A Review of Ensemble Classification for DNA Microarrays Data

Khoshgoftaar, Taghi M.; Dittman, David J.; Wald, Randall; Awada, Wael

doi:10.1109/ictai.2013.64

Cited by 15 publications

(6 citation statements)

References 42 publications

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“…Imbalanced data and the small number of samples are other problems of microarray data that challenge the performance of classification models in training and coping with unseen data. One method to cope with such challenges is to use ensemble learning models, which is in good agreement with many studies that have utilized these methods on the microarray datasets [44][45][46][47]. The aim of developing such a system is to offer a trade-off solution between test error and training error in an automated classification model.…”

Section: Introductionsupporting

confidence: 54%

A novel bio-inspired hybrid multi-filter wrapper gene selection method with ensemble classifier for microarray data

Nouri-Moghaddam

Ghazanfari

Fathian

2021

Neural Comput & Applic

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Microarray technology is known as one of the most important tools for collecting DNA expression data. This technology allows researchers to investigate and examine types of diseases and their origins. However, microarray data are often associated with a small sample size, a significant number of genes, imbalanced data, etc., making classification models inefficient. Thus, a new hybrid solution based on a multi-filter and adaptive chaotic multi-objective forest optimization algorithm (AC-MOFOA) is presented to solve the gene selection problem and construct the Ensemble Classifier. In the proposed solution, a multi-filter model (i.e., ensemble filter) is proposed as preprocessing step to reduce the dataset's dimensions, using a combination of five filter methods to remove redundant and irrelevant genes. Accordingly, the results of the five filter methods are combined using a voting-based function. Additionally, the results of the proposed multi-filter indicate that it has good capability in reducing the gene subset size and selecting relevant genes. Then, an AC-MOFOA based on the concepts of non-dominated sorting, crowding distance, chaos theory, and adaptive operators is presented. AC-MOFOA as a wrapper method aimed at reducing dataset dimensions, optimizing KELM, and increasing the accuracy of the classification, simultaneously. Next, in this method, an ensemble classifier model is presented using AC-MOFOA results to classify microarray data. The performance of the proposed algorithm was evaluated on nine public microarray datasets, and its results were compared in terms of the number of selected genes, classification efficiency, execution time, time complexity, hypervolume indicator, and spacing metric with five hybrid multi-objective methods, and three hybrid single-objective methods. According to the results, the proposed hybrid method could increase the accuracy of the KELM in most datasets by reducing the dataset's dimensions and achieve similar or superior performance compared to other multi-objective methods. Furthermore, the proposed Ensemble Classifier model could provide better classification accuracy and generalizability in the seven of nine microarray datasets compared to conventional ensemble methods. Moreover, the comparison results of the Ensemble Classifier model with three state-of-the-art ensemble generation methods indicate its competitive performance in which the proposed ensemble model achieved better results in the five of nine datasets. Supplementary Information The online version contains supplementary material available at 10.1007/s00521-021-06459-9.

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Section: Introductionsupporting

confidence: 54%

A novel bio-inspired hybrid multi-filter wrapper gene selection method with ensemble classifier for microarray data

Nouri-Moghaddam

Ghazanfari

Fathian

2021

Neural Comput & Applic

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“…The choice of the RF algorithm for this work is due to its ability to find complicated patterns in data and improve classification with less overfitting compared to other models ( Breiman, 2001 ; Khoshgoftaar et al, 2013 ; Maurya et al, 2021 ). RFs have also been shown to be robust to noise and perform better on imbalanced data sets ( Breiman, 2001 ; Khoshgoftaar et al, 2007 ; Khoshgoftaar et al, 2013 ). Even when including highly imbalanced cohorts, the RF model had better performance metrics than the published XGBoost model used to classify p53 pathway activity ( Zhang et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

A p53 transcriptional signature in primary and metastatic cancers derived using machine learning

et al. 2022

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The tumor suppressor gene, TP53, has the highest rate of mutation among all genes in human cancer. This transcription factor plays an essential role in the regulation of many cellular processes. Mutations in TP53 result in loss of wild-type p53 function in a dominant negative manner. Although TP53 is a well-studied gene, the transcriptome modifications caused by the mutations in this gene have not yet been explored in a pan-cancer study using both primary and metastatic samples. In this work, we used a random forest model to stratify tumor samples based on TP53 mutational status and detected a p53 transcriptional signature. We hypothesize that the existence of this transcriptional signature is due to the loss of wild-type p53 function and is universal across primary and metastatic tumors as well as different tumor types. Additionally, we showed that the algorithm successfully detected this signature in samples with apparent silent mutations that affect correct mRNA splicing. Furthermore, we observed that most of the highly ranked genes contributing to the classification extracted from the random forest have known associations with p53 within the literature. We suggest that other genes found in this list including GPSM2, OR4N2, CTSL2, SPERT, and RPE65 protein coding genes have yet undiscovered linkages to p53 function. Our analysis of time on different therapies also revealed that this signature is more effective than the recorded TP53 status in detecting patients who can benefit from platinum therapies and taxanes. Our findings delineate a p53 transcriptional signature, expand the knowledge of p53 biology and further identify genes important in p53 related pathways.

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“…Machine learning refers to the development, analysis (Khoshgoftaar et al, 2013) and implementation of methods that allow a machine to evolve through a learning process, and thus to perform tasks that are difficult or impossible to perform by more conventional algorithmic means.…”

Section: Machine Learningmentioning

confidence: 99%

Machine learning methods against false data injection in smart grid

Hamlich

Khantach

Belbounaguia

2020

IJRIS

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The false data injection in the power grid is a major risk for a good and safety functioning of the smart grid. The false data detection with conventional methods are incapable to detect some false measurements, to remedy this, we have opted to use machine learning which we used five classifiers to conceive an effective detection [k-nearest neighbour (KNN) algorithm, random trees, random forest decision trees, multilayer perceptron and support vector machine]. Our analysis is validated by experiments on a physical bus feeding system performed on PSS/in which we have developed a dataset for real measurement. Afterward we worked with MATLAB software to construct false measurements according to the Jacobean matrix of the state estimation. We tested the collected data with different classification algorithms, which gives good and satisfactory results.

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A Review of Ensemble Classification for DNA Microarrays Data

Cited by 15 publications

References 42 publications

A novel bio-inspired hybrid multi-filter wrapper gene selection method with ensemble classifier for microarray data

A novel bio-inspired hybrid multi-filter wrapper gene selection method with ensemble classifier for microarray data

A p53 transcriptional signature in primary and metastatic cancers derived using machine learning

Machine learning methods against false data injection in smart grid

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