MIDClass: Microarray Data Classification by Association Rules and Gene Expression Intervals

Giugno, Rosalba; Pulvirenti, Alfredo; Cascione, Luciano; Pigola, Giuseppe; Ferro, Alfredo

doi:10.1371/journal.pone.0069873

Cited by 17 publications

(16 citation statements)

References 36 publications

(37 reference statements)

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“…Another gene expression classification method based on association rules named microarray interval discriminant classifier was examined in Giugno et al [4]. The idea behind the method is that the gene expression interval values better differentiate subtypes in the same class.…”

Section: Introductionmentioning

confidence: 99%

Modified AHP for Gene Selection and Cancer Classification Using Type-2 Fuzzy Logic

Nguyen

Nahavandi

2016

IEEE Trans. Fuzzy Syst.

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Abstract-This paper proposes a modification to the analytic hierarchy process (AHP) to select the most informative genes that serve as inputs to an interval type-2 fuzzy logic system (IT2FLS) for cancer classification. Unlike the conventional AHP, the modified AHP allows to process quantitative factors that are ranking outcomes of individual gene selection methods including t-test, entropy, receiver operating characteristic curve, Wilcoxon test and signal to noise ratio. The IT2FLS is introduced for the classification task due to its great ability for handling nonlinear, noisy and outlier data, which are common problems in cancer microarray gene expression profiles. An unsupervised learning strategy using the fuzzy c-means clustering is employed to initialize parameters of the IT2FLS. Other classifiers such as multilayer perceptron network, support vector machine, and fuzzy ARTMAP are also implemented for comparisons. Experiments are carried out on three well-known microarray datasets: diffuse large B-cell lymphoma, leukemia cancer and prostate. Rather than the traditional cross validation, leave one out cross validation strategy is applied for the experiments. Results demonstrate the performance dominance of the IT2FLS against the competing classifiers. More noticeably, the modified AHP improves the classification performance not only of the IT2FLS but also of all other classifiers. Accordingly, the proposed combination between the modified AHP and IT2FLS is a powerful tool for cancer classification and can be implemented as a real clinical decision support system that is useful for medical practitioners.

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Section: Introductionmentioning

confidence: 99%

Modified AHP for Gene Selection and Cancer Classification Using Type-2 Fuzzy Logic

Nguyen

Nahavandi

2016

IEEE Trans. Fuzzy Syst.

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“…Clustering algorithms have been implemented in cloud and graphical processing unit (GPU) platforms extensively over the years as discussed in Table 2. 2008CLUMP software Olman et al (2008) K-means in GPU platform Wu et al (2009) Microarray and gene expression data was analysed using parallelised k-means and k-modes cluster algorithms Kraus and Kestler (2010) CloudVista: clustering in cloud computing environment Xu et al 2012Parallel affinity propagation algorithm for clustering large-scale microarray data Hierarchical clustering algorithm for biomedical research applications Tanaseichuk et al (2015) Distributed bioinformatics platform to leverage local with remote clusters for genome analysis A workflow for transcriptome, scRNAseq data analysis via clusters Yu and Lin (2016) Association rule mining An algorithm to mine association rules from gene expression databases Creighton and Hanash (2003) Integrative analysis of gene expression dataset based on discovery of association rules Carmona-Saez et al 2006Prediction of protein function from protein interaction networks using association analysis Atluri et al (2009) Prediction of promoter sequences using relational association rules Czibula et al (2012) Classification of microarray data using association rules Giugno et al (2013) Frequent itemset mining based on association rules for bioinformatics applications Laukens et al (2015)…”

Section: Data Analyticsmentioning

confidence: 99%

Emerging trend of big data analytics in bioinformatics: a literature review

Nagaraj¹,

Sharvani²,

Sridhar³

2018

IJBRA

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Advancement of unparalleled data in bioinformatics over the years is a major concern for storage and management. Such massive data must be handled efficiently to disseminate knowledge. Computational advancements in information technology present feasible analytical solutions to process such data. In this context, the paper is an attempt to highlight the influence of big data in bioinformatics. Some of the concepts emphasised are definition of big data; architectural platforms supporting data analytics; followed by the application of above-mentioned analytical techniques towards complex problems in bioinformatics. The challenges and future prospects of big data analytics in bioinformatics are briefly discussed. This paper provides a comprehensive summary of several data analytical techniques available for bioinformatics researchers and computer scientists.

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“…Association rule-based classifiers have achieved accuracies equivalent to traditional SVM methods for common biological problems [ 71 ]. This transparency has enabled a range of studies that used frequent itemset mining to generate rules for classification [ 72–74 ].…”

Section: Bioinformatics Applicationsmentioning

confidence: 99%

“…A common example is the classification of sample types (e.g. tumor and healthy) with gene expression data [ 37 , 72 , 73 ]. For this purpose, expression values are discretized, and association rules are generated from maximal itemsets [ 72 ].…”

Section: Bioinformatics Applicationsmentioning

confidence: 99%

“…tumor and healthy) with gene expression data [ 37 , 72 , 73 ]. For this purpose, expression values are discretized, and association rules are generated from maximal itemsets [ 72 ]. Furthermore, any other discrete or discretizable feature can be used, from cell properties [ 74 ] to protein–protein interactions [ 75 ].…”

Section: Bioinformatics Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

A primer to frequent itemset mining for bioinformatics

Naulaerts¹,

Meysman²,

Bittremieux³

et al. 2013

Briefings in Bioinformatics

109

107

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Over the past two decades, pattern mining techniques have become an integral part of many bioinformatics solutions. Frequent itemset mining is a popular group of pattern mining techniques designed to identify elements that frequently co-occur. An archetypical example is the identification of products that often end up together in the same shopping basket in supermarket transactions. A number of algorithms have been developed to address variations of this computationally non-trivial problem. Frequent itemset mining techniques are able to efficiently capture the characteristics of (complex) data and succinctly summarize it. Owing to these and other interesting properties, these techniques have proven their value in biological data analysis. Nevertheless, information about the bioinformatics applications of these techniques remains scattered. In this primer, we introduce frequent itemset mining and their derived association rules for life scientists. We give an overview of various algorithms, and illustrate how they can be used in several real-life bioinformatics application domains. We end with a discussion of the future potential and open challenges for frequent itemset mining in the life sciences.

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MIDClass: Microarray Data Classification by Association Rules and Gene Expression Intervals

Cited by 17 publications

References 36 publications

Modified AHP for Gene Selection and Cancer Classification Using Type-2 Fuzzy Logic

Modified AHP for Gene Selection and Cancer Classification Using Type-2 Fuzzy Logic

Emerging trend of big data analytics in bioinformatics: a literature review

A primer to frequent itemset mining for bioinformatics

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