Purpose-Parkinson’s disease(PD)is a well-known complex neurodegenerative disease.Typically,its identification is based on motor disorders,while the computer estimation of its main symptoms with computational machine ...Purpose-Parkinson’s disease(PD)is a well-known complex neurodegenerative disease.Typically,its identification is based on motor disorders,while the computer estimation of its main symptoms with computational machine learning(ML)has a high exposure which is supported by researches conducted.Nevertheless,ML approaches required first to refine their parameters and then to work with the best model generated.This process often requires an expert user to oversee the performance of the algorithm.Therefore,an attention is required towards new approaches for better forecasting accuracy.Design/methodology/approach-To provide an available identification model for Parkinson disease as an auxiliary function for clinicians,the authors suggest a new evolutionary classification model.The core of the prediction model is a fast learning network(FLN)optimized by a genetic algorithm(GA).To get a better subset of features and parameters,a new coding architecture is introduced to improve GA for obtaining an optimal FLN model.Findings-The proposed model is intensively evaluated through a series of experiments based on Speech and HandPD benchmark datasets.The very popular wrappers induction models such as support vector machine(SVM),K-nearest neighbors(KNN)have been tested in the same condition.The results support that the proposed model can achieve the best performances in terms of accuracy and g-mean.Originality/value-A novel efficient PD detectionmodel is proposed,which is called A-W-FLN.The A-W-FLN utilizes FLN as the base classifier;in order to take its higher generalization ability,and identification capability is alsoembedded to discover themost suitable featuremodel in the detection process.Moreover,the proposedmethod automatically optimizes the FLN’s architecture to a smaller number of hidden nodes and solid connecting weights.This helps the network to train on complex PD datasets with non-linear features and yields superior result.展开更多
Newton＇s learning algorithm of NN is presented and realized. In theory, the convergence rate of learning algorithm of NN based on Newton＇s method must be faster than BP＇s and other learning algorithms, because the ...Newton＇s learning algorithm of NN is presented and realized. In theory, the convergence rate of learning algorithm of NN based on Newton＇s method must be faster than BP＇s and other learning algorithms, because the gradient method is linearly convergent while Newton＇s method has second order convergence rate. The fast computing algorithm of Hesse matrix of the cost function of NN is proposed and it is the theory basis of the improvement of Newton＇s learning algorithm. Simulation results show that the convergence rate of Newton＇s learning algorithm is high and apparently faster than the traditional BP method＇s, and the robustness of Newton＇s learning algorithm is also better than BP method＇ s.展开更多
Obtaining accurate bathymetric maps is very valuable for marine environment monitoring,port planning,and so on.Accurately estimating water depth in turbid coastal waters using satellite remote sensing encounters chall...Obtaining accurate bathymetric maps is very valuable for marine environment monitoring,port planning,and so on.Accurately estimating water depth in turbid coastal waters using satellite remote sensing encounters challenges originating from low water transparency,but it is limited by the quantity,quality,and water quality of samples.This study introduces a fast feature cascade learning model(FFCLM)to enhance the accuracy of bathymetric inversion from multispectral satellite images,particularly when limited field samples are available.FFCLM leverages spectral bands and in situ data to derive effective inversion weights through feature concatenation and cascade fitting.Field experiments conducted at Nanshan Port and Rushikonda Beach gathered water depth,satellite,and in situ data.Comparative analysis with conventional machine learning algorithms,including support vector machine,random forest,and gradient boosting trees,indicates that FFCLM achieves lower errors and demonstrates more robust performance across study areas.This is especially more pronounced when using small training samples(n<100).Examination of key parameters and water depth profiles highlights FFCLM’s advantages in generalization and deep-water inversion.This study presents an efficient solution for small-sample bathymetric mapping in turbid coastal waters,utilizing spectral and physical information to overcome sample size limitations and enhancing satellite remote sensing capabilities for shallow water monitoring.展开更多
MicroRNAs(miRNAs)play a key role in the prevention,diagnosis,and treatment of complex diseases.However,identifying miRNA-disease associations(MDAs)through traditional methods is costly and time-consuming.Recent studie...MicroRNAs(miRNAs)play a key role in the prevention,diagnosis,and treatment of complex diseases.However,identifying miRNA-disease associations(MDAs)through traditional methods is costly and time-consuming.Recent studies have reported numerous validated MDAs,forming the basis for the prediction of new MDAs using computational methods.In this study,we propose SAETNMDA,a computational method that applies fast kernel learning(FKL)and variant triplet networks to predict MDAs.First,miRNA and disease similarities are integrated into two kernels via FKL to enrich biological data.Next,feature representations are obtained by applying stacked autoencoders(SAEs)and triplet networks,enabling the identification of associated pairs by mapping them to nearby locations in the embedding space,while unassociated ones are mapped distantly.Finally,we utilize XGBoost(Extreme Gradient Boosting)to obtain predictive scores for MDAs from these features.SAETNMDA’s performance is evaluated with 5-fold cross-validation(5-fold-CV)and compared with other methods.It achieves the highest AUC and AUPR(0.9419,0.4749 for HMDD v2.0;0.9496,0.5355 for HMDD v3.2,respectively).The performance is also validated on an independent dataset and de novo miRNAs,with SAETNMDA achieving the highest AUC and AUPR in all validations.Case studies also demonstrate the robust predictive capability of our method,with the top 50 predicted miRNAs validated for each of the three diseases.These results highlight SAETNMDA as an efficient model for MDA prediction.SAETNMDA’s source code is available at https://github.com/npxquynhdhsp/SAETNMDA.展开更多
文摘Purpose-Parkinson’s disease(PD)is a well-known complex neurodegenerative disease.Typically,its identification is based on motor disorders,while the computer estimation of its main symptoms with computational machine learning(ML)has a high exposure which is supported by researches conducted.Nevertheless,ML approaches required first to refine their parameters and then to work with the best model generated.This process often requires an expert user to oversee the performance of the algorithm.Therefore,an attention is required towards new approaches for better forecasting accuracy.Design/methodology/approach-To provide an available identification model for Parkinson disease as an auxiliary function for clinicians,the authors suggest a new evolutionary classification model.The core of the prediction model is a fast learning network(FLN)optimized by a genetic algorithm(GA).To get a better subset of features and parameters,a new coding architecture is introduced to improve GA for obtaining an optimal FLN model.Findings-The proposed model is intensively evaluated through a series of experiments based on Speech and HandPD benchmark datasets.The very popular wrappers induction models such as support vector machine(SVM),K-nearest neighbors(KNN)have been tested in the same condition.The results support that the proposed model can achieve the best performances in terms of accuracy and g-mean.Originality/value-A novel efficient PD detectionmodel is proposed,which is called A-W-FLN.The A-W-FLN utilizes FLN as the base classifier;in order to take its higher generalization ability,and identification capability is alsoembedded to discover themost suitable featuremodel in the detection process.Moreover,the proposedmethod automatically optimizes the FLN’s architecture to a smaller number of hidden nodes and solid connecting weights.This helps the network to train on complex PD datasets with non-linear features and yields superior result.
文摘Newton＇s learning algorithm of NN is presented and realized. In theory, the convergence rate of learning algorithm of NN based on Newton＇s method must be faster than BP＇s and other learning algorithms, because the gradient method is linearly convergent while Newton＇s method has second order convergence rate. The fast computing algorithm of Hesse matrix of the cost function of NN is proposed and it is the theory basis of the improvement of Newton＇s learning algorithm. Simulation results show that the convergence rate of Newton＇s learning algorithm is high and apparently faster than the traditional BP method＇s, and the robustness of Newton＇s learning algorithm is also better than BP method＇ s.
基金supported by the 2023 Hainan Province“South China Sea New Star”Science and Technology Innovation Talent Platform Project(NHXXRCXM202316)in part by Hainan Natural Science Foundation of China(nos.424QN253 and 620RC602)+5 种基金by the National Natural Science Foundation of China(no.61966013)in part by the Teaching Reform Research Project,Hainan Normal University,hsjg2023-07in part by the National Natural Science Foundation of China under grant 61991454in part by the National Key Research and Development Program of China under grant 2023Y FC3107605in part by the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University under grant SL2022ZD206in part by the Scientific Research Fund of Second Institute of Oceanography,MNR under grant SL2302.
文摘Obtaining accurate bathymetric maps is very valuable for marine environment monitoring,port planning,and so on.Accurately estimating water depth in turbid coastal waters using satellite remote sensing encounters challenges originating from low water transparency,but it is limited by the quantity,quality,and water quality of samples.This study introduces a fast feature cascade learning model(FFCLM)to enhance the accuracy of bathymetric inversion from multispectral satellite images,particularly when limited field samples are available.FFCLM leverages spectral bands and in situ data to derive effective inversion weights through feature concatenation and cascade fitting.Field experiments conducted at Nanshan Port and Rushikonda Beach gathered water depth,satellite,and in situ data.Comparative analysis with conventional machine learning algorithms,including support vector machine,random forest,and gradient boosting trees,indicates that FFCLM achieves lower errors and demonstrates more robust performance across study areas.This is especially more pronounced when using small training samples(n<100).Examination of key parameters and water depth profiles highlights FFCLM’s advantages in generalization and deep-water inversion.This study presents an efficient solution for small-sample bathymetric mapping in turbid coastal waters,utilizing spectral and physical information to overcome sample size limitations and enhancing satellite remote sensing capabilities for shallow water monitoring.
基金supported in part by the National Natural Science Foundation of China under Grant Nos.62473149 and U22A2041the Natural Science Foundation of Hunan Province of China under Grant No.2022JJ30428.
文摘MicroRNAs(miRNAs)play a key role in the prevention,diagnosis,and treatment of complex diseases.However,identifying miRNA-disease associations(MDAs)through traditional methods is costly and time-consuming.Recent studies have reported numerous validated MDAs,forming the basis for the prediction of new MDAs using computational methods.In this study,we propose SAETNMDA,a computational method that applies fast kernel learning(FKL)and variant triplet networks to predict MDAs.First,miRNA and disease similarities are integrated into two kernels via FKL to enrich biological data.Next,feature representations are obtained by applying stacked autoencoders(SAEs)and triplet networks,enabling the identification of associated pairs by mapping them to nearby locations in the embedding space,while unassociated ones are mapped distantly.Finally,we utilize XGBoost(Extreme Gradient Boosting)to obtain predictive scores for MDAs from these features.SAETNMDA’s performance is evaluated with 5-fold cross-validation(5-fold-CV)and compared with other methods.It achieves the highest AUC and AUPR(0.9419,0.4749 for HMDD v2.0;0.9496,0.5355 for HMDD v3.2,respectively).The performance is also validated on an independent dataset and de novo miRNAs,with SAETNMDA achieving the highest AUC and AUPR in all validations.Case studies also demonstrate the robust predictive capability of our method,with the top 50 predicted miRNAs validated for each of the three diseases.These results highlight SAETNMDA as an efficient model for MDA prediction.SAETNMDA’s source code is available at https://github.com/npxquynhdhsp/SAETNMDA.
