Clustering protein-protein interaction data with spectral clustering and fuzzy random walk

E. Krisna, A. Bustamam, K. A. Sugeng

Research output: Contribution to journalConference articlepeer-review

Abstract

Spectral Clustering is a graph clustering algorithm that makes use of eigenvector obtained from a matrix describing pairwise similarity between data points. It provides a dimensionality reduction for clustering in lower dimensions. One example of spectral clustering application is the clustering of protein-protein interaction (PPI) network. PPI networks are usually represented as a graph network with proteins and interactions as vertices and edges respectively. However, this spectral clustering only produces a hard clustering of proteins, whereas there may be some relationship between each protein clusters, and possibly multiple functionality for each proteins that has not been detected before. Fuzzy Random Walk is a fuzzy clustering method based on transition probability from a random walk on a dataset. In this paper, we combine both Spectral Clustering and Fuzzy Random Walk to cluster PPI network of protein TP53, a protein thatplays an important role in managing cell cycle, especially in tumor cell suppression. Using PPI dataset of TP53 obtained from the STRING database, we found the combined algorithm is proven to produce both robust and fuzzy clusters with each cluster explains one of TP53 protein's functionality related to the tumor cell.

Original languageEnglish
Article number012027
JournalJournal of Physics: Conference Series
Volume1211
Issue number1
DOIs
Publication statusPublished - 7 May 2019
Event2nd International Conference of Combinatorics, Graph Theory, and Network Topology, ICCGANT 2018 - Jember, East Java, Indonesia
Duration: 24 Nov 201825 Nov 2018

Keywords

  • Spectral Clustering, Protein-protein interaction (PPI) network, Fuzzy Clustering, Fuzzy Random Walk, TP53 Protein, Tumor cell.

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