"Boundarics in Biomedicine"is a cutting-edge interdisciplinary discipline,which is of great significance for understanding the origin of life,the interaction between internal and external environments,and th..."Boundarics in Biomedicine"is a cutting-edge interdisciplinary discipline,which is of great significance for understanding the origin of life,the interaction between internal and external environments,and the mechanism of disease occurrence and evolution.Here,the definition of Boundarics in Biomedicine is first described,including its connotation,research object,research method,challenges,and future perspectives."Boundarics in Biomedicine"is a cutting-edge interdisciplinary discipline involving multiple fields(e.g.,bioscience,medicine,chemistry,materials science,and information science)dedicated to investigating and solving key scientific questions in the formation,identification,and evolution of living organism boundaries.Specifically,it encompasses 3 levels:(a)the boundary between the living organism and the external environment,(b)internal boundary within living organism,and(c)the boundary related to disease in living organism.The advancement of research in Boundarics in Biomedicine is of great scientific significance for understanding the origin of life,the interaction between internal and external environments,and the mechanism of disease occurrence and evolution,thus providing novel principles,technologies,and methods for early diagnosis and prevention of major diseases,personalized drug development,and prognosis assessment(Fig.1).展开更多
Purpose-Patient treatment trajectory data are used to predict the outcome of the treatment to particular disease that has been carried out in the research.In order to determine the evolving disease on the patient and ...Purpose-Patient treatment trajectory data are used to predict the outcome of the treatment to particular disease that has been carried out in the research.In order to determine the evolving disease on the patient and changes in the health due to treatment has not considered existing methodologies.Hence deep learning models to trajectory data mining can be employed to identify disease prediction with high accuracy and less computation cost.Design/methodology/approach-Multifocus deep neural network classifiers has been utilized to detect the novel disease class and comorbidity class to the changes in the genome pattern of the patient trajectory data can be identified on the layers of the architecture.Classifier is employed to learn extracted feature set with activation and weight function and then merged on many aspects to classify the undetermined sequence of diseases as a new variant.The performance of disease progression learning progress utilizes the precision of the constituent classifiers,which usually has larger generalization benefits than those optimized classifiers.Findings-Deep learning architecture uses weight function,bias function on input layers and max pooling.Outcome of the input layer has applied to hidden layer to generate the multifocus characteristics of the disease,and multifocus characterized disease is processed in activation function using ReLu function along hyper parameter tuning which produces the effective outcome in the output layer of a fully connected network.Experimental results have proved using cross validation that proposed model outperforms methodologies in terms of computation time and accuracy.Originality/value-Proposed evolving classifier represented as a robust architecture on using objective function to map the data sequence into a class distribution of the evolving disease class to the patient trajectory.Then,the generative output layer of the proposed model produces the progression outcome of the disease of the particular patient trajectory.The model tries to produce the accurate prognosis outcomes by employing data conditional probability function.The originality of the work defines 70%and comparisons of the previous methods the method of values are accurate and increased analysis of the predictions.展开更多
基金supported by the National Natural Science Foundation of China(grant nos.T2342011,32025021,and T2222021).
文摘"Boundarics in Biomedicine"is a cutting-edge interdisciplinary discipline,which is of great significance for understanding the origin of life,the interaction between internal and external environments,and the mechanism of disease occurrence and evolution.Here,the definition of Boundarics in Biomedicine is first described,including its connotation,research object,research method,challenges,and future perspectives."Boundarics in Biomedicine"is a cutting-edge interdisciplinary discipline involving multiple fields(e.g.,bioscience,medicine,chemistry,materials science,and information science)dedicated to investigating and solving key scientific questions in the formation,identification,and evolution of living organism boundaries.Specifically,it encompasses 3 levels:(a)the boundary between the living organism and the external environment,(b)internal boundary within living organism,and(c)the boundary related to disease in living organism.The advancement of research in Boundarics in Biomedicine is of great scientific significance for understanding the origin of life,the interaction between internal and external environments,and the mechanism of disease occurrence and evolution,thus providing novel principles,technologies,and methods for early diagnosis and prevention of major diseases,personalized drug development,and prognosis assessment(Fig.1).
文摘Purpose-Patient treatment trajectory data are used to predict the outcome of the treatment to particular disease that has been carried out in the research.In order to determine the evolving disease on the patient and changes in the health due to treatment has not considered existing methodologies.Hence deep learning models to trajectory data mining can be employed to identify disease prediction with high accuracy and less computation cost.Design/methodology/approach-Multifocus deep neural network classifiers has been utilized to detect the novel disease class and comorbidity class to the changes in the genome pattern of the patient trajectory data can be identified on the layers of the architecture.Classifier is employed to learn extracted feature set with activation and weight function and then merged on many aspects to classify the undetermined sequence of diseases as a new variant.The performance of disease progression learning progress utilizes the precision of the constituent classifiers,which usually has larger generalization benefits than those optimized classifiers.Findings-Deep learning architecture uses weight function,bias function on input layers and max pooling.Outcome of the input layer has applied to hidden layer to generate the multifocus characteristics of the disease,and multifocus characterized disease is processed in activation function using ReLu function along hyper parameter tuning which produces the effective outcome in the output layer of a fully connected network.Experimental results have proved using cross validation that proposed model outperforms methodologies in terms of computation time and accuracy.Originality/value-Proposed evolving classifier represented as a robust architecture on using objective function to map the data sequence into a class distribution of the evolving disease class to the patient trajectory.Then,the generative output layer of the proposed model produces the progression outcome of the disease of the particular patient trajectory.The model tries to produce the accurate prognosis outcomes by employing data conditional probability function.The originality of the work defines 70%and comparisons of the previous methods the method of values are accurate and increased analysis of the predictions.
