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Full-Text Articles in Physical Sciences and Mathematics
Parallel Adaptive Algorithms For Sampling Large Scale Networks, Kanimathi Duraisamy
Parallel Adaptive Algorithms For Sampling Large Scale Networks, Kanimathi Duraisamy
Student Work
The study of real-world systems, represented as networks, has important application in many disciplines including social sciences [1], bioinformatics [2] and software engineering [3]. These networks are extremely large, and analyzing them is very expensive. Our research work involves developing parallel graph sampling methods for efficient analysis of gene correlation networks. Our sampling algorithms maintain important structural and informational properties of large unstructured networks. We focus on preserving the relative importance, based on combinatorial metrics, rather than the exact measures. We use a special subgraph technique, based on finding triangles called maximal chordal subgraphs, which maintains the highly connected portions …
The Development Of Parallel Adaptive Sampling Algorithms For Analyzing Biological Networks, Kathryn Dempsey Cooper, Kanimathi Duraisamy, Sanjukta Bhowmick, Hesham Ali
The Development Of Parallel Adaptive Sampling Algorithms For Analyzing Biological Networks, Kathryn Dempsey Cooper, Kanimathi Duraisamy, Sanjukta Bhowmick, Hesham Ali
Interdisciplinary Informatics Faculty Proceedings & Presentations
The availability of biological data in massive scales continues to represent unlimited opportunities as well as great challenges in bioinformatics research. Developing innovative data mining techniques and efficient parallel computational methods to implement them will be crucial in extracting useful knowledge from this raw unprocessed data, such as in discovering significant cellular subsystems from gene correlation networks. In this paper, we present a scalable combinatorial sampling technique, based on identifying maximum chordal subgraphs, that reduces noise from biological correlation networks, thereby making it possible to find biologically relevant clusters from the filtered network. We show how selecting the appropriate filter …