Support vector machine and principal component analysis for microarray data classification

Astuti, Widi; Adiwijaya, Adiwijaya

doi:10.1088/1742-6596/971/1/012003

Cited by 21 publications

(8 citation statements)

References 6 publications

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“…A prior study has illustrated the effect of a pre-PCA step on the accuracy of different SVM models for microarray data for colon cancer. 66 It was reported that PCA increased model performance in terms of accuracy and running time. Figure 3D shows the results of the PCA analysis of 20 spectral features.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

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Discriminatory Detection of ssDNA by Surface-Enhanced Raman Spectroscopy (SERS) and Tree-Based Support Vector Machine (Tr-SVM)

2021

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We report label-free detection of 86-base single-stranded DNA (ssDNA) gene segments by surface-enhanced Raman spectroscopy (SERS). The use of a slippery liquid infused porous (SLIP) membrane induced aggregation of 43 nm gold nanoparticles and ssDNA upon pin-free droplet evaporation. The combined SLIPSERS approach generates significant numbers of SERS hot-spots and enabled detection at the 100 nM level of mecA and intI1 gene segmentstwo genes of interest in the context of antibiotic resistance. Tree-based multiclass support vector machine (Tr-SVM) classifiers were built to discriminate SERS spectra of 12 different gene sequences obtained by SLIPSERS: mecA, intI1, as well as analogues of mecA and intI1, respectively, with 2–10 base mismatches, and two random sequences. The trained predictive Tr-SVM classifiers correctly identified each gene sequence with a prediction accuracy of ∼90%. This study illustrates a novel means for discriminatory label-free SERS detection of ssDNA enabled by Tr-SVM.

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Section: ■ Results and Discussionmentioning

confidence: 99%

“…PCA was preconducted to reduce the dataset dimensionality. A prior study has illustrated the effect of a pre-PCA step on the accuracy of different SVM models for microarray data for colon cancer . It was reported that PCA increased model performance in terms of accuracy and running time.…”

Section: Resultsmentioning

confidence: 99%

Discriminatory Detection of ssDNA by Surface-Enhanced Raman Spectroscopy (SERS) and Tree-Based Support Vector Machine (Tr-SVM)

2021

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“…Using the selected transcripts, we trained the classification models using MATLAB Statistics and Machine Learning Toolbox. Specifically, we consider four algorithms which are widely used for disease classification from expression data, including K -nearest neighbors (KNN) [12], random forest (RF) [13], support vector machine with cubic kernel (cSVM) [14], and SVM with Gaussian kernel (gSVM) [15]. We briefly describe each method here.…”

Section: Methodsmentioning

confidence: 99%

The feasibility of developing biomarkers from peripheral blood mononuclear cell RNAseq data in children with juvenile idiopathic arthritis using machine learning approaches

Poppenberg

Jiang

et al. 2019

Arthritis Res Ther

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BackgroundThe response to treatment for juvenile idiopathic arthritis (JIA) can be staged using clinical features. However, objective laboratory biomarkers of remission are still lacking. In this study, we used machine learning to predict JIA activity from transcriptomes from peripheral blood mononuclear cells (PBMCs). We included samples from children with Native American ancestry to determine whether the model maintained validity in an ethnically heterogeneous population.MethodsOur dataset consisted of 50 samples, 23 from children in remission and 27 from children with an active disease on therapy. Nine of these samples were from children with mixed European/Native American ancestry. We used 4 different machine learning methods to create predictive models in 2 populations: the whole dataset and then the samples from children with exclusively European ancestry.ResultsIn both populations, models were able to predict JIA status well, with training accuracies > 74% and testing accuracies > 78%. Performance was better in the whole dataset model. We note a high degree of overlap between genes identified in both populations. Using ingenuity pathway analysis, genes from the whole dataset associated with cell-to-cell signaling and interactions, cell morphology, organismal injury and abnormalities, and protein synthesis.ConclusionsThis study demonstrates it is feasible to use machine learning in conjunction with RNA sequencing of PBMCs to predict JIA stage. Thus, developing objective biomarkers from easy to obtain clinical samples remains an achievable goal.

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“…The outcome showed that when Linear and Cubic kernel functions are used, the scheme can achieve 100 percent accuracy for Ovarian and Lung Cancer data. The PCA substantially decreased the running period for all data sets in terms of running time [82]. Also, a research by [83], combined multiple omics datasets from similar biological hypotheses and presented two different types of PCA meta-analysis framework, namely, Meta PCA.…”

Section: Review For Pca Algorithmmentioning

confidence: 99%

A Review of Principal Component Analysis Algorithm for Dimensionality Reduction

Hasan

Abdulazeez

2021

JSCDM

123

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Big databases are increasingly widespread and are therefore hard to understand, in exploratory biomedicine science, big data in health research is highly exciting because data-based analyses can travel quicker than hypothesis-based research. Principal Component Analysis (PCA) is a method to reduce the dimensionality of certain datasets. Improves interpretability but without losing much information. It achieves this by creating new covariates that are not related to each other. Finding those new variables, or what we call the main components, will reduce the eigenvalue /eigenvectors solution problem. (PCA) can be said to be an adaptive data analysis technology because technology variables are developed to adapt to different data types and structures. This review will start by introducing the basic ideas of (PCA), describe some concepts related to (PCA), and discussing. What it can do, and reviewed fifteen articles of (PCA) that have been introduced and published in the last three years.

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Support vector machine and principal component analysis for microarray data classification

Cited by 21 publications

References 6 publications

Discriminatory Detection of ssDNA by Surface-Enhanced Raman Spectroscopy (SERS) and Tree-Based Support Vector Machine (Tr-SVM)

Discriminatory Detection of ssDNA by Surface-Enhanced Raman Spectroscopy (SERS) and Tree-Based Support Vector Machine (Tr-SVM)

The feasibility of developing biomarkers from peripheral blood mononuclear cell RNAseq data in children with juvenile idiopathic arthritis using machine learning approaches

A Review of Principal Component Analysis Algorithm for Dimensionality Reduction

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