Heterogeneous data fusion for brain tumor classification

Current research in biomedical informatics involves analysis of multipleheterogeneous data sets. This includes patient demographics, clinical and pathologydata, treatment history, patient outcomes as well as gene expression, DNA sequencesand other information sources such as gene ontology. Analysis of these data setscould lead to better disease diagnosis, prognosis, treatment and drug discovery.In this report, we present a novel machine learning framework for brain tumorclassification based on heterogeneous data fusion of metabolic and molecular datasets,including state-of-the-art high-resolution magic angle spinning (HRMAS) proton(1H) magnetic resonance spectroscopy and gene transcriptome profiling, obtainedfrom intact brain tumor biopsies. Our experimental results show that our novelframework outperforms any analysis using individual dataset.