Ordinal Multi-modal Feature Selection for Survival Analysis of Early-Stage Renal Cancer

2018 ◽  
pp. 648-656 ◽  
Author(s):  
Wei Shao ◽  
Jun Cheng ◽  
Liang Sun ◽  
Zhi Han ◽  
Qianjin Feng ◽  
...  
Keyword(s):  
Feature Selection ◽  
Survival Analysis ◽  
Renal Cancer ◽  
Early Stage ◽  
Selection For

Chicken Algorithm Based Feature Selection For Arabic Information Searching System

2019 ◽  
pp. 389
Author(s):  
زينب عبدالأمير ◽  
علياء كريم عبدالحسن
Keyword(s):  
Feature Selection ◽  
Information Searching ◽  
Selection For

miR-224 is an early-stage biomarker of hepatocellular carcinoma with miR-224 and miR-125b as prognostic biomarkers

2020 ◽  
Vol 14 (15) ◽  
pp. 1485-1500
Author(s):  
Lichao Yang ◽  
Chunmeng Wei ◽  
Yasi Li ◽  
Xiao He ◽  
Min He
Keyword(s):  
Hepatocellular Carcinoma ◽  
Systematic Review ◽  
Survival Analysis ◽  
Sensitivity And Specificity ◽  
Poor Prognosis ◽  
Early Stage ◽  
Meta Analysis ◽  
Benign Lesion ◽  
Diagnostic Efficiency ◽  
Low Expression

Aim: The aim was to systematically investigate the miRNA biomarkers for early diagnosis of hepatocellular carcinoma (HCC). Materials & methods: A systematic review and meta-analysis of miRNA expression in HCC were performed. Results: A total of 4903 cases from 30 original studies were comprehensively analyzed. The sensitivity and specificity of miR-224 in discriminating early-stage HCC patients from benign lesion patients were 0.868 and 0.792, which were superior to α-fetoprotein. Combined miR-224 with α-fetoprotein, the sensitivity and specificity were increased to 0.882 and 0.808. Prognostic survival analysis showed low expression of miR-125b and high expression of miR-224 were associated with poor prognosis. Conclusion: miR-224 had a prominent diagnostic efficiency in early-stage HCC, with miR-224 and miR-125b being valuable in the prognostic diagnosis.

An Integrated Feature Selection Algorithm for Cancer Classification using Gene Expression Data

2019 ◽  
Vol 21 (9) ◽  
pp. 631-645 ◽  
Author(s):  
Saeed Ahmed ◽  
Muhammad Kabir ◽  
Zakir Ali ◽  
Muhammad Arif ◽  
Farman Ali ◽  
...  
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Classification Accuracy ◽  
Early Stage ◽  
Small Sample Size ◽  
Feature Selection Method ◽  
Small Sample ◽  
Expression Data ◽  
Base Function

Aim and Objective: Cancer is a dangerous disease worldwide, caused by somatic mutations in the genome. Diagnosis of this deadly disease at an early stage is exceptionally new clinical application of microarray data. In DNA microarray technology, gene expression data have a high dimension with small sample size. Therefore, the development of efficient and robust feature selection methods is indispensable that identify a small set of genes to achieve better classification performance. Materials and Methods: In this study, we developed a hybrid feature selection method that integrates correlation-based feature selection (CFS) and Multi-Objective Evolutionary Algorithm (MOEA) approaches which select the highly informative genes. The hybrid model with Redial base function neural network (RBFNN) classifier has been evaluated on 11 benchmark gene expression datasets by employing a 10-fold cross-validation test. Results: The experimental results are compared with seven conventional-based feature selection and other methods in the literature, which shows that our approach owned the obvious merits in the aspect of classification accuracy ratio and some genes selected by extensive comparing with other methods. Conclusion: Our proposed CFS-MOEA algorithm attained up to 100% classification accuracy for six out of eleven datasets with a minimal sized predictive gene subset.

scholarly journals Fault Feature Selection for Distribution Network

2021 ◽  
Vol 1881 (2) ◽  
pp. 022080
Author(s):  
Zhiqiang Wu ◽  
Lizong Zhang ◽  
Gang Yu ◽  
Ying Wang ◽  
Tao Huang ◽  
...  
Keyword(s):  
Feature Selection ◽  
Distribution Network ◽  
Selection For
Sign in / Sign up

Export Citation Format

Share Document