Microarray cancer feature selection: Review, challenges and research directions

Hambali, Moshood A.; Oladele, Tinuke Omolewa; Adewole, Kayode S.

doi:10.1016/j.ijcce.2020.11.001

Cited by 57 publications

(48 citation statements)

References 153 publications

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“…Indeed, in the genomic domain (Figure 2), the hybrid learning approach gives results that are always superior to feature selection alone, as well as superior or comparable to data balancing or cost-sensitive learning alone, but with significant advantages in terms of computational cost and domain understanding, as only the most predictive features are used for prediction. The importance of devising learning strategies that use a reduced number of genes for cancer diagnosis, while ensuring at the same time a good predictive performance, has been widely highlighted in this domain [41,[48][49][50], and the hybrid approaches here discussed seem to provide a viable solution in this respect.…”

Section: Resultsmentioning

confidence: 99%

“…For the genomic domain, we considered two highly imbalanced benchmarks from the GEMLeR collection [40]: the task is to discriminate uterus or omentum cancer from other cancer types, based on the expression level of over ten thousand genes. Since the available instances (i.e., the biological samples) are far fewer than the features (i.e., the genes), this kind of classification task turns out to be especially challenging, as recognized by a vast literature in the field [21,41].…”

Section: Datasets and Settingsmentioning

confidence: 99%

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Learning from High-Dimensional and Class-Imbalanced Datasets Using Random Forests

Pes

2021

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Class imbalance and high dimensionality are two major issues in several real-life applications, e.g., in the fields of bioinformatics, text mining and image classification. However, while both issues have been extensively studied in the machine learning community, they have mostly been treated separately, and little research has been thus far conducted on which approaches might be best suited to deal with datasets that are class-imbalanced and high-dimensional at the same time (i.e., with a large number of features). This work attempts to give a contribution to this challenging research area by studying the effectiveness of hybrid learning strategies that involve the integration of feature selection techniques, to reduce the data dimensionality, with proper methods that cope with the adverse effects of class imbalance (in particular, data balancing and cost-sensitive methods are considered). Extensive experiments have been carried out across datasets from different domains, leveraging a well-known classifier, the Random Forest, which has proven to be effective in high-dimensional spaces and has also been successfully applied to imbalanced tasks. Our results give evidence of the benefits of such a hybrid approach, when compared to using only feature selection or imbalance learning methods alone.

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Section: Resultsmentioning

confidence: 99%

Section: Datasets and Settingsmentioning

confidence: 99%

Learning from High-Dimensional and Class-Imbalanced Datasets Using Random Forests

Pes

2021

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“…Cancer is one of the most fatal diseases around the globe ( Tabares-Soto et al, 2020 ; Hambali, Oladele & Adewole, 2020 ). According to the World Health Organization report, Cancer is marked as the second most deadly disease and an estimated 9.7 million deaths around the world in 2018 have occurred due to this signature disease ( Hambali, Oladele & Adewole, 2020 ). Generally, one in every six deaths all over the world, occurs due to cancer.…”

Section: Introductionmentioning

confidence: 99%

An ensemble machine learning model based on multiple filtering and supervised attribute clustering algorithm for classifying cancer samples

Bose¹,

Das²,

Banerjee³

et al. 2021

PeerJ Computer Science

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Background Machine learning is one kind of machine intelligence technique that learns from data and detects inherent patterns from large, complex datasets. Due to this capability, machine learning techniques are widely used in medical applications, especially where large-scale genomic and proteomic data are used. Cancer classification based on bio-molecular profiling data is a very important topic for medical applications since it improves the diagnostic accuracy of cancer and enables a successful culmination of cancer treatments. Hence, machine learning techniques are widely used in cancer detection and prognosis. Methods In this article, a new ensemble machine learning classification model named Multiple Filtering and Supervised Attribute Clustering algorithm based Ensemble Classification model (MFSAC-EC) is proposed which can handle class imbalance problem and high dimensionality of microarray datasets. This model first generates a number of bootstrapped datasets from the original training data where the oversampling procedure is applied to handle the class imbalance problem. The proposed MFSAC method is then applied to each of these bootstrapped datasets to generate sub-datasets, each of which contains a subset of the most relevant/informative attributes of the original dataset. The MFSAC method is a feature selection technique combining multiple filters with a new supervised attribute clustering algorithm. Then for every sub-dataset, a base classifier is constructed separately, and finally, the predictive accuracy of these base classifiers is combined using the majority voting technique forming the MFSAC-based ensemble classifier. Also, a number of most informative attributes are selected as important features based on their frequency of occurrence in these sub-datasets. Results To assess the performance of the proposed MFSAC-EC model, it is applied on different high-dimensional microarray gene expression datasets for cancer sample classification. The proposed model is compared with well-known existing models to establish its effectiveness with respect to other models. From the experimental results, it has been found that the generalization performance/testing accuracy of the proposed classifier is significantly better compared to other well-known existing models. Apart from that, it has been also found that the proposed model can identify many important attributes/biomarker genes.

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“…Analyzing gene expression data is crucial to find out harmful mutations and to avoid further consequences. [2] In recent years enormous research has been carried out in detecting type of cancer using genomic profiles. Still to achieve satisfactory cancer classification accuracy with the complete set of genes remains a great challenge, due to the high dimensions, small sample size, and presence of noise in gene expression data.…”

Section: Introductionmentioning

confidence: 99%

“…As per the survey done in [2] To solve the curse of dimensionality statistical methods like regression & non-parametric regression methods are used on Leukemia Microarray dataset resulted in better accuracy. [12] Grouping Genetic Algorithm (GGA) implemented on RNA-seq data for five types of cancers gave average accuracy of 98.81 & standard deviation of 0.0174.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Dimensionality Reduction Techniques in Cancer Detection using Microarray Data

Bhonde¹,

Prasad²

2021

AJCT

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Cancer is one of the major causes of deaths worldwide. This disease is more ghastly as it doesn't announce itself until it reaches in an advance stage. Still, mortality rate for cancer can be decreased if we diagnose & provide treatment at earliest. Though there are traditional clinical trials to predict a cancer there does not a single test which can correctly identify this disease. In the recent years DNA Microarray technology has been significantly used to analyze & predict the cancer. Analysis of gene expressions is not only interesting but also challenging as it is not only the concern of accuracy but also matter of life or death of a patient. DNA Microarray data is high dimensional, noisy & redundant, it makes task of classification more complicated as high computational cost is involved. Therefore feature selection & feature reduction becomes important task prior to classification. This paper presents comparative performance analysis of different dimensionality reduction techniques implemented on TCGA PANCANCER dataset.

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Microarray cancer feature selection: Review, challenges and research directions

Cited by 57 publications

References 153 publications

Learning from High-Dimensional and Class-Imbalanced Datasets Using Random Forests

Learning from High-Dimensional and Class-Imbalanced Datasets Using Random Forests

An ensemble machine learning model based on multiple filtering and supervised attribute clustering algorithm for classifying cancer samples

Performance Analysis of Dimensionality Reduction Techniques in Cancer Detection using Microarray Data

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