Increasing evidences have indicated that microRNAs (miRNAs) are functionally associated with the development and progression of various complex human diseases. However, the roles of miRNAs in multiple biological processes or various diseases and their underlying molecular mechanisms still have not been fully understood yet. Predicting potential miRNA-disease associations by integrating various heterogeneous biological datasets is of great significance to the biomedical research. Computational methods could obtain potential miRNA-disease associations in a short time, which significantly reduce the experimental time and cost. Considering the limitations in previous computational methods, we developed the model of Within and Between Score for MiRNA-Disease Association prediction (WBSMDA) to predict potential miRNAs associated with various complex diseases. WBSMDA could be applied to the diseases without any known related miRNAs. The AUC of 0.8031 based on Leave-one-out cross validation has demonstrated its reliable performance. WBSMDA was further applied to Colon Neoplasms, Prostate Neoplasms, and Lymphoma for the identification of their potential related miRNAs. As a result, 90%, 84%, and 80% of predicted miRNA-disease pairs in the top 50 prediction list for these three diseases have been confirmed by recent experimental literatures, respectively. It is anticipated that WBSMDA would be a useful resource for potential miRNA-disease association identification.

With the development of technologies, such as big data, cloud computing, and the Internet of Things (IoT), digital twin is being applied in industry as a precision simulation technology from concept to practice. Further, simulation plays a very important role in the healthcare field, especially in research on medical pathway planning, medical resource allocation, medical activity prediction, etc. By combining digital twin and healthcare, there will be a new and efficient way to provide more accurate and fast services for elderly healthcare. However, how to achieve personal health management throughout the entire lifecycle of elderly patients, and how to converge the medical physical world and the virtual world to realize real smart healthcare, are still two key challenges in the era of precision medicine. In this paper, a framework of the cloud healthcare system is proposed based on digital twin healthcare (CloudDTH). This is a novel, generalized, and extensible framework in the cloud environment for monitoring, diagnosing and predicting aspects of the health of individuals using, for example, wearable medical devices, toward the goal of personal health management, especially for the elderly. CloudDTH aims to achieve interaction and convergence between medical physical and virtual spaces. Accordingly, a novel concept of digital twin healthcare (DTH) is proposed and discussed, and a DTH model is implemented. Next, a reference framework of CloudDTH based on DTH is constructed, and its key enabling technologies are explored. Finally, the feasibility of some application scenarios and a case study for real-time supervision are demonstrated.INDEX TERMS Digital twin, elderly healthcare, personal health management, cloud computing, precision medicine, interaction, convergence. I. INTRODUCTIONAccording to the latest statistics from the United Nations Department of Economic and Social Affairs, the elderly population is forecasted to be 2.1 billion in 2050, with the aging population in the developing regions growing faster than in the developed regions [1]. In the aging society of the future, it is projected that nearly 50% of medical resources will be The associate editor coordinating the review of this manuscript and approving it for publication was Tai-Hoon Kim.

Psoriasis is a chronic inflammatory immune-mediated disorder affecting the skin and other organs including joints. Over 1,300 transcripts are altered in psoriatic involved skin compared to normal skin. However to our knowledge global epigenetic profiling of psoriatic skin is previously unreported. Here we describe a genome-wide study of altered CpG methylation in psoriatic skin. We determined the methylation levels at 27,578 CpG sites in skin samples from individuals with psoriasis (12 involved, 8 uninvolved) and 10 unaffected individuals. CpG methylation of involved skin differed from normal skin at 1,108 sites. Twelve mapped to the epidermal differentiation complex, upstream or within genes that are highly up-regulated in psoriasis. Hierarchical clustering of 50 of the top differentially methylated (DM) sites separated psoriatic from normal skin samples. CpG sites where methylation was correlated with gene expression are reported. Sites with inverse correlations between methylation and nearby gene expression include those of KYNU, OAS2, S100A12, and SERPINB3, whose strong transcriptional up-regulation are important discriminators of psoriasis. We observed intrinsic epigenetic differences in uninvolved skin. Pyrosequencing of bisulfite-treated DNA from skin biopsies at three DM loci confirmed earlier findings and revealed reversion of methylation levels towards the non-psoriatic state after one month of anti-TNF-α therapy.

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