QIAO ZHOU^2,3,4, JIAN LIU^1,2,*, LING XIN¹, YANYAN FANG^1,2, LEI WAN^1,2, DAN HUANG^1,2, JIANTING WEN^1,2

BIOCELL, Vol.47, No.7, pp. 1623-1643, 2023, DOI:10.32604/biocell.2023.028331

Abstract Objective: On the basis of data mining, systematic pharmacology, molecular docking, and experiment validation, the oxidative-inflammatory molecular targets of Coicis Semen in the therapy of osteoarthritis (OA) were explored. Methods: The association rule analysis was effectively applied to highlight the correlation between Coicis Semen and oxidative inflammation indices. The random walk model was subsequently used to evaluate the clinical efficacy of Coicis Semen. Network pharmacology was used to predict network targets. The binding affinity of the active ingredient in Coicis Semen to the key target of OA was also successfully predicted. Results: Coicis Semen showed a significant reduction in oxidative-inflammatory… More >

Qunhui Zhang¹, Mengzhe Lu^3,4, Liang Dai^2,*

Computer Systems Science and Engineering, Vol.41, No.2, pp. 795-809, 2022, DOI:10.32604/csse.2022.019987

Abstract Stock market trading is an activity in which investors need fast and accurate information to make effective decisions. But the fact is that forecasting stock prices by using various models has been suffering from low accuracy, slow convergence, and complex parameters. This study aims to employ a mixed model to improve the accuracy of stock price prediction. We present how to use a random walk based on jump-diffusion, to obtain stock predictions with a good-fitting degree by adjusting different parameters. Aimed at getting better parameters and then using the time series model to predict the data, we employed the time… More >