Survey on different Methods for Classifying Gene Expression using Microarray Approach

Mashhour, Emad Mohamed; El-Houby, Enas M. Fahmy; Wassif, Khaled; Salah, Akram

doi:10.5120/ijca2016911441

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…With this approach, it is possible to select features tailored for the induction algorithm ( Jadhav et al, 2018 ). The classification algorithm’s evaluation measures will be optimized while eliminating the features, hence offering better accuracy than the filter approach ( Inza et al, 2004 ; Mohamed et al, 2016 ).…”

Section: Gene Selection – Background and Developmentmentioning

confidence: 99%

“…The significant advantage of using a wrapper approach, as both feature subset generation and the induction algorithm are wrapped together; the model will have the ability to track the feature dependencies ( Rodrigues et al, 2014 ). The common drawback is that it becomes computationally intensive for datasets with high dimensions ( Mohamed et al, 2016 ). Examples of Wrapper techniques are Hill Climbing, Forward Selection, and Backward Elimination.…”

Section: Gene Selection – Background and Developmentmentioning

confidence: 99%

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Machine Learning Based Computational Gene Selection Models: A Survey, Performance Evaluation, Open Issues, and Future Research Directions

et al. 2020

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Gene Expression is the process of determining the physical characteristics of living beings by generating the necessary proteins. Gene Expression takes place in two steps, translation and transcription. It is the flow of information from DNA to RNA with enzymes’ help, and the end product is proteins and other biochemical molecules. Many technologies can capture Gene Expression from the DNA or RNA. One such technique is Microarray DNA. Other than being expensive, the main issue with Microarray DNA is that it generates high-dimensional data with minimal sample size. The issue in handling such a heavyweight dataset is that the learning model will be over-fitted. This problem should be addressed by reducing the dimension of the data source to a considerable amount. In recent years, Machine Learning has gained popularity in the field of genomic studies. In the literature, many Machine Learning-based Gene Selection approaches have been discussed, which were proposed to improve dimensionality reduction precision. This paper does an extensive review of the various works done on Machine Learning-based gene selection in recent years, along with its performance analysis. The study categorizes various feature selection algorithms under Supervised, Unsupervised, and Semi-supervised learning. The works done in recent years to reduce the features for diagnosing tumors are discussed in detail. Furthermore, the performance of several discussed methods in the literature is analyzed. This study also lists out and briefly discusses the open issues in handling the high-dimension and less sample size data.

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Section: Gene Selection – Background and Developmentmentioning

confidence: 99%

Section: Gene Selection – Background and Developmentmentioning

confidence: 99%

Machine Learning Based Computational Gene Selection Models: A Survey, Performance Evaluation, Open Issues, and Future Research Directions

et al. 2020

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“…Another common way to address the data high-dimensionality challenge is to use classification algorithms that control the model’s complexity through regularization [ 16 , 17 ]. One option is to regularize the log-likelihood function of the LR model.…”

Section: Introductionmentioning

confidence: 99%

Identification of biomarkers predictive of metastasis development in early-stage colorectal cancer using network-based regularization

Peixoto

Lopes

Martins³

et al. 2023

BMC Bioinformatics

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Colorectal cancer (CRC) is the third most common cancer and the second most deathly worldwide. It is a very heterogeneous disease that can develop via distinct pathways where metastasis is the primary cause of death. Therefore, it is crucial to understand the molecular mechanisms underlying metastasis. RNA-sequencing is an essential tool used for studying the transcriptional landscape. However, the high-dimensionality of gene expression data makes selecting novel metastatic biomarkers problematic. To distinguish early-stage CRC patients at risk of developing metastasis from those that are not, three types of binary classification approaches were used: (1) classification methods (decision trees, linear and radial kernel support vector machines, logistic regression, and random forest) using differentially expressed genes (DEGs) as input features; (2) regularized logistic regression based on the Elastic Net penalty and the proposed iTwiner—a network-based regularizer accounting for gene correlation information; and (3) classification methods based on the genes pre-selected using regularized logistic regression. Classifiers using the DEGs as features showed similar results, with random forest showing the highest accuracy. Using regularized logistic regression on the full dataset yielded no improvement in the methods’ accuracy. Further classification using the pre-selected genes found by different penalty factors, instead of the DEGs, significantly improved the accuracy of the binary classifiers. Moreover, the use of network-based correlation information (iTwiner) for gene selection produced the best classification results and the identification of more stable and robust gene sets. Some are known to be tumor suppressor genes (OPCML-IT2), to be related to resistance to cancer therapies (RAC1P3), or to be involved in several cancer processes such as genome stability (XRCC6P2), tumor growth and metastasis (MIR602) and regulation of gene transcription (NME2P2). We show that the classification of CRC patients based on pre-selected features by regularized logistic regression is a valuable alternative to using DEGs, significantly increasing the models’ predictive performance. Moreover, the use of correlation-based penalization for biomarker selection stands as a promising strategy for predicting patients’ groups based on RNA-seq data.

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A Gene Selection Method Considering Measurement Errors

Lee,

Kim

2024

Journal of Computational Biology

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Survey on different Methods for Classifying Gene Expression using Microarray Approach

Cited by 7 publications

References 22 publications

Machine Learning Based Computational Gene Selection Models: A Survey, Performance Evaluation, Open Issues, and Future Research Directions

Machine Learning Based Computational Gene Selection Models: A Survey, Performance Evaluation, Open Issues, and Future Research Directions

Identification of biomarkers predictive of metastasis development in early-stage colorectal cancer using network-based regularization

A Gene Selection Method Considering Measurement Errors

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