MSclassifier: median-supplement model-based classification tool for automated knowledge discovery.

ABSTRACT: High-throughput technologies have resulted in an exponential growth of publicly available and accessible datasets for biomedical research. Efficient computational models, algorithms and tools are required to exploit the datasets for knowledge discovery to aid medical decisions. Here, we introduce a new tool, MSclassifier, based on median-supplement approaches to machine learning to enable an automated and effective binary classification for optimal decision making. The MSclassifier package estimates medians of features (attributes) to deduce supplementary data, which is subsequently introduced into the training set for balancing and building superior models for classification. To test our approach, it is used to determine HER2 receptor expression status phenotypes in breast cancer and also

SUBMITTER: Adabor ES 

PROVIDER: S-EPMC7788522 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature