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An Extension to Local Network Alignment using Hidden Markov Models (HMMs)Topics: Data Mining and Machine Learning; Pattern Recognition, Clustering and Classification; Sequence Analysis

Keyword(s):Biological Networks, Local Network Alignment, Hidden Markov Models.

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Ontology
Subjects/Areas/Topics:Bioinformatics
;
Biomedical Engineering
;
Data Mining and Machine Learning
;
Pattern Recognition, Clustering and Classification
;
Sequence Analysis

Abstract: Local alignment is done on biological networks to find common conserved substructures belonging to different organisms. Many algorithms such as PathBLAST (Kelley et al., 2003), Network-BLAST (Scott et al., 2006) are used to align networks locally and they are generally good at finding small sized common substructures. However, these algorithms have same failures about finding larger substructures because of complexity issues. To overcome these issues, Hidden Markov Models (HMMs) is used. The study done by (Qian and Yoon, 2009), uses HMMs to find optimal conserved paths in two biological networks where aligned paths have constant path length. In this paper, we aim to make an extension to the local network alignment procedure done in (Qian
and Yoon, 2009) to find common substructures in varying length sizes between the biological networks. We again used same algorithm to find k-length exact matches from networks and we used them to find common substructures in two forms as sub-graphs and extended paths. These structures do not need to have the same number of nodes and should satisfy the predefined similarity threshold (s0). The other parameter is the length of exact paths (k) formed from biological networks and choosing a lower k value is faster but bigger values might be needed in order to balance the number of matching paths below s0.(More)

Local alignment is done on biological networks to find common conserved substructures belonging to different organisms. Many algorithms such as PathBLAST (Kelley et al., 2003), Network-BLAST (Scott et al., 2006) are used to align networks locally and they are generally good at finding small sized common substructures. However, these algorithms have same failures about finding larger substructures because of complexity issues. To overcome these issues, Hidden Markov Models (HMMs) is used. The study done by (Qian and Yoon, 2009), uses HMMs to find optimal conserved paths in two biological networks where aligned paths have constant path length. In this paper, we aim to make an extension to the local network alignment procedure done in (Qian and Yoon, 2009) to find common substructures in varying length sizes between the biological networks. We again used same algorithm to find k-length exact matches from networks and we used them to find common substructures in two forms as sub-graphs and extended paths. These structures do not need to have the same number of nodes and should satisfy the predefined similarity threshold (s0). The other parameter is the length of exact paths (k) formed from biological networks and choosing a lower k value is faster but bigger values might be needed in order to balance the number of matching paths below s0.

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Gündüz, H.; Gündüz, H. and Süzer, İ. (2016). An Extension to Local Network Alignment using Hidden Markov Models (HMMs).In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2016) ISBN 978-989-758-170-0, pages 253-257. DOI: 10.5220/0005742102530257

@conference{bioinformatics16, author={Hakan Gündüz. and Hakan Gündüz. and İbrahim Süzer.}, title={An Extension to Local Network Alignment using Hidden Markov Models (HMMs)}, booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2016)}, year={2016}, pages={253-257}, publisher={SciTePress}, organization={INSTICC}, doi={10.5220/0005742102530257}, isbn={978-989-758-170-0}, }

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2016) TI - An Extension to Local Network Alignment using Hidden Markov Models (HMMs) SN - 978-989-758-170-0 AU - Gündüz, H. AU - Gündüz, H. AU - Süzer, İ. PY - 2016 SP - 253 EP - 257 DO - 10.5220/0005742102530257