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geneSurv: An interactive web-based tool for survival analysis in genomics research.
Selcuk Korkmaz1, Dincer Goksuluk2, Gokmen Zararsiz3
1Trakya University, Faculty of Medicine, Department of Biostatistics and Medical Informatics, 22030, Merkez, Edirne, Turkey; Turcosa Analytics Solutions Ltd Co, Erciyes Teknopark, 38039, Kayseri, Turkey.
Computers in Biology and Medicine
|September 11, 2017
Summary
This study introduces geneSurv, a free, web-based tool for cancer survival analysis using genomics data. It addresses high-dimensional data challenges with feature selection and ensemble methods, enhancing predictive accuracy.
Area of Science:
- Bioinformatics
- Computational Biology
- Cancer Research
Background:
- Survival analysis is crucial in cancer research.
- Integrating clinical and genomics data improves predictive performance but creates high-dimensional data challenges.
- Existing tools lack user-friendliness and interactivity for high-dimensional genomics data in survival analysis.
Purpose of the Study:
- To develop an open-source, web-based tool for user-friendly survival analysis with high-dimensional genomics data.
- To provide an accessible platform integrating classical and advanced survival analysis methods.
- To enable optimal gene expression cutoff determination for dichotomization.
Main Methods:
- Developed geneSurv, an interactive, web-based tool.
- Incorporated feature selection and ensemble methods to handle high-dimensional data.
- Included Kaplan-Meier, Cox regression, penalized Cox regression, and Random Survival Forests.
- Implemented an optimal cutoff determination method.
Main Results:
- The tool, geneSurv, successfully handles high-dimensional data from various sources (microarray, RNA-Seq, proteomics, etc.).
- It offers a user-friendly interface for performing complex survival analyses.
- Provides optimal cutoff points for gene expression dichotomization.
Conclusions:
- geneSurv is a valuable, freely available resource for cancer researchers.
- It democratizes advanced survival analysis for high-dimensional genomics data.
- Facilitates improved predictive modeling in cancer studies.
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