Graph-Theory Based Simplification Techniques for Efficient Biological Network Analysis
| Autor: | Euiseong Ko, Donghyun Kim, Hyung Jae Chang, Mingon Kang |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Connected component Biological data Computer science business.industry 0206 medical engineering Gene regulatory network Context (language use) Graph theory 02 engineering and technology Machine learning computer.software_genre 03 medical and health sciences 030104 developmental biology Gene expression Algorithm design Artificial intelligence Data mining Greedy algorithm business computer 020602 bioinformatics Biological network |
| Zdroj: | BigDataService |
| DOI: | 10.1109/bigdataservice.2017.39 |
| Popis: | The recent years have witnessed the remarkable expansion of publicly available biological data in the related research fields. Many researches in these fields often require massive data to be analyzed by utilizing high-throughput sequencing technologies. However, it is very challenging to interpret the data efficiently due to it high complexity. This paper introduces two new graph algorithms which aim to improve the efficiency of the existing methods for biological network data interpretation. In particular, the algorithms focus on the problem of how to simplify gene regulatory networks so that many existing algorithms can efficiently discover important connected components of a biological system in their own context as many times as they need. The performance of the proposed algorithms is compared with each other with gene expression data of glioblastoma brain tumor cancer. |
| Databáze: | OpenAIRE |
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