Evaluation of BLAST-based edge-weighting metrics used for homology inference with the Markov Clustering algorithm

In this paper, a Genetic Algorithm is applied on the filter of the Enhanced Markov Clustering algorithm to optimize the selection of clusters having a high probability to represent protein complexes. The filter was applied on the results (obtained by experiments made on five different yeast datasets) of three different algorithms known for their efficiency on protein complex detection through protein interaction graphs. The results are compared with three popular clustering algorithms, proving the efficiency of the proposed method according to metrics such as successful prediction rate and geometrical accuracy.

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Using a Genetic Algorithm and Markov Clustering on Protein–Protein Interaction Graphs

Bioinformatics ◽

10.4018/978-1-4666-3604-0.ch043 ◽

2013 ◽

pp. 805-816

Author(s):

Charalampos Moschopoulos ◽

Grigorios Beligiannis ◽

Spiridon Likothanassis ◽

Sophia Kossida

Keyword(s):

Genetic Algorithm ◽

Protein Interaction ◽

Clustering Algorithm ◽

Protein Complexes ◽

Clustering Algorithms ◽

Interaction Graphs ◽

Protein Protein Interaction ◽

Protein Complex Detection ◽

Markov Clustering ◽

Complex Detection

In this paper, a Genetic Algorithm is applied on the filter of the Enhanced Markov Clustering algorithm to optimize the selection of clusters having a high probability to represent protein complexes. The filter was applied on the results (obtained by experiments made on five different yeast datasets) of three different algorithms known for their efficiency on protein complex detection through protein interaction graphs. The results are compared with three popular clustering algorithms, proving the efficiency of the proposed method according to metrics such as successful prediction rate and geometrical accuracy.

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Efficient Markov clustering algorithm for protein sequence grouping

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) ◽

10.1109/embc.2013.6609581 ◽

2013 ◽

Cited By ~ 1

Author(s):

Laszlo Szilagyi ◽

Sandor M. Szilagyi

Keyword(s):

Protein Sequence ◽

Clustering Algorithm ◽

Markov Clustering

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Implementation of regularized Markov clustering algorithm on protein interaction networks of schizophrenia's risk factor candidate genes

2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS) ◽

10.1109/icacsis.2016.7872726 ◽

2016 ◽

Cited By ~ 2

Author(s):

Rizky Ginanjar ◽

Alhadi Bustamam ◽

Hengki Tasman

Keyword(s):

Risk Factor ◽

Candidate Genes ◽

Protein Interaction ◽

Clustering Algorithm ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Markov Clustering

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Orthology clusters from gene trees with Possvm

10.1101/2021.05.03.442399 ◽

2021 ◽

Author(s):

Xavier Grau-Bové ◽

Arnau Sebé-Pedrós

Keyword(s):

Gene Family ◽

Phylogenetic Trees ◽

Clustering Algorithm ◽

Gene Tree ◽

Gene Family Evolution ◽

Gene Trees ◽

Orthologous Genes ◽

Gene Annotations ◽

Species Overlap ◽

Markov Clustering

Possvm (Phylogenetic Ortholog Sorting with Species oVerlap and MCL) is a tool that automates the process of classifying clusters of orthologous genes from precomputed phylogenetic trees. It identifies orthology relationships between genes using the species overlap algorithm to infer taxonomic information from the gene tree topology, and then uses the Markov Clustering Algorithm (MCL) to identify orthology clusters and provide annotated gene family classifications. Our benchmarking shows that this approach, when provided with accurate phylogenies, is able to identify manually curated orthogroups with high precision and recall. Overall, Possvm automates the routine process of gene tree inspection and annotation in a highly interpretable manner, and provides reusable outputs that can be used to obtain phylogeny-informed gene annotations and inform comparative genomics and gene family evolution analyses.

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Operon Prediction Model Based on Markov Clustering Algorithm

International Journal Bioautomation ◽

10.7546/ijba.2019.23.1.105-116 ◽

2019 ◽

Vol 23 (1) ◽

pp. 105-116

Author(s):

Yanfeng Liu ◽

◽

Yongquan Liang ◽

Keyword(s):

Prediction Model ◽

Clustering Algorithm ◽

Model Based ◽

Operon Prediction ◽

Markov Clustering

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The Implementation of Regularized Markov Clustering with Pigeon Inspired Optimization Algorithm in Analyzing the SARS-CoV-2 (COVID-19) Protein Interaction Network

Desimal Jurnal Matematika ◽

10.24042/djm.v3i3.6822 ◽

2020 ◽

Vol 3 (3) ◽

pp. 191-200

Author(s):

M. Syamsuddin Wisnubroto ◽

Marsudi Siburian ◽

Febri Dwi Irawati

Keyword(s):

Protein Interaction ◽

Optimization Algorithm ◽

Large Scale ◽

Clustering Algorithm ◽

Drug Research ◽

Interaction Network ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Clustering Methods ◽

Markov Clustering

Proteins interact with other proteins, DNA, and other molecules, forming large-scale protein interaction networks and for easy analysis, clustering methods are needed. Regularized Markov clustering algorithm is an improvement of MCL where operations on expansion are replaced by new operations that update the flow distributions of each node. But to reduce the weaknesses of the RMCL optimization, Pigeon Inspired Optimization Algorithm (PIO) is used to replace the inflation parameters. The simulation results of IPC SARS-Cov-2 (COVID-19) inflation parameters get the result of 42 proteins as the center of the cluster and 8 protein pairs interacting with each other. Proteins of COVID-19 that interact with 20 or more proteins are ORF8, NSP13, NSP7, M, N, ORF9C, NSP8, and NSP1. Their interactions might be used as a target for drug research.

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