Detection of functional and topological modules in protein interaction networks

Mapping the complete set of protein and gene interactions in the human cell has been a goal of the biological community for nearly two decades, since the first human genome was sequenced. To this end, computational approaches have been studied in depth to allow functional annotation of protein inter...

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Main Author: Ranjan, Bobby
Other Authors: Jagath C. Rajapakse
Format: Final Year Project
Language:English
Published: 2018
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Online Access:http://hdl.handle.net/10356/73997
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-739972023-03-03T20:36:02Z Detection of functional and topological modules in protein interaction networks Ranjan, Bobby Jagath C. Rajapakse School of Computer Science and Engineering Bioinformatics Research Centre DRNTU::Engineering::Computer science and engineering Mapping the complete set of protein and gene interactions in the human cell has been a goal of the biological community for nearly two decades, since the first human genome was sequenced. To this end, computational approaches have been studied in depth to allow functional annotation of protein interactions. In this project, we explored the potential of using four common module detection algorithms - stochastic block model, Louvain method, modified Louvain (incremental Louvain) method and link community - in order to detect functional modules of for protein interaction networks. We implemented these algorithms in a Cytoscape application for users to run on their respective networks. Using this application, we conducted a comparative study of the algorithms to understand their applicability in protein function annotation and determine how close topological modules of protein-interaction networks are to their functional modules. Bachelor of Engineering (Computer Engineering) 2018-04-23T05:03:25Z 2018-04-23T05:03:25Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/73997 en Nanyang Technological University 50 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering
spellingShingle DRNTU::Engineering::Computer science and engineering
Ranjan, Bobby
Detection of functional and topological modules in protein interaction networks
description Mapping the complete set of protein and gene interactions in the human cell has been a goal of the biological community for nearly two decades, since the first human genome was sequenced. To this end, computational approaches have been studied in depth to allow functional annotation of protein interactions. In this project, we explored the potential of using four common module detection algorithms - stochastic block model, Louvain method, modified Louvain (incremental Louvain) method and link community - in order to detect functional modules of for protein interaction networks. We implemented these algorithms in a Cytoscape application for users to run on their respective networks. Using this application, we conducted a comparative study of the algorithms to understand their applicability in protein function annotation and determine how close topological modules of protein-interaction networks are to their functional modules.
author2 Jagath C. Rajapakse
author_facet Jagath C. Rajapakse
Ranjan, Bobby
format Final Year Project
author Ranjan, Bobby
author_sort Ranjan, Bobby
title Detection of functional and topological modules in protein interaction networks
title_short Detection of functional and topological modules in protein interaction networks
title_full Detection of functional and topological modules in protein interaction networks
title_fullStr Detection of functional and topological modules in protein interaction networks
title_full_unstemmed Detection of functional and topological modules in protein interaction networks
title_sort detection of functional and topological modules in protein interaction networks
publishDate 2018
url http://hdl.handle.net/10356/73997
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