Markov Clustering Algorithms and Their Application in Analysis of PPI Network of Malaria Genes

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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From the static interactome to dynamic protein complexes: Three challenges

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720015710018 ◽

2015 ◽

Vol 13 (02) ◽

pp. 1571001 ◽

Cited By ~ 14

Author(s):

Chern Han Yong ◽

Limsoon Wong

Keyword(s):

Protein Interactions ◽

Protein Complexes ◽

Clustering Algorithms ◽

Ppi Network ◽

Protein Protein Interaction ◽

Static Interaction ◽

Ppi Networks ◽

Interaction Screening ◽

Discovery Algorithms ◽

Insight Into

Protein interactions and complexes behave in a dynamic fashion, but this dynamism is not captured by interaction screening technologies, and not preserved in protein–protein interaction (PPI) networks. The analysis of static interaction data to derive dynamic protein complexes leads to several challenges, of which we identify three. First, many proteins participate in multiple complexes, leading to overlapping complexes embedded within highly-connected regions of the PPI network. This makes it difficult to accurately delimit the boundaries of such complexes. Second, many condition- and location-specific PPIs are not detected, leading to sparsely-connected complexes that cannot be picked out by clustering algorithms. Third, the majority of complexes are small complexes (made up of two or three proteins), which are extra sensitive to the effects of extraneous edges and missing co-complex edges. We show that many existing complex-discovery algorithms have trouble predicting such complexes, and show that our insight into the disparity between the static interactome and dynamic protein complexes can be used to improve the performance of complex discovery.

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Protein Complex Discovery by Interaction Filtering from Protein Interaction Networks Using Mutual Rank Coexpression and Sequence Similarity

BioMed Research International ◽

10.1155/2015/165186 ◽

2015 ◽

Vol 2015 ◽

pp. 1-7 ◽

Cited By ~ 1

Author(s):

Ali Kazemi-Pour ◽

Bahram Goliaei ◽

Hamid Pezeshk

Keyword(s):

Protein Interaction ◽

Biological Networks ◽

Sequence Similarity ◽

Clustering Algorithms ◽

Edge Weight ◽

Clustering Methods ◽

Ppi Network ◽

Protein Protein Interaction ◽

New Methods ◽

Mutual Rank

The evaluation of the biological networks is considered the essential key to understanding the complex biological systems. Meanwhile, the graph clustering algorithms are mostly used in the protein-protein interaction (PPI) network analysis. The complexes introduced by the clustering algorithms include noise proteins. The error rate of the noise proteins in the PPI network researches is about 40–90%. However, only 30–40% of the existing interactions in the PPI databases depend on the specific biological function. It is essential to eliminate the noise proteins and the interactions from the complexes created via clustering methods. We have introduced new methods of weighting interactions in protein clusters and the splicing of noise interactions and proteins-based interactions on their weights. The coexpression and the sequence similarity of each pair of proteins are considered the edge weight of the proteins in the network. The results showed that the edge filtering based on the amount of coexpression acts similar to the node filtering via graph-based characteristics. Regarding the removal of the noise edges, the edge filtering has a significant advantage over the graph-based method. The edge filtering based on the amount of sequence similarity has the ability to remove the noise proteins and the noise interactions.

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How Far Are We from the Completion of the Human Protein Interactome Reconstruction?

Biomolecules ◽

10.3390/biom12010140 ◽

2022 ◽

Vol 12 (1) ◽

pp. 140

Author(s):

Georgios N. Dimitrakopoulos ◽

Maria I. Klapa ◽

Nicholas K. Moschonas

Keyword(s):

High Throughput ◽

Clustering Algorithms ◽

Human Protein ◽

Ppi Network ◽

Medicine Research ◽

Protein Protein Interaction ◽

Protein Interactome ◽

Addition Rate ◽

High Throughput Experiments ◽

Genome Scale

After more than fifteen years from the first high-throughput experiments for human protein–protein interaction (PPI) detection, we are still wondering how close the completion of the genome-scale human PPI network reconstruction is, what needs to be further explored and whether the biological insights gained from the holistic investigation of the current network are valid and useful. The unique structure of PICKLE, a meta-database of the human experimentally determined direct PPI network developed by our group, presently covering ~80% of the UniProtKB/Swiss-Prot reviewed human complete proteome, enables the evaluation of the interactome expansion by comparing the successive PICKLE releases since 2013. We observe a gradual overall increase of 39%, 182%, and 67% in protein nodes, PPIs, and supporting references, respectively. Our results indicate that, in recent years, (a) the PPI addition rate has decreased, (b) the new PPIs are largely determined by high-throughput experiments and mainly concern existing protein nodes and (c), as we had predicted earlier, most of the newly added protein nodes have a low degree. These observations, combined with a largely overlapping k-core between PICKLE releases and a network density increase, imply that an almost complete picture of a structurally defined network has been reached. The comparative unsupervised application of two clustering algorithms indicated that exploring the full interactome topology can reveal the protein neighborhoods involved in closely related biological processes as transcriptional regulation, cell signaling and multiprotein complexes such as the connexon complex associated with cancers. A well-reconstructed human protein interactome is a powerful tool in network biology and medicine research forming the basis for multi-omic and dynamic analyses.

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Selecting Clustering Algorithms for IBD Mapping

10.1101/2021.08.11.456036 ◽

2021 ◽

Author(s):

Ruhollah Shemirani ◽

Gillian M Belbin ◽

Keith Burghardt ◽

Kristina Lerman ◽

Christy L Avery ◽

...

Keyword(s):

Statistical Power ◽

Large Scale ◽

Clustering Algorithms ◽

False Negative ◽

Chromosome 1 ◽

Detection Methods ◽

Scalable Clustering ◽

Markov Clustering ◽

Cluster Properties ◽

Greedy Methods

Background: Groups of distantly related individuals who share a short segment of their genome identical-by-descent (IBD) can provide insights about rare traits and diseases in massive biobanks via a process called IBD mapping. Clustering algorithms play an important role in finding these groups. We set out to analyze the fitness of commonly used, fast and scalable clustering algorithms for IBD mapping applications. We designed a realistic benchmark for local IBD graphs and utilized it to compare clustering algorithms in terms of statistical power. We also investigated the effectiveness of common clustering metrics as replacements for statistical power. Results: We simulated 3.4 million clusters across 850 experiments with varying cluster counts, false-positive, and false-negative rates. Infomap and Markov Clustering (MCL) community detection methods have high statistical power in most of the graphs, compared to greedy methods such as Louvain and Leiden. We demonstrate that standard clustering metrics, such as modularity, cannot predict statistical power of algorithms in IBD mapping applications, though they can help with simulating realistic benchmarks. We extend our findings to real datasets by analyzing 3 populations in the Population Architecture using Genomics and Epidemiology (PAGE) Study with ~51,000 members and 2 million shared segments on Chromosome 1, resulting in the extraction of ~39 million local IBD clusters across three different populations in PAGE. We used cluster properties derived in PAGE to increase the accuracy of our simulations and comparison. Conclusions: Markov Clustering produces a 30% increase in statistical power compared to the current state-of-art approach, while reducing runtime by 3 orders of magnitude; making it computationally tractable in modern large-scale genetic datasets. We provide an efficient implementation to enable clustering at scale for IBD mapping and poplation-based linkage for various populations and scenarios.

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Protein functional module identification method combining topological features and gene expression data

BMC Genomics ◽

10.1186/s12864-021-07620-3 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Zihao Zhao ◽

Wenjun Xu ◽

Aiwen Chen ◽

Yueyue Han ◽

Shengrong Xia ◽

...

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Functional Module ◽

Clustering Algorithms ◽

Expression Patterns ◽

Structure Characteristic ◽

Functional Modules ◽

Expression Data ◽

Ppi Network ◽

Topological Features

Abstract Background The study of protein complexes and protein functional modules has become an important method to further understand the mechanism and organization of life activities. The clustering algorithms used to analyze the information contained in protein-protein interaction network are effective ways to explore the characteristics of protein functional modules. Results This paper conducts an intensive study on the problems of low recognition efficiency and noise in the overlapping structure of protein functional modules, based on topological characteristics of PPI network. Developing a protein function module recognition method ECTG based on Topological Features and Gene expression data for Protein Complex Identification. Conclusions The algorithm can effectively remove the noise data reflected by calculating the topological structure characteristic values in the PPI network through the similarity of gene expression patterns, and also properly use the information hidden in the gene expression data. The experimental results show that the ECTG algorithm can detect protein functional modules better.

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Handling WSD using Hierarchical Clustering Algorithm with sentences

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset1841120 ◽

2018 ◽

pp. 83-88

Author(s):

Mohana Priya K ◽

Pooja Ragavi S ◽

Krishna Priya G

Keyword(s):

Hierarchical Clustering ◽

Similarity Measure ◽

Clustering Algorithm ◽

Clustering Algorithms ◽

Cosine Similarity Measure ◽

Hierarchical Clustering Algorithm ◽

Multiple Levels ◽

Pos Tagger ◽

Sentence Clustering ◽

The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

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