows that the hubness and authority values, along with PageRank values, can produce a predictive model of the functional role of neurons, which is better than using many other topological measures. We show that hubness and authority, despite its similarity to PageRank, produce much different information about the connectome. We also propose the use of a new measure, called the hub-authority ratio, as a way to look at the data from a different perspective. Our experimental results show that including this feature increases the predictive capability of the functional role classification model. We believe this has the potential for major application to the analysis of other connectomes, including humans." @default.
- W2886756553 created "2018-08-22" @default.
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- W2886756553 date "2018-05-01" @default.
- W2886756553 modified "2023-09-25" @default.
- W2886756553 title "Using Link Analysis Algorithms to Study the Role of Neurons in the Worm Connectome" @default.
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- W2886756553 doi "https://doi.org/10.1109/aina.2018.00100" @default.
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