Essential proteins are crucial for biological processes and can be identified through both experimental and computational methods.While experimental approaches are highly accurate,they often demand extensive time and ...Essential proteins are crucial for biological processes and can be identified through both experimental and computational methods.While experimental approaches are highly accurate,they often demand extensive time and resources.To address these challenges,we present a computational ensemble learning framework designed to identify essential proteins more efficiently.Our method begins by using node2vec to transform proteins in the protein–protein interaction(PPI)network into continuous,low-dimensional vectors.We also extract a range of features from protein sequences,including graph-theory-based,information-based,compositional,and physiochemical attributes.Additionally,we leverage deep learning techniques to analyze high-dimensional position-specific scoring matrices(PSSMs)and capture evolutionary information.We then combine these features for classification using various machine learning algorithms.To enhance performance,we integrate the outputs of these algorithms through ensemble methods such as voting,weighted averaging,and stacking.This approach effectively addresses data imbalances and improves both robustness and accuracy.Our ensemble learning framework achieves an AUC of 0.960 and an accuracy of 0.9252,outperforming other computational methods.These results demonstrate the effectiveness of our approach in accurately identifying essential proteins and highlight its superior feature extraction capabilities.展开更多
Objective Post-operative cognitive dysfunction(POCD)and post-operative delirium(POD)are two common post-operative cerebral complications.The current meta-analysis was to systematically review the effects of penehyclid...Objective Post-operative cognitive dysfunction(POCD)and post-operative delirium(POD)are two common post-operative cerebral complications.The current meta-analysis was to systematically review the effects of penehyclidine hydrochloride(PHC)on POCD and POD in surgical patients.Methods Electronic databases were searched to identify all randomized controlled trials comparing PHC with atropine/scopolamine/placebo on POCD and POD in surgical patients.Primary outcomes of interest included the incidences of POCD and POD;the secondary outcomes of interest included peri-operative minimental state examination(MMSE)scores.Two authors independently extracted peri-operative data,including patients'baseline characteristics,surgical variables,and outcome data.For dichotomous data(POCD and POD occurrence),treatment effects were calculated as odds ratio(OR)and 95%confidential interval(Cl).Each outcome was tested for heterogeneity,and randomized-effects or fixed-effects model was used in the presence or absence of significant heterogeneity.For continuous variables(MMSE scores),treatment effects were calculated as weighted mean difference(WMD)and 95%CI.Statistical significance was defined as P<0.05.Results Our search yielded 33 studies including 4017 patients.Meta-analysis showed that,the incidence of POCD in PHC group was comparable to that in saline group(OR=0.97;95%Ck 0.S8-1.64;P=0.92),scopolamine group(OR=0.78;95%CI:0.48-1.27;P=0.32)and atropine group(0R=1.20;95%Ch 0.86-1.67;P=0.29).The incidence of POD in PHC group was comparable to that in saline group(OR=1.53;95%CI:0.81-2.90;P=0.19)and scopolamine group(OR=0.53;95%CI:0.06-4.56;P=0.56),but higher than that in atropine group(OR=4.49;95%CI:1.34-15.01;P=0.01).Conclusions PHC premedication was not associated with increased incidences of POCD or POD as compared to either scopolamine or placebo.展开更多
基金financially supported by the National Key R&D Program of China(Grant No.2022YFF1202600)the National Natural Science Foundation of China(Grant No.82301158)+4 种基金Science and Technology Innovation Action Plan of Shanghai Science and Technology Committee(Grant No.22015820100)Two-hundred Talent Support(Grant No.20152224)Translational Medicine Innovation Project of Shanghai Jiao Tong University School of Medicine(Grant No.TM201915)Clinical Research Project of Multi-Disciplinary Team,Shanghai Ninth People’s Hospital,Shanghai Jiao Tong University School of Medicine(Grant No.201914)China Postdoctoral Science Foundation(Grant No.2023M742332)。
文摘Essential proteins are crucial for biological processes and can be identified through both experimental and computational methods.While experimental approaches are highly accurate,they often demand extensive time and resources.To address these challenges,we present a computational ensemble learning framework designed to identify essential proteins more efficiently.Our method begins by using node2vec to transform proteins in the protein–protein interaction(PPI)network into continuous,low-dimensional vectors.We also extract a range of features from protein sequences,including graph-theory-based,information-based,compositional,and physiochemical attributes.Additionally,we leverage deep learning techniques to analyze high-dimensional position-specific scoring matrices(PSSMs)and capture evolutionary information.We then combine these features for classification using various machine learning algorithms.To enhance performance,we integrate the outputs of these algorithms through ensemble methods such as voting,weighted averaging,and stacking.This approach effectively addresses data imbalances and improves both robustness and accuracy.Our ensemble learning framework achieves an AUC of 0.960 and an accuracy of 0.9252,outperforming other computational methods.These results demonstrate the effectiveness of our approach in accurately identifying essential proteins and highlight its superior feature extraction capabilities.
文摘Objective Post-operative cognitive dysfunction(POCD)and post-operative delirium(POD)are two common post-operative cerebral complications.The current meta-analysis was to systematically review the effects of penehyclidine hydrochloride(PHC)on POCD and POD in surgical patients.Methods Electronic databases were searched to identify all randomized controlled trials comparing PHC with atropine/scopolamine/placebo on POCD and POD in surgical patients.Primary outcomes of interest included the incidences of POCD and POD;the secondary outcomes of interest included peri-operative minimental state examination(MMSE)scores.Two authors independently extracted peri-operative data,including patients'baseline characteristics,surgical variables,and outcome data.For dichotomous data(POCD and POD occurrence),treatment effects were calculated as odds ratio(OR)and 95%confidential interval(Cl).Each outcome was tested for heterogeneity,and randomized-effects or fixed-effects model was used in the presence or absence of significant heterogeneity.For continuous variables(MMSE scores),treatment effects were calculated as weighted mean difference(WMD)and 95%CI.Statistical significance was defined as P<0.05.Results Our search yielded 33 studies including 4017 patients.Meta-analysis showed that,the incidence of POCD in PHC group was comparable to that in saline group(OR=0.97;95%Ck 0.S8-1.64;P=0.92),scopolamine group(OR=0.78;95%CI:0.48-1.27;P=0.32)and atropine group(0R=1.20;95%Ch 0.86-1.67;P=0.29).The incidence of POD in PHC group was comparable to that in saline group(OR=1.53;95%CI:0.81-2.90;P=0.19)and scopolamine group(OR=0.53;95%CI:0.06-4.56;P=0.56),but higher than that in atropine group(OR=4.49;95%CI:1.34-15.01;P=0.01).Conclusions PHC premedication was not associated with increased incidences of POCD or POD as compared to either scopolamine or placebo.
