scholarly journals Determining Cutoff Point of Ensemble Trees Based on Sample Size in Predicting Clinical Dose with DNA Microarray Data

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Selen Yılmaz Isıkhan ◽  
Erdem Karabulut ◽  
Celal Reha Alpar
Keyword(s):  
Gene Expression ◽  
Sample Size ◽  
Feature Selection Method ◽  
Microarray Gene Expression Data ◽  
Cutoff Point ◽  
Gradient Boosting ◽  
Support Vector ◽  
Microarray Gene Expression ◽  
Dose Prediction ◽  
Clinical Dose

Background/Aim. Evaluating the success of dose prediction based on genetic or clinical data has substantially advanced recently. The aim of this study is to predict various clinical dose values from DNA gene expression datasets using data mining techniques. Materials and Methods. Eleven real gene expression datasets containing dose values were included. First, important genes for dose prediction were selected using iterative sure independence screening. Then, the performances of regression trees (RTs), support vector regression (SVR), RT bagging, SVR bagging, and RT boosting were examined. Results. The results demonstrated that a regression-based feature selection method substantially reduced the number of irrelevant genes from raw datasets. Overall, the best prediction performance in nine of 11 datasets was achieved using SVR; the second most accurate performance was provided using a gradient-boosting machine (GBM). Conclusion. Analysis of various dose values based on microarray gene expression data identified common genes found in our study and the referenced studies. According to our findings, SVR and GBM can be good predictors of dose-gene datasets. Another result of the study was to identify the sample size of n=25 as a cutoff point for RT bagging to outperform a single RT.

scholarly journals Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251800
Author(s):  
Dominik Schaack ◽  
Markus A. Weigand ◽  
Florian Uhle
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Intensive Care ◽  
Gene Expression Data ◽  
Meta Analysis ◽  
Microarray Gene Expression Data ◽  
Support Vector ◽  
Expression Data ◽  
Microarray Gene Expression ◽  
Microarray Gene

We investigate the feasibility of molecular-level sample classification of sepsis using microarray gene expression data merged by in silico meta-analysis. Publicly available data series were extracted from NCBI Gene Expression Omnibus and EMBL-EBI ArrayExpress to create a comprehensive meta-analysis microarray expression set (meta-expression set). Measurements had to be obtained via microarray-technique from whole blood samples of adult or pediatric patients with sepsis diagnosed based on international consensus definition immediately after admission to the intensive care unit. We aggregate trauma patients, systemic inflammatory response syndrome (SIRS) patients, and healthy controls in a non-septic entity. Differential expression (DE) analysis is compared with machine-learning-based solutions like decision tree (DT), random forest (RF), support vector machine (SVM), and deep-learning neural networks (DNNs). We evaluated classifier training and discrimination performance in 100 independent iterations. To test diagnostic resilience, we gradually degraded expression data in multiple levels. Clustering of expression values based on DE genes results in partial identification of sepsis samples. In contrast, RF, SVM, and DNN provide excellent diagnostic performance measured in terms of accuracy and area under the curve (>0.96 and >0.99, respectively). We prove DNNs as the most resilient methodology, virtually unaffected by targeted removal of DE genes. By surpassing most other published solutions, the presented approach substantially augments current diagnostic capability in intensive care medicine.

MINIMUM REDUNDANCY FEATURE SELECTION FROM MICROARRAY GENE EXPRESSION DATA

2005 ◽  
Vol 03 (02) ◽  
pp. 185-205 ◽  
Author(s):  
CHRIS DING ◽  
HANCHUAN PENG
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Microarray Gene Expression Data ◽  
Support Vector ◽  
Small Subset ◽  
Expression Data ◽  
Microarray Gene Expression ◽  
Linear Discriminant ◽  
Selection Framework

How to selecting a small subset out of the thousands of genes in microarray data is important for accurate classification of phenotypes. Widely used methods typically rank genes according to their differential expressions among phenotypes and pick the top-ranked genes. We observe that feature sets so obtained have certain redundancy and study methods to minimize it. We propose a minimum redundancy — maximum relevance (MRMR) feature selection framework. Genes selected via MRMR provide a more balanced coverage of the space and capture broader characteristics of phenotypes. They lead to significantly improved class predictions in extensive experiments on 6 gene expression data sets: NCI, Lymphoma, Lung, Child Leukemia, Leukemia, and Colon. Improvements are observed consistently among 4 classification methods: Naïve Bayes, Linear discriminant analysis, Logistic regression, and Support vector machines. Supplimentary: The top 60 MRMR genes for each of the datasets are listed in . More information related to MRMR methods can be found at .

scholarly journals Robust proportional overlapping analysis for feature selection in binary classification within functional genomic experiments

2021 ◽  
Vol 7 ◽  
pp. e562
Author(s):  
Muhammad Hamraz ◽  
Naz Gul ◽  
Mushtaq Raza ◽  
Dost Muhammad Khan ◽  
Umair Khalil ◽  
...  
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Selection ◽  
Binary Classification ◽  
Feature Selection Method ◽  
Brier Score ◽  
Classification Error ◽  
Support Vector ◽  
Microarray Gene Expression ◽  
Absolute Deviation

In this paper, a novel feature selection method called Robust Proportional Overlapping Score (RPOS), for microarray gene expression datasets has been proposed, by utilizing the robust measure of dispersion, i.e., Median Absolute Deviation (MAD). This method robustly identifies the most discriminative genes by considering the overlapping scores of the gene expression values for binary class problems. Genes with a high degree of overlap between classes are discarded and the ones that discriminate between the classes are selected. The results of the proposed method are compared with five state-of-the-art gene selection methods based on classification error, Brier score, and sensitivity, by considering eleven gene expression datasets. Classification of observations for different sets of selected genes by the proposed method is carried out by three different classifiers, i.e., random forest, k-nearest neighbors (k-NN), and support vector machine (SVM). Box-plots and stability scores of the results are also shown in this paper. The results reveal that in most of the cases the proposed method outperforms the other methods.

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