ABSTRACT
Bioinformatics is an interdisciplinary science that combines the tools of mathematics, computer science, and biology to clarify and explore the biological implications of large amounts of biological data. With the continuous development of genome sequencing technology, a large number of biological data has been generated, and mining of the biological significance contained in big data has become one of the main tasks that need to be solved urgently. This article summarizes the risk prediction models for hepatocellular carcinoma (HCC) based on feature genes, so as to provide new perspectives for early identification, prognosis, and treatment optimization of HCC.
ABSTRACT
Objective:To investigate the relationship between the epidemic of Coronavirus Disease in 2019 (COVID-19) in China and population migration from Wuhan before the city implemented strict migration restrictions.Methods:We collected the cumulative number of confirmed cases with COVID-19 up to January 31, 2020 from the official website of the health administrative departments, and information on population migration out of Wuhan during January 10, 2020 and January 24, 2020, approximately half months prior to the implementation of strict migration restrictions by the city, from Baidu population-migration big data platform. Population migration data were provided for the top 100 cities in the form of percentage values, calculated as the number of migrants from Wuhan into these cities divided by the total number of migrants out of Wuhan during the same period multiplied by 100%. The two-independent sample non-parametric Wilcoxon rank-sum test was used to compare the distribution of cumulative number of cases between the top 100 cities and the remaining 205 non-top 100 cities of China. The relationship between the cumulative number of cases and the percentage of migrants from Wuhan into the top 100 cities were further assessed by Pearson correlation and by multiple linear regression with adjustment for population size, population density, and GDP per capita.Results:The top 100 cities accounted for 91.6% of total migrants out of Wuhan, and the top 14 cities were all in Hubei province. There were a total of 5,869 cases in the top 100 cities, with a median (interquartile range) of 21.5 (12~55) cases, whereas in the 205 non-top 100 cities there were a total of 1,063 cases, with a median (interquartile range) of 4 (2~7) cases. The median cumulative number of cases differed significantly between the two types of cities ( P<0.001). Among the top 100 cities, there was a strong correlation between the percentage of migrants from Wuhan and the cumulated number of cases (Pearson correlation coefficient=0.92), and for every 1 percentage point increase in migrants from Wuhan the cumulative number of cases increased by approximately 42 (95%CI, 39 to 45). The number of cumulative number of cases in Wenzhou and Chongqing was detected as potential outliers in regression diagnosis ( P<0.001, corresponding standardized residuals were 5.2 and 3.5, respectively), suggesting that the number of cases in these two cities was substantially higher than others with similar amount of migrants from Wuhan. Conclusions:There is a strong positive association between the percentage of migrants from Wuhan and the epidemic status of COVID-19 infections in cities in China.
ABSTRACT
Objective@#To investigate the relationship between the epidemic of Coronavirus Disease in 2019 (COVID-19) in China and population migration from Wuhan before the city implemented strict migration restrictions.@*Methods@#We collected the cumulative number of confirmed cases with COVID-19 up to January 31, 2020 from the official website of the health administrative departments, and information on population migration out of Wuhan during January 10, 2020 and January 24, 2020, approximately half months prior to the implementation of strict migration restrictions by the city, from Baidu population-migration big data platform. Population migration data were provided for the top 100 cities in the form of percentage values, calculated as the number of migrants from Wuhan into these cities divided by the total number of migrants out of Wuhan during the same period multiplied by 100%. The two-independent sample non-parametric Wilcoxon rank-sum test was used to compare the distribution of cumulative number of cases between the top 100 cities and the remaining 205 non-top 100 cities of China. The relationship between the cumulative number of cases and the percentage of migrants from Wuhan into the top 100 cities were further assessed by Pearson correlation and by multiple linear regression with adjustment for population size, population density, and GDP per capita.@*Results@#The top 100 cities accounted for 91.6% of total migrants out of Wuhan, and the top 14 cities were all in Hubei province. There were a total of 5,869 cases in the top 100 cities, with a median (interquartile range) of 21.5 (12~55) cases, whereas in the 205 non-top 100 cities there were a total of 1,063 cases, with a median (interquartile range) of 4 (2~7) cases. The median cumulative number of cases differed significantly between the two types of cities (P<0.001). Among the top 100 cities, there was a strong correlation between the percentage of migrants from Wuhan and the cumulated number of cases (Pearson correlation coefficient=0.92), and for every 1 percentage point increase in migrants from Wuhan the cumulative number of cases increased by approximately 42 (95%CI, 39 to 45). The number of cumulative number of cases in Wenzhou and Chongqing was detected as potential outliers in regression diagnosis (P<0.001, corresponding standardized residuals were 5.2 and 3.5, respectively), suggesting that the number of cases in these two cities was substantially higher than others with similar amount of migrants from Wuhan.@*Conclusions@#There is a strong positive association between the percentage of migrants from Wuhan and the epidemic status of COVID-19 infections in cities in China.