Quality Of Biclusters Research Articles

Identification of ESCC Potential Biomarkers using Biclustering Algorithms

An extensive empirical study is presented in this work to identify potential biomarkers of ESCC by employing fifteen prominent biclustering algorithms on synthetic and real datasets. For systematic analyses, we implement the algorithms on a variety of synthetic datasets and evaluate the quality of biclusters using recovery and relevance scores. The biclustering algorithms showing adequate results on synthetic datasets are implemented on real ESCC microarray dataset of both normal and disease samples separately. Gene enrichment analysis has been carried out to recognize the best possible bicluster(s) of individual algorithms. Our approach exploits the set of best possible biclusters in the downstream analysis towards the identification of the potential biomarkers with reference to a set of established elite genes for ESCC. Our approach depends on Pearson correlation, conversion of floating valued correlation matrix into a binary matrix, degree analysis based on elite genes, deviation of degree in their respective mapping bicluster, significant alteration of gene expression values while transitioning from normal to disease conditions, and gene ontology and pathway analyses. Finally, we detect 9 ESCC potential biomarker genes; SH3GLB1, ARPC2, APPL1, CALM1, FTL, LPAR1, PLAU, PSMB4, and SCP2; which shows the topological as well as biological significance of ESCC elite genes.

Automatic evolution of bi-clusters from microarray data using self-organized multi-objective evolutionary algorithm

In the current paper, a novel approach is proposed for bi-clustering of gene expression data using the fusion of differential evolution framework and self-organizing map (SOM), named as BiClustSMEA. Variable number of gene and condition cluster centers are encoded in different solutions of the population to determine the number of bi-clusters from a dataset in an automated way. The concept of SOM is utilized in designing new genetic operators for both gene and condition clusters to reach to the optimal solution in a faster way. In order to measure the goodness of a bi-clustering solution, three bi-cluster quality measures, mean squared error, row variance, and bi-cluster size, are optimized simultaneously using differential evolution as the underlying optimization strategy. The concept of polynomial mutation is incorporated in our framework to generate highly diverse solutions which in turn helps in faster convergence. The proposed approach is applied on two real-life microarray gene expression datasets and results are compared with various state-of-the-art techniques. Results obtained clearly illustrate that our approach extracts high-quality bi-clusters as compared to other methods and also it converges much faster than other competitors. Further, the obtained results are validated using statistical significance test and biological significance test.

Bi-clustering of microarray data using a symmetry-based multi-objective optimization framework

High-throughput technologies, like DNA microarray, help in simultaneous monitoring of the expression levels of thousands of genes during important biological processes and over the collection of experimental conditions. Automatically uncovering functionally related genes is a basic building block to solve various problems related to functional genomics. But sometimes a subset of genes may not be similar with respect to all the conditions present in the dataset; thus, bi-clustering concept becomes popular where different subsets of genes and the corresponding subsets of conditions with respect to which genes are most similar are automatically identified. In the current study, we have posed this problem in the multi-objective optimization (MOO) framework where different bi-cluster quality measures are optimized simultaneously. The search potentiality of a simulated annealing-based MOO technique, AMOSA, is used for the simultaneous optimization of these measures. A case study on the suitability of different distance measures in solving the bi-clustering problem is also conducted. The competency of the proposed multi-objective-based bi-clustering approach is shown for three benchmark datasets. The obtained results are further validated using statistical and biological significance tests.

A structured view on pattern mining-based biclustering

Mining matrices to find relevant biclusters, subsets of rows exhibiting a coherent pattern over a subset of columns, is a critical task for a wide-set of biomedical and social applications. Since biclustering is a challenging combinatorial optimization task, existing approaches place restrictions on the allowed structure, coherence and quality of biclusters. Biclustering approaches relying on pattern mining (PM) allow an exhaustive yet efficient space exploration together with the possibility to discover flexible structures of biclusters with parameterizable coherency and noise-tolerance. Still, state-of-the-art contributions are dispersed and the potential of their integration remains unclear.This work proposes a structured and integrated view of the contributions of state-of-the-art PM-based biclustering approaches, makes available a set of principles for a guided definition of new PM-based biclustering approaches, and discusses their relevance for applications in pattern recognition. Empirical evidence shows that these principles guarantee the robustness, efficiency and flexibility of PM-based biclustering.

BicPAM: Pattern-based biclustering for biomedical data analysis.

BackgroundBiclustering, the discovery of sets of objects with a coherent pattern across a subset of conditions, is a critical task to study a wide-set of biomedical problems, where molecular units or patients are meaningfully related with a set of properties. The challenging combinatorial nature of this task led to the development of approaches with restrictions on the allowed type, number and quality of biclusters. Contrasting, recent biclustering approaches relying on pattern mining methods can exhaustively discover flexible structures of robust biclusters. However, these approaches are only prepared to discover constant biclusters and their underlying contributions remain dispersed.MethodsThe proposed BicPAM biclustering approach integrates existing principles made available by state-of-the-art pattern-based approaches with two new contributions. First, BicPAM is the first efficient attempt to exhaustively mine non-constant types of biclusters, including additive and multiplicative coherencies in the presence or absence of symmetries. Second, BicPAM provides strategies to effectively compose different biclustering structures and to handle arbitrary levels of noise inherent to data and with discretization procedures.ResultsResults show BicPAM’s superiority against its peers and its ability to retrieve unique types of biclusters of interest, to efficiently deliver exhaustive solutions and to successfully recover planted biclusters in datasets with varying levels of missing values and noise. Its application over gene expression data leads to unique solutions with heightened biological relevance.ConclusionsBicPAM approaches integrate existing disperse efforts towards pattern-based biclustering and provides the first critical strategies to efficiently discover exhaustive solutions of biclusters with shifting, scaling and symmetric assumptions with varying quality and underlying structures. Additionally, BicPAM dynamically adapts its behavior to mine data with different levels of missing values and noise.

An effective measure for assessing the quality of biclusters

Biclustering is becoming a popular technique for the study of gene expression data. This is mainly due to the capability of biclustering to address the data using various dimensions simultaneously, as opposed to clustering, which can use only one dimension at the time. Different heuristics have been proposed in order to discover interesting biclusters in data. Such heuristics have one common characteristic: they are guided by a measure that determines the quality of biclusters. It follows that defining such a measure is probably the most important aspect. One of the popular quality measure is the mean squared residue (MSR). However, it has been proven that MSR fails at identifying some kind of patterns. This motivates us to introduce a novel measure, called virtual error (VE), that overcomes this limitation. Results obtained by using VE confirm that it can identify interesting patterns that could not be found by MSR.

An Archived Multi Objective Simulated Annealing Method to Discover Biclusters in Microarray Data

With the advent of microarray technology it has been possible to measure thousands of expression values of genes in a single experiment. Analysis of large scale geonomics data, notably gene expression, has initially focused on clustering methods. Recently, biclustering techniques were proposed for revealing submatrices showing unique patterns. Biclustering or simultaneous clustering of both genes and conditions is challenging particularly for the analysis of high-dimensional gene expression data in information retrieval, knowledge discovery, and data mining. In biclustering of microarray data, several objectives have to be optimized simultaneously and often these objectives are in conflict with each other. A multi objective model is very suitable for solving this problem. Our method proposes a algorithm which is based on multi objective Simulated Annealing for discovering biclusters in gene expression data. Experimental result in bench mark data base present a significant improvement in overlap among biclusters and coverage of elements in gene expression and quality of biclusters.

Biclustering of expression data with evolutionary computation

Microarray techniques are leading to the development of sophisticated algorithms capable of extracting novel and useful knowledge from a biomedical point of view. In this work, we address the biclustering of gene expression data with evolutionary computation. Our approach is based on evolutionary algorithms, which have been proven to have excellent performance on complex problems, and searches for biclusters following a sequential covering strategy. The goal is to find biclusters of maximum size with mean squared residue lower than a given /spl delta/. In addition, we pay special attention to the fact of looking for high-quality biclusters with large variation, i.e., with a relatively high row variance, and with a low level of overlapping among biclusters. The quality of biclusters found by our evolutionary approach is discussed and the results are compared to those reported by Cheng and Church, and Yang et al. In general, our approach, named SEBI, shows an excellent performance at finding patterns in gene expression data.