ABSTRACT
Link prediction is a concept of network theory that intends to find a link between two separate network entities. In the present world of social media, this concept has taken root, and its application is seen through numerous social networks. A typical example is 2004, 4 February "TheFeacebook," currently known as just Facebook. It uses this concept to recommend friends by checking their links using various algorithms. The same goes for shopping and e-commerce sites. Notwithstanding all the merits link prediction presents, they are only enjoyed by large networks. For sparse networks, there is a wide disparity between the links that are likely to form and the ones that include. A barrage of literature has been written to approach this problem; however, they mostly come from the angle of unsupervised learning (UL). While it may seem appropriate based on a dataset's nature, it does not provide accurate information for sparse networks. Supervised learning could seem reasonable in such cases. This research is aimed at finding the most appropriate link-based link prediction methods in the context of big data based on supervised learning. There is a tone of books written on the same; nonetheless, they are core issues that are not always addressed in these studies, which are critical in understanding the concept of link prediction. This research explicitly looks at the new problems and uses the supervised approach in analyzing them to devise a full-fledge holistic link-based link prediction method. Specifically, the network issues that we will be delving into the lack of specificity in the existing techniques, observational periods, variance reduction, sampling approaches, and topological causes of imbalances. In the subsequent sections of the paper, we explain the theory prediction algorithms, precisely the flow-based process. We specifically address the problems on sparse networks that are never discussed with other prediction methods. The resolutions made by addressing the above techniques place our framework above the previous literature's unsupervised approaches.
ABSTRACT
Metabolic syndrome (MS) is a complex polygenic inheritance disease, caused by the accumulative effect of multiple genes and some environmental factors. Insulin resistance is the pathophysiologic foundation of MS. From the viewpoint of TCM, phlegm is the basic pathological factor of MS. In different periods and different symptoms of the disease, phlegm can be found during the whole disease course. Genes as an important component of the innate factors, play an important role in the formation of phlegm syndrome. Researches on correlation between susceptibility genes and phlegm syndrome in MS are important to enrich the connotation of phlegm syndrome and improve the level of diagnosis and treatment for this disease.
ABSTRACT
Objective;To explore the mechanism ofSimiaosan drug-containing serum on prevention and treatment of renal fibrosis.Methods Human renal tubular epithelial cells (HK-2) were divided into blank group, model group, control group, and drug-containing serum high dosage group, medium dosage group and low dosage group. The expressions of CTGF and BMP-7 were determined by real-time PCR assay and immunocytochemistry.Results After the HK-2 cell was induced by uric acid (UA), the expression of CTGF significantly increased (P<0.05), the expression of BMP-7 significantly decreased (P<0.05). After intervened bySimiaosan, the expression of CTGF significantly decreased (P<0.05), the expression of BMP-7 significantly increased (P<0.05) and present dosage dependent.Conclusion Reducing the expression of CTGF and up-regulation the expression of BMP-7 and controlling epithelial-myofibroblast transdifferentiation may be the mechanism ofSimiaosan on prevention and treatment of renal fibrosis.
