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Drug similarities play an important role in modern biology and medicine, as they help scientists gain deep insights into drugs' therapeutic mechanisms and conduct wet labs that may significantly improve the efficiency of drug research and development. Nowadays, a number of drug-related databases have been constructed, with which many methods have been developed for computing similarities between drugs for studying associations between drugs, human diseases, proteins (drug targets) and more. In this review, firstly, we briefly introduce the publicly available drug-related databases. Secondly, based on different drug features, interaction relationships and multimodal data, we summarize similarity calculation methods in details. Then, we discuss the applications of drug similarities in various biological and medical areas. Finally, we evaluate drug similarity calculation methods with common evaluation metrics to illustrate the important roles of drug similarity measures on different applications. [ABSTRACT FROM AUTHOR]

Various microbes have proved to be closely related to the pathogenesis of human diseases. While many computational methods for predicting human microbe-disease associations (MDAs) have been developed, few systematic reviews on these methods have been reported. In this study, we provide a comprehensive overview of the existing methods. Firstly, we introduce the data used in existing MDA prediction methods. Secondly, we classify those methods into different categories by their nature and describe their algorithms and strategies in detail. Next, experimental evaluations are conducted on representative methods using different similarity data and calculation methods to compare their prediction performances. Based on the principles of computational methods and experimental results, we discuss the advantages and disadvantages of those methods and propose suggestions for the improvement of prediction performances. Considering the problems of the MDA prediction at present stage, we discuss future work from three perspectives including data, methods and formulations at the end. [ABSTRACT FROM AUTHOR]

Background To investigate the detection of the increase of interarm systolic pressure difference (IASBPD) in people aged ≥35 years in our country and its related factors. Methods Stratified multistage sampling method was used to randomly select 262 urban and rural residents from 31 provinces from 2012 to 2015. Further, simple random sampling method was used to select 16 cities and 17 rural residents from eastern, central, and western regions. A total of 34,121 subjects aged ≥35 years were analyzed after removing the missing information. Data were collected by questionnaire, and blood pressure in both arms was measured by ankle–brachial index meter. All data were analyzed by SPSS 23.0 software. Continuous data were expressed as mean ± SD. Comparison between groups was performed using t -test if it was normal distribution, and rank-sum test if it was not. Categorical data were expressed as frequency sum rate or constituent ratio, χ 2 test was used for comparison of various rates and constituent ratio, and rank-sum test was used for rank data. Multivariate logistic regression was used to analyze the influencing factors of IASBPD increase. Results The detection rates of IASBPD ≥10 and ≥15 mm Hg were 16.31% and 7.55%, respectively. The detection rate of IASBPD ≥10 mm Hg in males was higher than that in females (P < 0.05). Multivariate logistic regression showed that age, family history of hypertension, low HDL-C, hypertension, and abdominal obesity were risk factors for IASBPD ≥10 and 15 mm Hg (all P < 0.05). Conclusions The detection rates of IASBPD ≥10 and 15 mm Hg are 16.31% and 7.55%, respectively, in Chinese ≥35 years old people, and age, low HDL-C emia, family history of hypertension, hypertension, and abdominal obesity are the influencing factors of large differences in interarm blood pressure. [ABSTRACT FROM AUTHOR]