FUMET: A fuzzy network module extraction technique for gene expression data

Mahanta, Priyakshi; Ahmed, Hasin Afzal; Bhattacharyya, Dhruba K.; Ghosh, Ashish

doi:10.1007/s12038-014-9423-2

Cited by 14 publications

(7 citation statements)

References 21 publications

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“…Table 2 shows that the modules extracted from Dataset 2 are enriched with functions like gluthathione peroxidate activity, response to wounding, monocarboxylic acid metabolic process, jasmonic acid biosynthetic process, perioxidase activity with q values 2.87e-16, 4.38e-15, 2.10e-12, 5.47e-10, 1.22e-9, respectively. Table 3 gives a comparison between FUMET4 and the proposed method in terms of p values obtained for Dataset 3. Additionally, Tables 4 and 5 depict that GO terms obtained using the proposed method for Dataset 1 have lower p values and q values than that of FUMET and Qcut, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…In the past two decades, a good number of methods, such as, FCM21, SYNCLUS22, FLAME2, FUZZY-EWKM323, FUMET4, and Qcut5 have been introduced for construction of CEN. These methods are based on various correlation measures.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, along with the construction of CEN, it is important to extract the functionally correlated network modules. Researchers have also introduced several tools and methods to support extraction of network modules from co-expression networks2345.…”

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confidence: 99%

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THD-Module Extractor: An Application for CEN Module Extraction and Interesting Gene Identification for Alzheimer’s Disease

Kakati

Kashyap

Bhattacharyya

2016

Sci Rep

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There exist many tools and methods for construction of co-expression network from gene expression data and for extraction of densely connected gene modules. In this paper, a method is introduced to construct co-expression network and to extract co-expressed modules having high biological significance. The proposed method has been validated on several well known microarray datasets extracted from a diverse set of species, using statistical measures, such as p and q values. The modules obtained in these studies are found to be biologically significant based on Gene Ontology enrichment analysis, pathway analysis, and KEGG enrichment analysis. Further, the method was applied on an Alzheimer's disease dataset and some interesting genes are found, which have high semantic similarity among them, but are not significantly correlated in terms of expression similarity. Some of these interesting genes, such as MAPT, CASP2, and PSEN2, are linked with important aspects of Alzheimer's disease, such as dementia, increase cell death, and deposition of amyloid-beta proteins in Alzheimer's disease brains. The biological pathways associated with Alzheimer's disease, such as, Wnt signaling, Apoptosis, p53 signaling, and Notch signaling, incorporate these interesting genes. The proposed method is evaluated in regard to existing literature.The volume of data obtained by microarray or other new high throughput technologies have been increasing enormously, and as a result, the biological databases are growing enormously. The key challenge is to analyze these large amount of data and extract meaningful information. Therefore, it is an interesting and challenging task to represent these data in an easy way, so that researchers can communicate and interpret data efficiently. A biological network graph allows interactive visual representation and analysis of data. In a biological network, nodes represent bio-entities (genes or proteins) and edges represent associations between the bio-entities. The biological networks can be categorized broadly based on their constituent bio-entities, such as, protein-protein interaction network, gene co-expression network (CEN), and metabolic network. These biological networks encode the relationships, such as, correlation, co-expression, and co-regulation, among the bio-entities.A gene CEN is defined as an undirected graph, where two genes are connected to each other if their expression levels are similar across conditions. Information related to physical-interaction, genetic interaction, shared protein domains, co-localization, pathway, and predicted functions can be inferred from a gene CEN 1 . A gene CEN primarily shows correlation or association in a pair of genes in terms of standard correlation measures and mutual information. These networks encode the relationships of each entity with its neighbors, which helps in geometrical interpretations for extraction of functionally related genes or network modules.There are many established methods for construction of CEN based on various approaches and cor...

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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THD-Module Extractor: An Application for CEN Module Extraction and Interesting Gene Identification for Alzheimer’s Disease

Kakati

Kashyap

Bhattacharyya

2016

Sci Rep

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“…In a complex network, relatively dense regions are termed as network modules which represent a set of regulated genes corresponding to similar biological function. Mahanta et al (2014) developed a fuzzy network module extraction technique (FUMET). The FUMET takes two input parameters such as number of modules and membership threshold, and works on weighted co-expression network.…”

Section: The Membership Functions and Set Of Target (T) Relationsmentioning

confidence: 99%

Fuzzy logic based approaches for gene regulatory network inference

Raza

2019

Artificial Intelligence in Medicine

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The rapid advancement in high-throughput techniques has fuelled the generation of large volume of biological data rapidly with low cost. Some of these techniques are microarray and next generation sequencing which provides genome level insight of living cells. As a result, the size of most of the biological databases, such as NCBI-GEO, NCBI-SRA, is exponentially growing. These biological data are analyzed using computational techniques for knowledge discovery -which is one of the objectives of bioinformatics research. Gene regulatory network (GRN) is a gene-gene interaction network which plays pivotal role in understanding gene regulation process and disease studies. From the last couple of decades, the researchers are interested in developing computational algorithms for GRN inference (GRNI) using high-throughput experimental data. Several computational approaches have been applied for inferring GRN from gene expression data including statistical techniques (correlation coefficient), information theory (mutual information), regression based approaches, probabilistic approaches (Bayesian networks, naïve byes), artificial neural networks, and fuzzy logic. The fuzzy logic, along with its hybridization with other intelligent approach, is well studied in GRNI due to its several advantages. In this paper, we present a consolidated review on fuzzy logic and its hybrid approaches for GRNI developed during last two decades.

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“…PPI networks are important sources of information related to biological process and complex metabolic functions of the cell. Cluster analysis is a choice of methodology for the extraction of functional modules Mahanta et al (2014) from protein-protein interaction networks. Clustering can be defined as the grouping of objects based on their sharing of discrete and measurable properties.…”

Section: Introductionmentioning

confidence: 99%

MIPCE: An MI-based protein complex extraction technique

2015

Self Cite

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Protein-protein interaction (PPI) networks are believed to be important sources of information related to biological processes and complex metabolic functions of the cell. Identifying protein complexes is of great importance for understanding cellular organization and functions of organisms. In this work, a method is proposed, referred to as MIPCE, to find protein complexes in a PPI network based on mutual information.MIPCE has been biologically validated by GO-based score and satisfactory results have been obtained. We have also compared our method with some wellknown methods and obtained better results in terms of various parameters such as precession, recall and F-measure.

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FUMET: A fuzzy network module extraction technique for gene expression data

Cited by 14 publications

References 21 publications

THD-Module Extractor: An Application for CEN Module Extraction and Interesting Gene Identification for Alzheimer’s Disease

THD-Module Extractor: An Application for CEN Module Extraction and Interesting Gene Identification for Alzheimer’s Disease

Fuzzy logic based approaches for gene regulatory network inference

MIPCE: An MI-based protein complex extraction technique

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