PREDICTION OF THE THERAPEUTIC EFFECTIVENESS OF NEW DRUGS FROM CLINICAL PHARMACOLOGY STUDIES

The development of new drugs for therapeutic purposes has become very expensive and time-consuming in American and European countries.It is estimated that on the average 50 to 100 million dollars and 10 or more years from the time of patenting are required to make a new drug available for general prescription. Every new drug needs to be charac-

A numerical investigation of hydraulic fracturing on coal seam permeability based on PFC‑COMSOL coupling method

Hydraulic fracturing and permeability enhancement are effective methods to improve low-permeability coal seams.However,few studies focused on methods to increase permeability,and there are no suitable prediction methods for engineering applications.In this work,PFC2D software was used to simulate coal seam hydraulic fracturing.The results were used in a coupled mathematical model of the interaction between coal seam deformation and gas flow.The results show that the displacement and velocity of particles increase in the direction of minimum principal stress,and the cracks propagate in the direction of maximum principal stress.The gas pressure drop rate and permeability increase rate of the fracture model are higher than that of the non-fracture model.Both parameters decrease rapidly with an increase in the drainage time and approach 0.The longer the hydraulic fracturing time,the more complex the fracture network is,and the faster the gas pressure drops.However,the impact of fracturing on the gas drainage effect declines over time.As the fracturing time increases,the difference between the horizontal and vertical permeability increases.However,this difference decreases as the gas drainage time increases.The higher the initial void pressure,the faster the gas pressure drops,and the greater the permeability increase is.However,the influence of the initial void pressure on the permeability declines over time.The research results provide guidance for predicting the anti-reflection effect of hydraulic fracturing in underground coal mines.

An Improved Machine Learning Technique with Effective Heart Disease Prediction System

Heart disease is the leading cause of death worldwide.Predicting heart disease is challenging because it requires substantial experience and knowledge.Several research studies have found that the diagnostic accuracy of heart disease is low.The coronary heart disorder determines the state that influences the heart valves,causing heart disease.Two indications of coronary heart disorder are strep throat with a red persistent skin rash,and a sore throat covered by tonsils or strep throat.This work focuses on a hybrid machine learning algorithm that helps predict heart attacks and arterial stiffness.At first,we achieved the component perception measured by using a hybrid cuckoo search particle swarm optimization(CSPSO)algorithm.With this perception measure,characterization and accuracy were improved,while the execution time of the proposed model was decreased.The CSPSO-deep recurrent neural network algorithm resolved issues that state-of-the-art methods face.Our proposed method offers an illustrative framework that helps predict heart attacks with high accuracy.The proposed technique demonstrates the model accuracy,which reached 0.97 with the applied dataset.

Comparison of REML and MINQUE for Estimated Variance Components and Predicted Random Effects

Linear mixed model (LMM) approaches have been widely applied in many areas of research data analysis because they offer great flexibility for different data structures and linear model systems. In this study, emphasis is placed on comparing the properties of two LMM approaches: restricted maximum likelihood (REML) and minimum norm quadratic unbiased estimation (MINQUE) with and without resampling techniques being included. Bias, testing power, Type I error, and computing time were compared between REML and MINQUE approaches with and without Jackknife technique based on 500 simulated data sets. Results showed that MINQUE and REML methods performed equally regarding bias, Type I error, and power. Jackknife-based MINQUE and REML greatly improved power compared to non-Jackknife based linear mixed model approaches. Results also showed that MINQUE is more time-saving compared to REML, especially with the use of resampling techniques and large data set analysis. Results from the actual cotton data analysis were in agreement with our simulated results. Therefore, Jackknife-based MINQUE approaches could be recommended to achieve desirable power with reduced time for a large data analysis and model simulations.

Clustering Gene Expression Data Based on Predicted Differential Effects of GV Interaction

Microarray has become a popular biotechnology in biological and medical research. However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw measurements have inherent “noise” within microarray experiments. Currently, logarithmic ratios are usually analyzed by various clustering methods directly, which may introduce bias interpretation in identifying groups of genes or samples. In this paper, a statistical method based on mixed model approaches was proposed for microarray data cluster analysis. The underlying rationale of this method is to partition the observed total gene expression level into various variations caused by different factors using an ANOVA model, and to predict the differential effects of GV （gene by variety） interaction using the adjusted unbiased prediction （AUP） method. The predicted GV interaction effects can then be used as the inputs of cluster analysis. We illustrated the application of our method with a gene expression dataset and elucidated the utility of our approach using an external validation.