Analyzing protein-protein interactions of coronavirus using markov clustering with cuckoo search and ant lion optimization

R. Afriyani, A. Bustamam, D. Sarwinda

Research output: Contribution to journalConference articlepeer-review


Proteins are complex organic compounds made up of smaller units called amino acids that are bonded together in long chains. Protein interacts with other proteins or molecules and becomes essential in the structure, function, and regulation of organisms' cells. The Protein-Protein Interaction (PPI) results in a considerably large network. Consequently, there is a need to find a method to simplify the network for easy interpretation of the protein-protein interaction. One of the most common methods is Markov Clustering (MCL). MCL has been applied to solve graph clustering problems based on stochastic flow simulation. MCL has three main stages in the process, namely expansion, inflation, and pruning. Although MCL produces a fast and well-balanced non-hierarchical clustering, it has a limitation where the results depend on the inflation parameter being inputted manually. In this study, we develop a method to combine Markov Clustering (MCL) with Cuckoo Search (CS) and Ant Lion Optimization (ALO) Algorithm. CS and ALO are applied in MCL algorithm to obtain an optimized inflation parameter automatically. PPI network of SARS-CoV-2 and other related coronavirus datasets are used in this research and is presented in the form of a graph. The experiment shows that CS-MCL forms 47 clusters, while ALO-MCL yields 14 cluster on the PPI dataset.

Original languageEnglish
Article number012009
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 7 Jan 2021
Event10th International Conference and Workshop on High Dimensional Data Analysis, ICW-HDDA 2020 - Sanur-Bali, Indonesia
Duration: 12 Oct 202015 Oct 2020


Dive into the research topics of 'Analyzing protein-protein interactions of coronavirus using markov clustering with cuckoo search and ant lion optimization'. Together they form a unique fingerprint.

Cite this