Feature selection methods for big data bioinformatics: A survey from the search perspective.
By Lipo Wang, Yaoli Wang, Qing Chang. Methods, 2016
299 citations recorded by the source corpus.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/8a8ec7b22ded7e455b1d156d0a57f3eba9cf38b0
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Molecular Biology
- Ayça Deniz
- Sheng Yang
- Jiawei Luo
- Tansel Dökeroğlu
- Hristos Tyralis
- Andreas Langousis
- Jie Cai
- Shulin Wang
Citations in the corpus
- A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources (2019, background): “Other issues that should be properly taken into account when implementing machine learning algorithms in general, and random forests in particular, include: the need for comparison in large datasets [51] using formal procedures [52], reproducibility of applications [53], and variable selection [54].”
- Comparison of Variable Selection Methods for Clinical Predictive Modeling (2018, methodology): “…regression-based methods (stepwise backward selection using p-value and AIC, Least Absolute Shrinkage and Selection Operator, and Elastic Net) and four tree-based methods (Variable Selection Using Random Forest, Regularized Random Forests, Boruta, and Gradient Boosted Feature Selection) [8, 19–23].”
- Machine Learning and Integrative Analysis of Biomedical Big Data (2019, background): “This is the so-called curse of dimensionality or the p >> n problem in machine learning [25,34].”
- A comprehensive survey on recent metaheuristics for feature selection (2022, background): “surveyed the principles of feature selection and recent applications in big bioinformatics data [74].”
- 3D Deep Learning on Medical Images: A Review (2020, background): “However, in the case of medical images, there is much less variation in comparison to traditional image datasets [120].”
- Feature selection in machine learning: A new perspective (2018, background): “In gene expression analysis, the dimension might be high (e.g., up to 20,000) while the sample size is small (e.g., approximately 50) [146, 147].”