Data fusion strategies for multi-modal classification of auto-immune dysregulation with vibrational spectroscopy

Multi-modal spectroscopic analysis of biological systems may offer an improved overall non-invasive biophotonic metric of the status of the system, further enhancing the diagnostic and prognostic capabilities of these technologies. In the present study macrophages were extracted from wild-type mice and mice with a knock-out of the gene regulating miR-155, which has been observed to occur in patients with various autoimmune disorders, including multiple sclerosis (MS). Macrophages were stimulated in-vitro to produce an immune response and were then screened spectroscopically with FTIR and Raman spectroscopy (at 532nm and 660nm). Low, medium and high level data fusion strategies for classification of response to stimulation and miRNA regulation were piloted, using downstream principal components analysis-support vector machine classifiers to test the impact of these strategies on classification performance. These techniques allowed the development of a combined high-level data-fusion, classification pipeline with a high. level of classification accuracy (F1>0.9), with reduced variability in performance. Our proposed spectroscopic assay-data fusion strategy may provide an adjunct to clinical screening and diagnosis of various autoinuuune disorders whose aetiology is associated with genetic dysregulation.