To identify similar diseases has significant implications for revealing the etiology and pathogenesis of diseases and further research in the domain of biomedicine. Currently most methods for the measurement of disease similarity utilize either associations of ontological disease concepts or functional interactions between disease-related genes. These methods are heavily dependent on the ontology, which are not always available, and the selection of datasets. Moreover, many methods suffer from a drawback that they only use a single metric to evaluate disease similarity from an individual data source, which may result in biased conclusions without consideration of other aspects. In this study, we proposed a novel ontology-independent framework, namely RADAR, for learning representations for diseases to deduce their similarities from an integrative perspective. By leveraging the associations between diseases and disease-related biomedical entities, a disease similarity network was built under various metrics. Then a multi-layer disease similarity network was constructed by integrating multiple disease similarity networks derived from multiple data sources, where the representation learning was derived to provide a comprehensive evaluation of disease similarities. The performance of RADAR was assessed by a benchmark disease set and 100 random disease sets. Experimental results demonstrated that RADAR can detect similar diseases effectively.
Multi‐walled carbon nanotubes (MWCNT) were first non‐covalently functioned by tannic acid (TA), then blended with nitrile rubber (NBR) and ethylene propylene diene monomer (EPDM) with NBR:EPDM:TA‐MWCNT = 70:30:5. TA‐MWCNT and the composites had been characterized by various techniques, including Fourier transform infrared spectroscopy (FT‐IR), Raman analysis, transmission electron microscopy (TEM), mechanical properties testing, thermal‐oxidative aging testing, differential scanning calorimetry (DSC), dynamic mechanical analysis (DMA), and scanning electron microscopy (SEM). Results indicated that TA molecules were successfully absorbed to the surface of MWCNT. And compared with NBR/EPDM/MWCNT composites, the compatibility between NBR and EPDM had been significantly improved. The NBR/EPDM/TA‐MWCNT exhibited excellent mechanical properties and thermal‐oxidative aging properties due to the phenolic hydroxyl groups on the surface of TA‐MWCNT, which had high chemical reactivity and physical adhesion. These phenolic hydroxyl groups gave TA‐MWCNT better dispersion in the rubber matrixes and can participate in the vulcanization process, creating the firm interface between the rubber matrixes and the surface of MWCNT.
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