A Graph-Based Approach for Clustering Analysis of Gene Expression Data by Using Topological Features

The development of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many machine learning methods have been developed, including supervised, unsupervised, and semi-supervised to infer gene regulatory networks. Most of these methods ignore the class imbalance problem which can lead to decreasing the accuracy of predicting regulatory interactions in the network. Therefore, developing an effective method considering imbalanced data is a challenging task. In this paper, we propose EnGRNT approach to infer GRNs with high accuracy that uses ensemble-based methods. The proposed approach, as well as the gene expression data, considers the topological features of GRN. We applied our approach to the simulated Escherichia coli dataset. Experimental results demonstrate that the appropriateness of the inference method relies on the size and type of expression profiles in microarray data. Except for multifactorial experimental conditions, the proposed approach outperforms unsupervised methods. The obtained results recommend the application of EnGRNT on the imbalanced datasets.

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TOPOLOGICAL FEATURES IN CANCER GENE EXPRESSION DATA

Biocomputing 2015 ◽

10.1142/9789814644730_0012 ◽

2014 ◽

Cited By ~ 5

Author(s):

S. LOCKWOOD ◽

B. KRISHNAMOORTHY

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Cancer Gene ◽

Expression Data ◽

Topological Features

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Clustering analysis for gene expression data: A methodological review

2012 5th International Symposium on Communications, Control and Signal Processing ◽

10.1109/isccsp.2012.6217811 ◽

2012 ◽

Cited By ~ 5

Author(s):

Rui Fa ◽

Asoke K. Nandi ◽

Li-Yun Gong

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Clustering Analysis ◽

Expression Data ◽

Methodological Review

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Clustering analysis of tumor metabolic networks

BMC Bioinformatics ◽

10.1186/s12859-020-03564-9 ◽

2020 ◽

Vol 21 (S10) ◽

Author(s):

Ichcha Manipur ◽

Ilaria Granata ◽

Lucia Maddalena ◽

Mario R. Guarracino

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Protein Interactions ◽

Biological Networks ◽

Clustering Analysis ◽

Large Scale ◽

Metabolic Networks ◽

Computational Time ◽

Expression Data ◽

Metabolic Models

Abstract Background Biological networks are representative of the diverse molecular interactions that occur within cells. Some of the commonly studied biological networks are modeled through protein-protein interactions, gene regulatory, and metabolic pathways. Among these, metabolic networks are probably the most studied, as they directly influence all physiological processes. Exploration of biochemical pathways using multigraph representation is important in understanding complex regulatory mechanisms. Feature extraction and clustering of these networks enable grouping of samples obtained from different biological specimens. Clustering techniques separate networks depending on their mutual similarity. Results We present a clustering analysis on tissue-specific metabolic networks for single samples from three primary tumor sites: breast, lung, and kidney cancer. The metabolic networks were obtained by integrating genome scale metabolic models with gene expression data. We performed network simplification to reduce the computational time needed for the computation of network distances. We empirically proved that networks clustering can characterize groups of patients in multiple conditions. Conclusions We provide a computational methodology to explore and characterize the metabolic landscape of tumors, thus providing a general methodology to integrate analytic metabolic models with gene expression data. This method represents a first attempt in clustering large scale metabolic networks. Moreover, this approach gives the possibility to get valuable information on what are the effects of different conditions on the overall metabolism.

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