In biomedical sciences, precise classification of data from normal and abnormal individuals is crucial. In this study, we address analysis of biomedical image data exploiting LAD which is a mathematical optimization-based supervised learning methodology. We propose an interpretable pattern recognition algorithm through set covering problem for practically applying large-scale biomedical data. To demonstrate the explainability and testing performance of our approach, we present computational results from analyzing breast cancer image data extracted from [3].

In this paper, we develop clinical Alzheimer’s Disease pattern as a combination of protein expression quantity using logical analysis of data on ROSMAP brain samples [1]. As a result, 14 transcripts are selected as support markers and compose interpretable patterns. These patterns show far statistical significance than any individual transcripts. In addition, patterns also indicate novel combinations of transcripts that have a little relation on the STRING network. Our result demonstrates a possible novel approach on analyzing interconnected transcripts, expecting a full pathology of the Alzheimer’s Disease.