A developed ant colony algorithm for cancer molecular subtype classification to reveal the predictive biomarker in the renal cell carcinoma

Background:Recently,researchers have been attracted in identifying the crucial genes related to cancer,which plays important role in cancer diagnosis and treatment.However,in performing the cancer molecular subtype classification task from cancer gene expression data,it is challenging to obtain those significant genes due to the high dimensionality and high noise of data.Moreover,the existing methods always suffer from some issues such as premature convergence.Methods:To address those problems,we propose a new ant colony optimization(ACO)algorithm called DACO to classify the cancer gene expression datasets,identifying the essential genes of different diseases.In DACO,first,we propose the initial pheromone concentration based on the weight ranking vector to accelerate the convergence speed;then,a dynamic pheromone volatility factor is designed to prevent the algorithm from getting stuck in the local optimal solution;finally,the pheromone update rule in the Ant Colony System is employed to update the pheromone globally and locally.To demonstrate the performance of the proposed algorithm in classification,different existing approaches are compared with the proposed algorithm on eight high-dimensional cancer gene expression datasets.Results:The experiment results show that the proposed algorithm performs better than other effective methods in terms of classification accuracy and the number of feature sets.It can be used to address the classification problem effectively.Moreover,a renal cell carcinoma dataset is employed to reveal the biological significance of the proposed algorithm from a number of biological analyses.Conclusion:The results demonstrate that CAPS may play a crucial role in the occurrence and development of renal clear cell carcinoma.

Dynamic analysis of heat extraction rate by supercritical carbon dioxide in fractured rock mass based on a thermal-hydraulic-mechanics coupled model

Heat production from geothermal reservoirs is a typical heat transfer process involving a cold working fluid contacting a hot rock formation.Compared to the thermal-physical characteristics of water,supercritical CO_(2)(scCO_(2))has a higher heat storage capacity over a wide temperature-pressure range and may be favored as a heat transfer fluid.Singularly characteristic of scCO_(2)-based heat extraction is that the hydraulic-thermal properties of the scCO_(2) vary dramatically and dynamically with the spatial pressure gradient during unsteady-state flow along fracture.This highly nonlinear behavior presents a challenge in the accurate estimation of heat extraction efficiency in scCO_(2)-based EGS.In this paper,a thermal-h ydraulic-mechanical(THM)coupled model is developed by considering deformation of the fractured reservoir,non-Darcy flow and the varying thermal-physical properties of scCO_(2).The proposed model is validated by matching the modeling temperature distribution with published data.The results show that during continuous injection of scCO_(2),the fracture first widens and then narrows,ultimately reopening over the long term.The sequential fracture deformation behaviors are in response to the combined impacts of mechanical compression and thermally-induced deformation.By controlling the injection parameters of the scCO_(2),it is found that the heat extraction rate is positively correlated to its pore pressure or mass flow rate.The heat extraction rate can be significantly enhanced,when the inlet temperature of scCO_(2) is below its critical temperature.As a result,the heat increment recovered per unit mass of scCO_(2) decreases as the hot rock is gradually cooled.Meanwhile,the heat increment recovered per unit mass of scCO_(2) decreases by increasing the inlet temperature of scCO_(2) or its mass flow rate,but increases as the outlet pressure rises.Furthermore,multi-linear regression indicates that controlling the inlet temperature of the scCO_(2) can significantly improve the thermodynamic efficiency of heat extraction.