Microarray gene expression classification with few genes: Criteria to combine attribute selection and classification methods

Alonso-González, Carlos J.; Moro-Sancho, Q. Isaac; Simon-Hurtado, Arancha; Varela-Arrabal, Ricardo

doi:10.1016/j.eswa.2012.01.096

Cited by 43 publications

(17 citation statements)

References 34 publications

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“…In [33], an ensemble of five filters (IG, correlation-based feature selection (CFS), consistencybased, interaction, and ReliefF) and three classifiers (naïve Bayes, C4.5, and IB1) was proposed. The researchers used a simple voting scheme for classification.…”

Section: The Proposed Methodologymentioning

confidence: 99%

“…The proposed system was evaluated using skewed cancer gene expression datasets downloaded from the Kent Ridge Bio-Medical Data Set website (http://datam.i2r.a-star.edu.sq/datasets/krbd/) [33]. The Kent Ridge Bio-Medical Data Set Repository is an online repository of high-dimensional biomedical datasets, including gene expression data, protein-profiling data and genomic sequence data that are related to classification.…”

Section: Microarray Datasetsmentioning

confidence: 99%

“…The test matrix contains 12 relapse instances and seven non-relapse instances. For each test, two important criteria were utilized for observational assessment of the performance: the number of selected genes (features) and the classification accuracy [33].…”

Section: Microarray Datasetsmentioning

confidence: 99%

“…Note that the genes are organized in the matrix in order of descending minimal intensity. This means that the expression values are not normalized with respect to the mean intensity in each experiment [15,33,34].…”

Section: Microarray Datasetsmentioning

confidence: 99%

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Breast and Colon Cancer Classification from Gene Expression Profiles Using Data Mining Techniques

et al. 2020

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Early detection of cancer increases the probability of recovery. This paper presents an intelligent decision support system (IDSS) for the early diagnosis of cancer based on gene expression profiles collected using DNA microarrays. Such datasets pose a challenge because of the small number of samples (no more than a few hundred) relative to the large number of genes (in the order of thousands). Therefore, a method of reducing the number of features (genes) that are not relevant to the disease of interest is necessary to avoid overfitting. The proposed methodology uses the information gain (IG) to select the most important features from the input patterns. Then, the selected features (genes) are reduced by applying the grey wolf optimization (GWO) algorithm. Finally, the methodology employs a support vector machine (SVM) classifier for cancer type classification. The proposed methodology was applied to two datasets (Breast and Colon) and was evaluated based on its classification accuracy, which is the most important performance measure in disease diagnosis. The experimental results indicate that the proposed methodology is able to enhance the stability of the classification accuracy as well as the feature selection.Symmetry 2020, 12, 408 2 of 15 the age of 20 in the United States. After leukaemia, brain cancer and others, tumours of the CNS are the second most common type of cancer among children; the rate of such tumours has never reached more than 26% among children under the age of one year [1,2]. In 2019, 1,762,450 new cancer cases of brain and other nervous system tumours were reported in the United States, and the number of associated deaths was estimated to be 606,880. Thus, it is important to develop a methodology of detecting cancer in the early stages before the tumour worsens, thereby reducing the risk of death [3].The conventional methods of diagnosing most existing diseases depend on human experience to recognize cases that correspond to confirmed data patterns. However, this age-old diagnosis methodology is subject to human error and imprecise diagnosis and is both time-consuming and labour-intensive, thus causing undue stress throughout the whole process. As an alternative, computer-aided diagnosis (CAD) systems based on machine learning have been continually improving and are employed to support specialists in the determination of diagnosis decisions [3][4][5].Most current CAD systems for medical diagnosis depend on diverse information, such as medical laboratory tests (e.g., blood tests and magnetic resonance imaging (MRI)), medical indicators (finger tremors and lung signs or symptoms), and various types of digital images (such as X-rays and ultrasound images). However, physical medical examinations pose a risk of transmission of infection through tools and other channels, such as scratching of the skin while taking a blood sample [6][7][8]. X-rays are harmful because of the exposure of body cells to radiation. The quality of ultrasound data depends on the accuracy and integrity of the image, whi...

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Section: The Proposed Methodologymentioning

confidence: 99%

Section: Microarray Datasetsmentioning

confidence: 99%

Section: Microarray Datasetsmentioning

confidence: 99%

Section: Microarray Datasetsmentioning

confidence: 99%

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Breast and Colon Cancer Classification from Gene Expression Profiles Using Data Mining Techniques

et al. 2020

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“…Recently, Alonso et al [2] have proposed the relaxation of maximum accuracy criteria to select such combination of feature selection and classification algorithm that reduce the number of selected features. Their work involves three feature selection methods: two filter methods and one hybrid i.e.…”

Section: Related Workmentioning

confidence: 99%

An incremental feature selection approach based on scatter matrices for classification of cancer microarray data

Sardana

Agrawal

Kaur

2014

International Journal of Computer Mathematics

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Microarray data are often characterized by high dimension and small sample size. There is a need to reduce its dimension for better classification performance and computational efficiency of the learning model. The minimum redundancy and maximum relevance (mRMR), which is widely explored to reduce the dimension of the data, requires discretization and setting of external parameters. We propose an incremental formulation of the trace of ratio of the scatter matrices to determine a relevant set of genes which does not involve discretization and external parameter setting. It is analytically shown that the proposed incremental formulation is computationally efficient in comparison to its batch formulation. Extensive experiments on 14 well-known available microarray cancer datasets demonstrate that the performance of the proposed method is better in comparison to the well-known mRMR method. Statistical tests also show that the proposed method is significantly better when compared to the mRMR method.

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Classification of Microarray Data

Sánchez-Maroño

Fontenla-Romero

Pérez-Sánchez

2019

Methods in Molecular Biology

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Microarray gene expression classification with few genes: Criteria to combine attribute selection and classification methods

Cited by 43 publications

References 34 publications

Breast and Colon Cancer Classification from Gene Expression Profiles Using Data Mining Techniques

Breast and Colon Cancer Classification from Gene Expression Profiles Using Data Mining Techniques

An incremental feature selection approach based on scatter matrices for classification of cancer microarray data

Classification of Microarray Data

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