An expert system to classify microarray gene expression data using gene selection by decision tree

Horng, Jorng-Tzong; Wu, Liang; Liu, Baw-Juine; Kuo, Jun-Li; Kuo, Wen-Horng; Zhang, Jin-Jian

doi:10.1016/j.eswa.2008.12.037

Cited by 55 publications

(26 citation statements)

References 28 publications

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“…Hence, we review and discuss some representative methods to provide inspiring examples to illustrate how BI techniques can be applied to solve medical watermarking problems. We want to stress that the literature in this domain is of course huge and that therefore the work that we include here is only exemplatory and focussing on recent advances, while many other interesting research approaches had to be omitted due to space limitations [4].…”

Section: Introductionmentioning

confidence: 99%

Bio-inspiring Techniques in Watermarking Medical Images: A Review

Soliman

Hassanien

Onsi

2014

Bio-Inspiring Cyber Security and Cloud Services: Trends and Innovations

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Bio-inspiring (BI) is a well-established paradigm with current systems having many of the characteristics of biological computers and capable of performing a variety of tasks that are difficult to do using conventional techniques. BI is considered as one of the most important increasing fields, which attract a large number of researchers and scientists working in areas such as neuro-computing, global optimization, swarms and evolutionary computing. On the other hand, digital radiological modalities in modern hospitals have led to the producing a variety of a vast amount of digital medical files. Therefore, for the medical imaging, the authenticity needs to ensure the image belongs to the correct patient, the integrity check to ensure the image has not been modified, and safe transfer are very big challenges. The integrity of the images must be protected by using watermarking, which is called integrity watermark. At the same time the copyright and intellectual property of the medical images should be also protected, which is called copyright watermark. This chapter presents a brief overview of well known Bio-inspiring techniques including neural networks, genetic algorithm, swarms and evolutionary algorithms and show how BI techniques could be successfully employed to solve watermarking problem. Challenges to be addressed and future directions of research are also presented and an extensive bibliography is included.

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Section: Introductionmentioning

confidence: 99%

Bio-inspiring Techniques in Watermarking Medical Images: A Review

Soliman

Hassanien

Onsi

2014

Bio-Inspiring Cyber Security and Cloud Services: Trends and Innovations

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“…Another research on dimension reduction was conveyed by Horng et al [17]. They introduced a new method of gene selection based on C4.5 algorithm.…”

Section: A Algorithms C45 and Cartmentioning

confidence: 99%

Decision Tree Classifiers in Bioinformatics

Poļaka¹,

Tom²,

Borisov³

2010

Scientific Journal of Riga Technical University. Computer Sciences

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-This paper presents a literature review of articles related to the use of decision tree classifiers in gene microarray data analysis published in the last ten years. The main focus is on researches solving the cancer classification problem using single decision tree classifiers (algorithms C4.5 and CART) and decision tree forests (e.g. random forests) showing strengths and weaknesses of the proposed methodologies when compared to other popular classification methods. The article also touches the use of decision tree classifiers in gene selection.

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“…A gene expression dataset contains thousands of gene expression values, many of which may be redundant or irrelevant for classification [11]. Leaving out relevant attributes or keeping irrelevant attributes may affect the performance of the classification algorithm.…”

Section: Data Normalization and Feature Selectionmentioning

confidence: 99%

Effect of intuitionistic fuzzy normalization in microarray gene selection

2018

Turk J Elec Eng & Comp Sci

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Abstract:Analysis of gene expression data is essential in microarray gene expression in order to retrieve the required information. Gene expression data generally contain a large number of genes but a small number of samples. The complicated relations among the different genes make analysis more difficult, and removing irrelevant genes improves the quality of results. This paper presents two fuzzy preprocessing techniques, using a fuzzy set (FS) and intuitionistic fuzzy set (IFS), to normalize datasets. In the feature selection part, four statistical methods were used. Using three publicly available gene expression datasets, the fuzzy normalization techniques were compared with two standard normalization techniques (min-max and Z-score) as well as raw gene expression. The classifiers of support vector machine, k-nearestneighbor, and random forest were used to identify the accuracy of selected features. The experimental results show that the genes selected using FS-and IFS-normalized datasets give high classification accuracy; in addition, IFS outperforms FS normalization.

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An expert system to classify microarray gene expression data using gene selection by decision tree

Cited by 55 publications

References 28 publications

Bio-inspiring Techniques in Watermarking Medical Images: A Review

Bio-inspiring Techniques in Watermarking Medical Images: A Review

Decision Tree Classifiers in Bioinformatics

Effect of intuitionistic fuzzy normalization in microarray gene selection

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