In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classificati...In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classification methods that utilize evolutionary algorithms(EAs)for gene expression profiles in cancer or medical applications based on research motivations,challenges,and recommendations.Relevant studies were retrieved from four major academic databases-IEEE,Scopus,Springer,and ScienceDirect-using the keywords‘cancer classification’,‘optimization’,‘FS’,and‘gene expression profile’.A total of 67 papers were finally selected with key advancements identified as follows:(1)The majority of papers(44.8%)focused on developing algorithms and models for FS and classification.(2)The second category encompassed studies on biomarker identification by EAs,including 20 papers(30%).(3)The third category comprised works that applied FS to cancer data for decision support system purposes,addressing high-dimensional data and the formulation of chromosome length.These studies accounted for 12%of the total number of studies.(4)The remaining three papers(4.5%)were reviews and surveys focusing on models and developments in prediction and classification optimization for cancer classification under current technical conditions.This review highlights the importance of optimizing FS in EAs to manage high-dimensional data effectively.Despite recent advancements,significant limitations remain:the dynamic formulation of chromosome length remains an underexplored area.Thus,further research is needed on dynamic-length chromosome techniques for more sophisticated biomarker gene selection techniques.The findings suggest that further advancements in dynamic chromosome length formulations and adaptive algorithms could enhance cancer classification accuracy and efficiency.展开更多
As high-speed railway transportation advances toward increased velocities,it is imperative to enhance the mechanical performance of EA4T axle steel,especially through microstructures regulation by thermal–mechanical ...As high-speed railway transportation advances toward increased velocities,it is imperative to enhance the mechanical performance of EA4T axle steel,especially through microstructures regulation by thermal–mechanical processing.However,little research has been conducted on the phase transformation and microstructure evolution mechanism of EA4T steel under thermal–mechanical load,resulting in a lack of theoretical guidance.The hot deformation behavior and phase transformation mechanism of EA4T steel were investigated under different conditions of strain rates(0.01–10 s^(−1))and temperatures(850–1200℃).A relation of deformation stresses with Zener–Hollomon parameter was established to characterize the mechanical response and dynamic softening effect of EA4T steel during hot compression.The evolution of grain boundaries with different misorientations has been analyzed to evaluate the influence of strain rates and temperatures on the dynamic recrystallization.It was found that the grain refinement mechanisms of EA4T steel by dynamic recrystallization including twin-assisted boundary bulging,sub-grain rotation,and sub-grain growth.Transmission electron microscopy observations confirmed that dynamic recrystallization nuclei and small recrystallized grains impeded martensite phase nucleation during hot deformation,while the ongoing dynamic recrystallization consumed deformation stored energy and reduced dislocation density,which mitigated the stress concentration in the parent phase of martensite,thereby facilitating the uniform growth of martensite lath with a mixing structure of nanotwins and dislocations during quenching.展开更多
基金funded by the Ministry of Higher Education of Malaysia,grant number FRGS/1/2022/ICT02/UPSI/02/1.
文摘In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classification methods that utilize evolutionary algorithms(EAs)for gene expression profiles in cancer or medical applications based on research motivations,challenges,and recommendations.Relevant studies were retrieved from four major academic databases-IEEE,Scopus,Springer,and ScienceDirect-using the keywords‘cancer classification’,‘optimization’,‘FS’,and‘gene expression profile’.A total of 67 papers were finally selected with key advancements identified as follows:(1)The majority of papers(44.8%)focused on developing algorithms and models for FS and classification.(2)The second category encompassed studies on biomarker identification by EAs,including 20 papers(30%).(3)The third category comprised works that applied FS to cancer data for decision support system purposes,addressing high-dimensional data and the formulation of chromosome length.These studies accounted for 12%of the total number of studies.(4)The remaining three papers(4.5%)were reviews and surveys focusing on models and developments in prediction and classification optimization for cancer classification under current technical conditions.This review highlights the importance of optimizing FS in EAs to manage high-dimensional data effectively.Despite recent advancements,significant limitations remain:the dynamic formulation of chromosome length remains an underexplored area.Thus,further research is needed on dynamic-length chromosome techniques for more sophisticated biomarker gene selection techniques.The findings suggest that further advancements in dynamic chromosome length formulations and adaptive algorithms could enhance cancer classification accuracy and efficiency.
基金support was received from National Key Research and Development Plan from China:Demonstration and application of special steel for typical components of high-end equipment(2017YFB0703004).
文摘As high-speed railway transportation advances toward increased velocities,it is imperative to enhance the mechanical performance of EA4T axle steel,especially through microstructures regulation by thermal–mechanical processing.However,little research has been conducted on the phase transformation and microstructure evolution mechanism of EA4T steel under thermal–mechanical load,resulting in a lack of theoretical guidance.The hot deformation behavior and phase transformation mechanism of EA4T steel were investigated under different conditions of strain rates(0.01–10 s^(−1))and temperatures(850–1200℃).A relation of deformation stresses with Zener–Hollomon parameter was established to characterize the mechanical response and dynamic softening effect of EA4T steel during hot compression.The evolution of grain boundaries with different misorientations has been analyzed to evaluate the influence of strain rates and temperatures on the dynamic recrystallization.It was found that the grain refinement mechanisms of EA4T steel by dynamic recrystallization including twin-assisted boundary bulging,sub-grain rotation,and sub-grain growth.Transmission electron microscopy observations confirmed that dynamic recrystallization nuclei and small recrystallized grains impeded martensite phase nucleation during hot deformation,while the ongoing dynamic recrystallization consumed deformation stored energy and reduced dislocation density,which mitigated the stress concentration in the parent phase of martensite,thereby facilitating the uniform growth of martensite lath with a mixing structure of nanotwins and dislocations during quenching.
