A Study of Feature Selection and Extraction Algorithms for Cancer Subtype Prediction
This work addresses computational challenges in cancer subtype prediction for biomedical researchers, but it is incremental as it builds on existing feature selection methods.
The study tackled the problem of classifying cancer subtypes using high-dimensional omics data by analyzing feature selection algorithms, finding that sequential application of existing methods reduces computational cost and improves predictive performance, with dimension reduction further enhancing model performance in some cases.
In this work, we study and analyze different feature selection algorithms that can be used to classify cancer subtypes in case of highly varying high-dimensional data. We apply three different feature selection methods on five different types of cancers having two separate omics each. We show that the existing feature selection methods are computationally expensive when applied individually. Instead, we apply these algorithms sequentially which helps in lowering the computational cost and improving the predictive performance. We further show that reducing the number of features using some dimension reduction techniques can improve the performance of machine learning models in some cases. We support our findings through comprehensive data analysis and visualization.