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A novel feature ranking method for prediction of cancer stages using proteomics data
- Source :
- PLoS ONE, Vol 12, Iss 9, p e0184203 (2017), PLoS ONE
- Publication Year :
- 2017
- Publisher :
- Public Library of Science (PLoS), 2017.
-
Abstract
- Proteomic analysis of cancers' stages has provided new opportunities for the development of novel, highly sensitive diagnostic tools which helps early detection of cancer. This paper introduces a new feature ranking approach called FRMT. FRMT is based on the Technique for Order of Preference by Similarity to Ideal Solution method (TOPSIS) which select the most discriminative proteins from proteomics data for cancer staging. In this approach, outcomes of 10 feature selection techniques were combined by TOPSIS method, to select the final discriminative proteins from seven different proteomic databases of protein expression profiles. In the proposed workflow, feature selection methods and protein expressions have been considered as criteria and alternatives in TOPSIS, respectively. The proposed method is tested on seven various classifier models in a 10-fold cross validation procedure that repeated 30 times on the seven cancer datasets. The obtained results proved the higher stability and superior classification performance of method in comparison with other methods, and it is less sensitive to the applied classifier. Moreover, the final introduced proteins are informative and have the potential for application in the real medical practice.
- Subjects :
- 0301 basic medicine
Proteomics
Decision Analysis
Proteome
Computer science
Protein Expression
Datasets as Topic
lcsh:Medicine
Bioinformatics
Linear Discriminant Analysis
Biochemistry
Machine Learning
Database and Informatics Methods
0302 clinical medicine
Mathematical and Statistical Techniques
Discriminative model
Neoplasms
lcsh:Science
Multidisciplinary
Proteomic Databases
Applied Mathematics
Simulation and Modeling
TOPSIS
030220 oncology & carcinogenesis
Physical Sciences
Engineering and Technology
Management Engineering
Statistics (Mathematics)
Algorithms
Research Article
Computer and Information Sciences
Feature selection
Research and Analysis Methods
Models, Biological
Cross-validation
03 medical and health sciences
Machine Learning Algorithms
Breast cancer
Artificial Intelligence
Support Vector Machines
medicine
Gene Expression and Vector Techniques
Biomarkers, Tumor
Humans
Statistical Methods
Molecular Biology Techniques
Molecular Biology
Molecular Biology Assays and Analysis Techniques
business.industry
Decision Trees
lcsh:R
Cancer
Biology and Life Sciences
Pattern recognition
medicine.disease
030104 developmental biology
Biological Databases
lcsh:Q
Artificial intelligence
business
Classifier (UML)
Biomarkers
Mathematics
Software
Forecasting
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 12
- Issue :
- 9
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....0a6be52357ddfef778150c00f3d6a508