highMLR: An open-source package for R with machine learning for feature selection in high dimensional cancer clinical genome time to event data

• Published in: Expert systems with applications 2022-12, Vol.210, p.118432, Article 118432
• Main Authors: Bhattacharjee, Atanu, Vishwakarma, Gajendra K., Banerjee, Souvik, Pashchenko, Alexander F.
• Format: Article
• Language: English
• Subjects: Feature selection; Gene expression; High dimension; Machine learning; Survival data
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2022.118432

Machine learning techniques, popularly used as a tool for dimensionality reduction and pattern recognition of features, have been utilized extensively in data mining. In survival analysis, where the primary outcome is the time until a specific event occurs, identifying relevant features for building an efficient prediction model is essential. This is where machine learning can be a suitable option. However, there is an existing gap in utilizing machine learning techniques in high-dimensional survival data due to the non-availability of convenient programming functions and packages. In this article, we have developed an efficient machine learning procedure for analyzing survival data associated with high-dimensional gene expressions. Though there are several R libraries available for performing machine learning, no package support is available to implement machine learning with classification on high-dimensional survival data. highMLR, our developed R package, is capable of implementing machine learning methods on high dimensional survival data and provides a way of feature selection based on the logarithmic loss function. Several statistical methods for survival analysis have been incorporated into this machine learning algorithm. A high-dimensional gene expression dataset has been analyzed using the proposed R library to show its efficacy in feature selection.