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Activity Number: 440 - SLDS CSpeed 8
Type: Contributed
Date/Time: Thursday, August 12, 2021 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #318340
Title: Novel Entropy-Based Criterion in the Selection of Clusters of a Biological Network Structure
Author(s): Gul Bahar Bulbul*
Companies: BGSU
Keywords: radial basis function; entropy-based criterion; kernel trick; biological network structure; unsupervised learning; convex clustering
Abstract:

To construct a high dimensional biological network whose known for their complexity, several supervised learning methods have been proposed in the field of statistics. In fact, the problem arises when dealing with biological network structures in which nonconvex structures might reflect the true relations of the components rather than convex forms so that unsupervised learning procedures can outperform their supervised counterparts. In this study, it is aimed to show how an unsupervised learning technique can produce a correct biological network structure. In particular, it is done by constructing a dissimilarity matrix driven by a kernel-based strategy that implies using the automatically enlarged feature space rather than the original version of it. Furthermore, it is proposed to be applied entropy-based distance criterion in the selection of clusters unlike the generic methods that are known as radial basis techniques in order to capture nonconvex clusters. To conclude, the aim of this study is to find suitable dissimilarity matrix by an unsupervised way, then to detect the correct biological network structure with its correct clusters by a proposed entropy based criterion.


Authors who are presenting talks have a * after their name.

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