Fuzzy Logic for Elimination of Redundant Information of Microarray Data

Huerta, Edmundo Bonilla; Duval, Béatrice; Hao, Jin‐Kao

doi:10.1016/s1672-0229(08)60021-2

Cited by 29 publications

(25 citation statements)

References 42 publications

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“…Classification using Type-2 Fuzzy Logic Thanh Nguyen and Saeid Nahavandi D More specifically, a few papers have employed type-1 FL for microarray cancer classification including those of [11][12][13][14]. Nevertheless, it has been well-known that the type-2 FL has more capability to deal with uncertainty than the type-1 FL [15].…”

Section: Modified Ahp For Gene Selection and Cancermentioning

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: Modified Ahp For Gene Selection and Cancermentioning

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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“…In the research conducted in 2008 (Huerta et al [9]),the scientists proposed a fuzzy logic based data pre-processing approach for elimination of information redundancy of microarray data. This approach also helps to deal with the problems related to the imprecise and noisy nature of gene expression data.…”

Section: Methodsmentioning

confidence: 99%

“…If the described methods would be grouped by techniques they use, then all methods could be divided into three groups: algorithms using fuzzy if-then rules (Ho et al [7], Vinterbo et al [5], Schaefer et al [10]); algorithms that use fuzzy preprocessing step (Huerta et al [9]) and algorithms that use fuzzy logic to identify relationships between genes (Wolf et al [1], Resson et al [3]). …”

Section: Methodsmentioning

confidence: 99%

“…In 2008 an article (Huerta et al [9]) was published, in which fuzzy logic based data preprocessing is used, it is composed of two main steps -the use of fuzzy inference rules to transform the gene expression levels of given data set into fuzzy values and the application of similarity relation of these fuzzy values to define fuzzy equivalence groups. Authors studied the performance of different filtering/ranking methods, relevance measures and different classifiers.…”

Section: Introductionmentioning

confidence: 99%

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Using Fuzzy Logic to Solve Bioinformatics Tasks

Gasparovica¹,

Novoselova²,

Aļeksejeva³

2010

Scientific Journal of Riga Technical University. Computer Sciences

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-The goal of this research is to investigate, collect and identify published methods that use fuzzy techniques in bioinformatics tasks. Special attention is paid to studying how the advantages of fuzzy techniques are used in various stages like preprocessing, optimization and building a classifier of classification task as difficult as processing microarray data. This article also inspects the most popular databases used in bioinformatics. The most perspective methods are given more detailed descriptions. Conclusions are made about working abilities of the algorithms and their use in further research.

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“…It is well-known that omics data contain noise due to experimental procedures and biological heterogeneity. An efficient way to reduce the negative effect of noise is to apply fuzzy discretization, using a fuzzy inference system [78]. This technique has been applied to identify set of discriminant genes from gene expression data [79].…”

Section: Feature Selection and Representationmentioning

confidence: 99%

Knowledge Generation with Rule Induction in Cancer Omics

Scala

Federico

Fortino

et al. 2019

IJMS

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The explosion of omics data availability in cancer research has boosted the knowledge of the molecular basis of cancer, although the strategies for its definitive resolution are still not well established. The complexity of cancer biology, given by the high heterogeneity of cancer cells, leads to the development of pharmacoresistance for many patients, hampering the efficacy of therapeutic approaches. Machine learning techniques have been implemented to extract knowledge from cancer omics data in order to address fundamental issues in cancer research, as well as the classification of clinically relevant sub-groups of patients and for the identification of biomarkers for disease risk and prognosis. Rule induction algorithms are a group of pattern discovery approaches that represents discovered relationships in the form of human readable associative rules. The application of such techniques to the modern plethora of collected cancer omics data can effectively boost our understanding of cancer-related mechanisms. In fact, the capability of these methods to extract a huge amount of human readable knowledge will eventually help to uncover unknown relationships between molecular attributes and the malignant phenotype. In this review, we describe applications and strategies for the usage of rule induction approaches in cancer omics data analysis. In particular, we explore the canonical applications and the future challenges and opportunities posed by multi-omics integration problems.

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Fuzzy Logic for Elimination of Redundant Information of Microarray Data

Cited by 29 publications

References 42 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

Using Fuzzy Logic to Solve Bioinformatics Tasks

Knowledge Generation with Rule Induction in Cancer Omics

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