Effective Data Dimensionality Reduction Workflow for High-Dimensional Gene Expression Datasets

Das, Utsha; Srizon, Azmain Yakin; Hasan, Md. Al Mehedi; Rahman, Julia; Islam, Khaled Ben

doi:10.1109/tensymp50017.2020.9230847

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…The RFE selection feature used in previous research applies random forest with cross-validation to maintain the height of accuracy of large data [14]. Then in research [15] examined several novels with random by measuring using mutual information and RFE with Support Vector Machine (SVM) as the estimator.…”

Section: Methodsmentioning

confidence: 99%

Comparison of Genetic Algorithm and Recursive Feature Elimination on High Dimensional Data

Pristyanto,

Wirantanu

2024

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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The use of big data in companies is currently used in file processing. With large capacity files, it can affect the performance in terms of time in the company, so to overcome the problem of high-dimensional data, feature selection is used in selecting the number of features. On the WDC dataset with 30 features and 569 data points, feature selection is performed using the Recusive Feature Elimination (RFE) and Genetic Algorithm (GA) models. Then a comparison of evaluation values is made to determine which feature selection is best for solving the problem. From the 14 tables of evaluation results and discussion in tables 1 to 14, it is found that in the evaluation of accuracy and the use of weighted macros on precision, recall, and f1 score, using GA selection features has slightly higher results than RFE, so it is concluded that GA selection features are better at solving problems in high-dimensional data.

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Section: Methodsmentioning

confidence: 99%

Comparison of Genetic Algorithm and Recursive Feature Elimination on High Dimensional Data

Pristyanto,

Wirantanu

2024

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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“…As PCA is the traditional yet effective dimensionality reduction technique for high dimensional datasets with its ability to project a large number of features to small feature space while preserving as much information as possible, it has been widely used for bioinformatics studies 17,32 . It can predict a sequence of the best linear combinations based on the original attributes of a certain set and reveals a new and reduced set of variables, determined to be the principal components, while also ensuring that little data from the original set is excluded from the analysis.…”

Section: Pca As Dimensionality Reductionmentioning

confidence: 99%

Improving the robustness and stability of a machine learning model for breast cancer prognosis through the use of multi-modal classifiers

Arya

Saha

Mathur³

et al. 2023

Sci Rep

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Breast cancer is a deadly disease with a high mortality rate among PAN cancers. The advancements in biomedical information retrieval techniques have been beneficial in developing early prognosis and diagnosis systems for cancer patients. These systems provide the oncologist with plenty of information from several modalities to make the correct and feasible treatment plan for breast cancer patients and protect them from unnecessary therapies and their toxic side effects. The cancer patient’s related information can be collected using various modalities like clinical, copy number variation, DNA-methylation, microRNA sequencing, gene expression, and histopathological whole slide images. High dimensionality and heterogeneity in these modalities demand the development of some intelligent systems to understand related features to the prognosis and diagnosis of diseases and make correct predictions. In this work, we have studied some end-to-end systems having two main components : (a) dimensionality reduction techniques applied to original features from different modalities and (b) classification techniques applied to the fusion of reduced feature vectors from different modalities for automatic predictions of breast cancer patients into two categories: short-time and long-time survivors. Principal component analysis (PCA) and variational auto-encoders (VAEs) are used as the dimensionality reduction techniques, followed by support vector machines (SVM) or random forest as the machine learning classifiers. The study utilizes raw, PCA, and VAE extracted features of the TCGA-BRCA dataset from six different modalities as input to the machine learning classifiers. We conclude this study by suggesting that adding more modalities to the classifiers provides complementary information to the classifier and increases the stability and robustness of the classifiers. In this study, the multimodal classifiers have not been validated on primary data prospectively.

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Effective Data Dimensionality Reduction Workflow for High-Dimensional Gene Expression Datasets

Cited by 2 publications

References 17 publications

Comparison of Genetic Algorithm and Recursive Feature Elimination on High Dimensional Data

Comparison of Genetic Algorithm and Recursive Feature Elimination on High Dimensional Data

Improving the robustness and stability of a machine learning model for breast cancer prognosis through the use of multi-modal classifiers

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