@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.}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 ; Conference date: 13-11-2017 Through 16-11-2017",
year = "2017",
month = dec,
day = "15",
doi = "10.1109/BIBM.2017.8218036",
language = "English (US)",
series = "Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2303--2305",
editor = "Illhoi Yoo and Zheng, {Jane Huiru} and Yang Gong and Hu, {Xiaohua Tony} and Chi-Ren Shyu and Yana Bromberg and Jean Gao and Dmitry Korkin",
booktitle = "Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017",
}