Analyzing high dimensional correlated data using feature ranking and classifiers

Patil, Abhijeet R.; Leung, Ming-Ying; Kim, Sangjin

doi:10.1515/cmb-2019-0008

Cited by 7 publications

(5 citation statements)

References 42 publications

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“…(iii) Due to partial probe missing in the TCGA data, we did not take into account the local methylation correlations (i.e., significant correlations in methylation levels in neighboring regions) in this study, as it could lead to the loss of information related to methylation cascade regions. We plan to address this issue using more sophisticated methods in our future research [34][35][36]. (iv) Moreover, according to UMAP cluster results, it can be determined that there are relatively obvious differences between samples from different databases, which can be attributed to the different methods used to correct and normalize methylation data [37].…”

Section: Discussionmentioning

confidence: 99%

Hierarchical classification-based pan-cancer methylation analysis to classify primary cancer

Yang,

Zeng,

Liu

et al. 2023

BMC Bioinformatics

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Hierarchical classification offers a more specific categorization of data and breaks down large classification problems into subproblems, providing improved prediction accuracy and predictive power for undefined categories, while also mitigating the impact of poor-quality data. Despite these advantages, its application in predicting primary cancer is rare. To leverage the similarity of cancers and the specificity of methylation patterns among them, we developed the Cancer Hierarchy Classification Tool (CHCT) using the idea of hierarchical classification, with methylation data from 30 cancer types and 8239 methylome samples downloaded from publicly available databases (The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO)). We used unsupervised clustering to divide the classification subproblems and screened differentially methylated sites using Analysis of variance (ANOVA) test, Tukey-kramer test, and Boruta algorithms to construct models for each classifier module. After validation, CHCT accurately classified 1568 out of 1660 cases in the test set, with an average accuracy of 94.46%. We further curated an independent validation cohort of 677 cancer samples from GEO and assigned a diagnosis using CHCT, which showed high diagnostic potential with generally high accuracies (an average accuracy of 91.40%). Moreover, CHCT demonstrates predictive capability for additional cancer types beyond its original classifier scope as demonstrated in the medulloblastoma and pituitary tumor datasets. In summary, CHCT can hierarchically classify primary cancer by methylation profile, by splitting a large-scale classification of 30 cancer types into ten smaller classification problems. These results indicate that cancer hierarchical classification has the potential to be an accurate and robust cancer classification method.

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Section: Discussionmentioning

confidence: 99%

Hierarchical classification-based pan-cancer methylation analysis to classify primary cancer

Yang,

Zeng,

Liu

et al. 2023

BMC Bioinformatics

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“…In building a classification model, it is a very important process to select features that affect the classification result [16]. In this study, popular feature selection methods were used, such as F-score, chi-square, and mutual information, which are univariate feature selection methods [16][17][18]. In addition, a feature selection technique using Gini importance was also used [19].…”

Section: Feature Selection Methodsmentioning

confidence: 99%

“…The Fisher score (F-score) is one of the most popular feature selection methods [21]. F-score is a univariate selection method and selects the optimal features based on a statistical model [16]. It can be used mainly in linear models.…”

Section: Fisher Scorementioning

confidence: 99%

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A Study on Facial Expression Change Detection Using Machine Learning Methods with Feature Selection Technique

et al. 2021

Self Cite

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Along with the fourth industrial revolution, research in the biomedical engineering field is being actively conducted. Among these research fields, the brain–computer interface (BCI) research, which studies the direct interaction between the brain and external devices, is in the spotlight. However, in the case of electroencephalograph (EEG) data measured through BCI, there are a huge number of features, which can lead to many difficulties in analysis because of complex relationships between features. For this reason, research on BCIs using EEG data is often insufficient. Therefore, in this study, we develop the methodology for selecting features for a specific type of BCI that predicts whether a person correctly detects facial expression changes or not by classifying EEG-based features. We also investigate whether specific EEG features affect expression change detection. Various feature selection methods were used to check the influence of each feature on expression change detection, and the best combination was selected using several machine learning classification techniques. As a best result of the classification accuracy, 71% of accuracy was obtained with XGBoost using 52 features. EEG topography was confirmed using the selected major features, showing that the detection of changes in facial expression largely engages brain activity in the frontal regions.

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“…Accuracy is a commonly used criterion for evaluating the efficacy of classification models when applied to balanced datasets. The computation entails the determination of the proportion of correctly classified samples relative to the overall number of samples used in the classification model [31].…”

Section: Performance Metricsmentioning

confidence: 99%

Proposed Two-Steps Procedure of Classification High Dimensional Data with Regularized Logistic Regression

Alshebly,

Abdullah

2023

Stat., optim. inf. comput.

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The field of Bioinformatics has developed in response to the rapid increase in biological data, particularly high-dimensional gene expression data. Bioinformatics utilizes optimization, computational science, and statistical methods to effectively address challenges in the field of molecular biology. Numerous genes (variables) in gene expression are irrelevant to their study. Gene selection has been demonstrated to be an effective means of enhancing the performance of numerous methods of classification. The job of acquiring significant variables via the use of ranking variable selection (RVS) techniques and then picking the most effective classifier is an enormous challenge in the context of high-dimensional data. in this study, we proposed a new ranking filter method using smooth clipped absolute deviation depending on the resampling technique (RSVS) to obtain a proficient subset of genes with strong classification abilities. This is achieved by merging A screening technique employed as a filtering method in conjunction with Regularized Logistic Regression, such as LASSO,ALASSO,ENET, and MCP. The study involved the utilization of both simulated and real datasets to conduct an empirical evaluation of the proposed approach. The findings indicated that the proposed method outperformed other established methods. it was tested using three publicly data sets about Cancer. The Results demonstrate that the suggested approach is highly effective and viable, thus showing a strong level of performance with regards to accuracy, geometric mean, and the area under the curve. Furthermore, The findings suggest that the genes most often chosen are physiologically associated with the specific form of cancer. Therefore, the method that has been suggested has potential advantages for the classification of cancer via the use of DNA gene expression data within a clinical setting.

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Analyzing high dimensional correlated data using feature ranking and classifiers

Cited by 7 publications

References 42 publications

Hierarchical classification-based pan-cancer methylation analysis to classify primary cancer

Hierarchical classification-based pan-cancer methylation analysis to classify primary cancer

A Study on Facial Expression Change Detection Using Machine Learning Methods with Feature Selection Technique

Proposed Two-Steps Procedure of Classification High Dimensional Data with Regularized Logistic Regression

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