Analysis of clustering algorithms in biological networks

Asuda Sharma, Hesham H Ali

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Biological data is often represented as networks, as in the case of protein-protein interactions and metabolic pathways. Modeling, analyzing, and visualizing networks can help make sense of large volumes of data generated by high-throughput experiments. However, due to their size and complex structure, biological networks can be difficult to interpret without further processing. Cluster analysis is a widely-used approach to extract meaningful information from biological networks. In this work, we provide a study that surveys some of the widely used clustering algorithms used for clustering biological data. We identify the advantages and disadvantages of each algorithm and attempt to identify features associated with datasets that align well with each approach. We also propose a new clustering method based on graph matching and node merging techniques in an attempt to fill the gap left by the current clustering approaches.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2303-2305
Number of pages3
Volume2017-January
ISBN (Electronic)9781509030491
DOIs
StatePublished - Dec 15 2017
Event2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 - Kansas City, United States
Duration: Nov 13 2017Nov 16 2017

Other

Other2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
CountryUnited States
CityKansas City
Period11/13/1711/16/17

Fingerprint

Clustering algorithms
Cluster Analysis
Proteins
Cluster analysis
Merging
Throughput
Processing
Information Services
Metabolic Networks and Pathways
Experiments

Keywords

  • Cluster analysis
  • Matching
  • Node-merging
  • Unsupervised learning
  • Weighted graph matching

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics

Cite this

Sharma, A., & Ali, H. H. (2017). Analysis of clustering algorithms in biological networks. In Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 (Vol. 2017-January, pp. 2303-2305). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2017.8218036

Analysis of clustering algorithms in biological networks. / Sharma, Asuda; Ali, Hesham H.

Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 2303-2305.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Sharma, A & Ali, HH 2017, Analysis of clustering algorithms in biological networks. in Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 2303-2305, 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017, Kansas City, United States, 11/13/17. https://doi.org/10.1109/BIBM.2017.8218036
Sharma A, Ali HH. Analysis of clustering algorithms in biological networks. In Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Vol. 2017-January. Institute of Electrical and Electronics Engineers Inc. 2017. p. 2303-2305 https://doi.org/10.1109/BIBM.2017.8218036
Sharma, Asuda ; Ali, Hesham H. / Analysis of clustering algorithms in biological networks. Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. pp. 2303-2305
@inproceedings{d4e7041b5337420a8152f15c4cab9f7d,
title = "Analysis of clustering algorithms in biological networks",
abstract = "Biological data is often represented as networks, as in the case of protein-protein interactions and metabolic pathways. Modeling, analyzing, and visualizing networks can help make sense of large volumes of data generated by high-throughput experiments. However, due to their size and complex structure, biological networks can be difficult to interpret without further processing. Cluster analysis is a widely-used approach to extract meaningful information from biological networks. In this work, we provide a study that surveys some of the widely used clustering algorithms used for clustering biological data. We identify the advantages and disadvantages of each algorithm and attempt to identify features associated with datasets that align well with each approach. We also propose a new clustering method based on graph matching and node merging techniques in an attempt to fill the gap left by the current clustering approaches.",
keywords = "Cluster analysis, Matching, Node-merging, Unsupervised learning, Weighted graph matching",
author = "Asuda Sharma and Ali, {Hesham H}",
year = "2017",
month = "12",
day = "15",
doi = "10.1109/BIBM.2017.8218036",
language = "English (US)",
volume = "2017-January",
pages = "2303--2305",
booktitle = "Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Analysis of clustering algorithms in biological networks

AU - Sharma, Asuda

AU - Ali, Hesham H

PY - 2017/12/15

Y1 - 2017/12/15

N2 - Biological data is often represented as networks, as in the case of protein-protein interactions and metabolic pathways. Modeling, analyzing, and visualizing networks can help make sense of large volumes of data generated by high-throughput experiments. However, due to their size and complex structure, biological networks can be difficult to interpret without further processing. Cluster analysis is a widely-used approach to extract meaningful information from biological networks. In this work, we provide a study that surveys some of the widely used clustering algorithms used for clustering biological data. We identify the advantages and disadvantages of each algorithm and attempt to identify features associated with datasets that align well with each approach. We also propose a new clustering method based on graph matching and node merging techniques in an attempt to fill the gap left by the current clustering approaches.

AB - Biological data is often represented as networks, as in the case of protein-protein interactions and metabolic pathways. Modeling, analyzing, and visualizing networks can help make sense of large volumes of data generated by high-throughput experiments. However, due to their size and complex structure, biological networks can be difficult to interpret without further processing. Cluster analysis is a widely-used approach to extract meaningful information from biological networks. In this work, we provide a study that surveys some of the widely used clustering algorithms used for clustering biological data. We identify the advantages and disadvantages of each algorithm and attempt to identify features associated with datasets that align well with each approach. We also propose a new clustering method based on graph matching and node merging techniques in an attempt to fill the gap left by the current clustering approaches.

KW - Cluster analysis

KW - Matching

KW - Node-merging

KW - Unsupervised learning

KW - Weighted graph matching

UR - http://www.scopus.com/inward/record.url?scp=85046274150&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85046274150&partnerID=8YFLogxK

U2 - 10.1109/BIBM.2017.8218036

DO - 10.1109/BIBM.2017.8218036

M3 - Conference contribution

VL - 2017-January

SP - 2303

EP - 2305

BT - Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017

PB - Institute of Electrical and Electronics Engineers Inc.

ER -