Priyakshi Mahanta

Triclustering in gene expression data analysis: A selected survey

Mining microarray data sets is important in bioinformatics research and biomedical applications. Recently, mining triclusters or 3D clusters in a Gene Sample Time or 3D microarray data is an emerging area of research. Each tricluster contains a subset of genes and a subset of samples such that the genes are coherent on the samples along the time series. There is a scarcity of triclustering algorithms in the literature of microarray data analysis. We review some existing triclustering algorithms and discuss their merits and demerits. Finally we are trying to provide the researcher who are new to this field a base platform by exposing the issues which are still challenging in triclustering through our analysis of these algorithms.

Shifting-and-scaling correlation based biclustering algorithm

The existence of various types of correlations among the expressions of a group of biologically significant genes poses challenges in developing effective methods of gene expression data analysis. The initial focus of computational biologists was to work with only absolute and shifting correlations. However, researchers have found that the ability to handle shifting-and-scaling correlation enables them to extract more biologically relevant and interesting patterns from gene microarray data. In this paper, we introduce an effective shifting-and-scaling correlation measure named Shifting and Scaling Similarity (SSSim), which can detect highly correlated gene pairs in any gene expression data. We also introduce a technique named Intensive Correlation Search (ICS) biclustering algorithm, which uses SSSim to extract biologically significant biclusters from a gene expression data set. The technique performs satisfactorily with a number of benchmarked gene expression data sets when evaluated in terms of functional categories in Gene Ontology database.

An effective method for network module extraction from microarray data

BackgroundThe development of high-throughput Microarray technologies has provided various opportunities to systematically characterize diverse types of computational biological networks. Co-expression network have become popular in the analysis of microarray data, such as for detecting functional gene modules.ResultsThis paper presents a method to build a co-expression network (CEN) and to detect network modules from the built network. We use an effective gene expression similarity measure called NMRS (Normalized mean residue similarity) to construct the CEN. We have tested our method on five publicly available benchmark microarray datasets. The network modules extracted by our algorithm have been biologically validated in terms of Q value and p value.ConclusionsOur results show that the technique is capable of detecting biologically significant network modules from the co-expression network. Biologist can use this technique to find groups of genes with similar functionality based on their expression information.

GERC: Tree Based Clustering for Gene Expression Data

Measurement of gene expression using DNA micro arrays have revolutionized biological and medical research. This paper presents a divisive clustering algorithm that produces a tree of genes called GERC tree along with the generated clusters. Unlike a dendrogram, a GERC tree is a general tree and it is an ample resource for biological information about the genes in a data set. The leaves of the tree represent the desired clusters. The clustering method was tested with several real-life data sets and the proposed method has been found satisfactory.

Intersected coexpressed subcube miner: An effective triclustering algorithm

Triclustering techniques extract genes that have similar expression patterns in a set of samples across a set of time points. A challenge in triclustering is to account for both inter-temporal and intra-temporal gene coherence. Other challenges include avoidance of time-dominated and sample-dominated results and detection of time latent triclusters. This paper presents a technique based on order preserving sub-matrices to find a set of triclusters from gene-sample-time data. The technique finds a set of initial modules in each unordered pair of gene-sample planes, which are then extended to final triclusters. We propose a planar similarity measure called PMRS to extend the initial modules to the final triclusters.

Discretization in gene expression data analysis: a selected survey

Discretization techniques are widely used as preprocessing task in different classification techniques specially in the area of machine learning. These techniques have also been used as a preprocessing task for computational construction of regulatory networks in gene expression data analysis. We analyze the use of some widely used discretization techniques in other gene expression data analysis tasks such as gene functional prediction. This paper evaluates the performance of these discretization techniques as a preprocessing task by applying the discretized gene expression data on different clustering algorithms. The results generated by the clustering algorithms are internally and externally validated against different discretization techniques. Finally, we introduce some of the important issues and research challenges.

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

Construction of co-expression network and extraction of network modules have been an appealing area of bioinformatics research. This article presents a co-expression network construction and a biologically relevant network module extraction technique based on fuzzy set theoretic approach. The technique is able to handle both positive and negative correlations among genes. The constructed network for some benchmark gene expression datasets have been validated using topological internal and external measures. The effectiveness of network module extraction technique has been established in terms of well-known p-value, Q-value and topological statistics.

Module extraction from subspace co-expression networks

Most existing algorithms for co-expression network construction for the purpose of gene expression data analysis define correlation between a pair of genes over the set of all samples as an edge. In this paper, we propose a way to represent co-expression network that traces correlation among genes over subspace of samples. A method is presented for construction of such a co-expression network. A connectivity measure is also introduced to determine connectivity among genes in the proposed representation of co-expression network. The proposed connectivity measure is used with k-means clustering algorithm to extract network modules from the sub-space co-expression network. The methodology has been applied over real life gene expression datasets and the results are validated in terms of external indices such as p value and Q value.

MIPCE: an MI-based protein complex extraction technique.

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 well-known methods and obtained better results in terms of various parameters such as precession, recall and F-measure.

A Subspace Module Extraction Technique for Gene Expression Data

Construction of co-expression network and extraction of network modules have been an appealing area of bioinformatics research. In literature, most existing algorithms of gene co-expression network extract network modules where all samples are considered. In this paper, we propose a method to construct a co-expression network based on mutual information and to extract network modules defined over a subset of samples. The method was applied over several real life gene expression datasets and the results are validated in terms of p value, Q value and topological properties.