Dimension reduction methods for microarray data: a review

Aziz, Rabia Musheer; Verma, Chandan Kumar; Srivastava, Namita

doi:10.3934/bioeng.2017.1.179

Cited by 46 publications

(24 citation statements)

References 35 publications

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“…In this point of view, filter methods have a lot of flexibility as they can be combined with not only any learning algorithm, but also any gene selection method, such as a wrapper method, resulting in a hybrid method. The performance of a hybrid method relies totally on the combination of filter and wrapper methods as well as the classifier [ 18 ]. We believe that accurate gene selection by filter methods clearly allow better classification accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…These feature selection methods can be mainly classified into four categories depending on how they are combined with learning algorithms in classification tasks: filter, wrapper, embedded, and hybrid methods. For details and the corresponding examples of these methods, we refer the reader to several review papers [ 10 – 18 ]. As many researchers commented, filter methods have been dominant over the past decades due to its strong advantages, although they are the earliest in the literature [ 11 – 13 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

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Robust gene selection methods using weighting schemes for microarray data analysis

Kang

Song

2017

BMC Bioinformatics

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BackgroundA common task in microarray data analysis is to identify informative genes that are differentially expressed between two different states. Owing to the high-dimensional nature of microarray data, identification of significant genes has been essential in analyzing the data. However, the performances of many gene selection techniques are highly dependent on the experimental conditions, such as the presence of measurement error or a limited number of sample replicates.ResultsWe have proposed new filter-based gene selection techniques, by applying a simple modification to significance analysis of microarrays (SAM). To prove the effectiveness of the proposed method, we considered a series of synthetic datasets with different noise levels and sample sizes along with two real datasets. The following findings were made. First, our proposed methods outperform conventional methods for all simulation set-ups. In particular, our methods are much better when the given data are noisy and sample size is small. They showed relatively robust performance regardless of noise level and sample size, whereas the performance of SAM became significantly worse as the noise level became high or sample size decreased. When sufficient sample replicates were available, SAM and our methods showed similar performance. Finally, our proposed methods are competitive with traditional methods in classification tasks for microarrays.ConclusionsThe results of simulation study and real data analysis have demonstrated that our proposed methods are effective for detecting significant genes and classification tasks, especially when the given data are noisy or have few sample replicates. By employing weighting schemes, we can obtain robust and reliable results for microarray data analysis.Electronic supplementary materialThe online version of this article (10.1186/s12859-017-1810-x) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust gene selection methods using weighting schemes for microarray data analysis

Kang

Song

2017

BMC Bioinformatics

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“…us, a process that can reduce the dimensionality complexity of this type of data is required. In addition, a dimensionality reduction step will minimize errors obtained in the subsequent classification stage [1,12,[33][34][35].…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Particle Swarm Optimized Hybrid Kernel-Based Multiclass Support Vector Machine for Microarray Cancer Data Analysis

Segera

Mbuthia

Nyete

2019

BioMed Research International

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Determining an optimal decision model is an important but difficult combinatorial task in imbalanced microarray-based cancer classification. Though the multiclass support vector machine (MCSVM) has already made an important contribution in this field, its performance solely depends on three aspects: the penalty factor C, the type of kernel, and its parameters. To improve the performance of this classifier in microarray-based cancer analysis, this paper proposes PSO-PCA-LGP-MCSVM model that is based on particle swarm optimization (PSO), principal component analysis (PCA), and multiclass support vector machine (MCSVM). The MCSVM is based on a hybrid kernel, i.e., linear-Gaussian-polynomial (LGP) that combines the advantages of three standard kernels (linear, Gaussian, and polynomial) in a novel manner, where the linear kernel is linearly combined with the Gaussian kernel embedding the polynomial kernel. Further, this paper proves and makes sure that the LGP kernel confirms the features of a valid kernel. In order to reveal the effectiveness of our model, several experiments were conducted and the obtained results compared between our model and other three single kernel-based models, namely, PSO-PCA-L-MCSVM (utilizing a linear kernel), PSO-PCA-G-MCSVM (utilizing a Gaussian kernel), and PSO-PCA-P-MCSVM (utilizing a polynomial kernel). In comparison, two dual and two multiclass imbalanced standard microarray datasets were used. Experimental results in terms of three extended assessment metrics (F-score, G-mean, and Accuracy) reveal the superior global feature extraction, prediction, and learning abilities of this model against three single kernel-based models.

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“…The common methods that are used in filter based approaches are pearson's correlation coefficient, classifier based filters, information gain, gain ratio and relief algorithm. However, the multivariate filters such as correlation based filters are slower, less scalable and may include redundant features whereas the univariate filters such as information gain and gain ratio ignore the attribute dependencies and consider them independently [21]. Another main drawback of the univariate, multivariate and Relief method is that there is no interaction with the classifiers [22].…”

Section: Feature Selectionmentioning

confidence: 99%

Feature Selection using Optimized Multiple Rank Score Model for Credit Scoring

Rajaleximi¹,

Ahmed²,

Alenezi³

2019

IJIES

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Fraud identification and prevention is becoming the decisive challenge for any financial institutions and financial service industries. Majority of the banks use a credit score, a numerical or statistical value to estimate the customer's creditworthiness based on their credit history. A credit score model will be built by employing training dataset and further, an analytical process will be conceded to estimate the credit score of each customer. Thus, for any credit score model, the lenders acquire various data about the customer from various external agencies. The collected data may encompass irrelevant parameters which will not help in making any decisions and also decelerates the global performance of the model. Accordingly, feature selection is an imperative process to eliminate less relevant attributes for any dataset, especially in credit scoring. This paper emphasizes on enlightening the performance of the attribute selection process using multiple rank score model. The proposed method accumulates the results using the optimized threshold algorithm and outperforms well in selecting the quality attribute for the underlying credit score model. The experimental evaluation has been carried out and it is proved that the accuracy and performance of the suggested method are comparatively better than the existing single rank techniques.

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Dimension reduction methods for microarray data: a review

Cited by 46 publications

References 35 publications

Robust gene selection methods using weighting schemes for microarray data analysis

Robust gene selection methods using weighting schemes for microarray data analysis

Particle Swarm Optimized Hybrid Kernel-Based Multiclass Support Vector Machine for Microarray Cancer Data Analysis

Feature Selection using Optimized Multiple Rank Score Model for Credit Scoring

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