Lysine Lipoylation is a protective and conserved Post Translational Modification(PTM)in proteomics research like prokaryotes and eukaryotes.It is connected with many biological processes and closely linked with many m...Lysine Lipoylation is a protective and conserved Post Translational Modification(PTM)in proteomics research like prokaryotes and eukaryotes.It is connected with many biological processes and closely linked with many metabolic diseases.To develop a perfect and accurate classification model for identifying lipoylation sites at the protein level,the computational methods and several other factors play a key role in this purpose.Usually,most of the techniques and different traditional experimental models have a very high cost.They are time-consuming;so,it is required to construct a predictor model to extract lysine lipoylation sites.This study proposes a model that could predict lysine lipoylation sites with the help of a classification method known as Artificial Neural Network(ANN).The ANN algorithm deals with the noise problem and imbalance classification in lipoylation sites dataset samples.As the result shows in ten-fold cross-validation,a brilliant performance is achieved through the predictor model with an accuracy of 99.88%,and also achieved 0.9976 as the highest value of MCC.So,the predictor model is a very useful and helpful tool for lipoylation sites prediction.Some of the residues around lysine lipoylation sites play a vital part in prediction,as demonstrated during feature analysis.The wonderful results reported through the evaluation and prediction of this model can provide an informative and relative explanation for lipoylation and its molecular mechanisms.展开更多
Disease diagnosis is a challenging task due to a large number of associated factors.Uncertainty in the diagnosis process arises frominaccuracy in patient attributes,missing data,and limitation in the medical expert’s...Disease diagnosis is a challenging task due to a large number of associated factors.Uncertainty in the diagnosis process arises frominaccuracy in patient attributes,missing data,and limitation in the medical expert’s ability to define cause and effect relationships when there are multiple interrelated variables.This paper aims to demonstrate an integrated view of deploying smart disease diagnosis using the Internet of Things(IoT)empowered by the fuzzy inference system(FIS)to diagnose various diseases.The Fuzzy Systemis one of the best systems to diagnose medical conditions because every disease diagnosis involves many uncertainties,and fuzzy logic is the best way to handle uncertainties.Our proposed system differentiates new cases provided symptoms of the disease.Generally,it becomes a time-sensitive task to discriminate symptomatic diseases.The proposed system can track symptoms firmly to diagnose diseases through IoT and FIS smartly and efficiently.Different coefficients have been employed to predict and compute the identified disease’s severity for each sign of disease.This study aims to differentiate and diagnose COVID-19,Typhoid,Malaria,and Pneumonia.This study used the FIS method to figure out the disease over the use of given data related to correlating with input symptoms.MATLAB tool is utilised for the implementation of FIS.Fuzzy procedure on the aforementioned given data presents that affectionate disease can derive from the symptoms.The results of our proposed method proved that FIS could be utilised for the diagnosis of other diseases.This study may assist doctors,patients,medical practitioners,and other healthcare professionals in early diagnosis and better treat diseases.展开更多
文摘Lysine Lipoylation is a protective and conserved Post Translational Modification(PTM)in proteomics research like prokaryotes and eukaryotes.It is connected with many biological processes and closely linked with many metabolic diseases.To develop a perfect and accurate classification model for identifying lipoylation sites at the protein level,the computational methods and several other factors play a key role in this purpose.Usually,most of the techniques and different traditional experimental models have a very high cost.They are time-consuming;so,it is required to construct a predictor model to extract lysine lipoylation sites.This study proposes a model that could predict lysine lipoylation sites with the help of a classification method known as Artificial Neural Network(ANN).The ANN algorithm deals with the noise problem and imbalance classification in lipoylation sites dataset samples.As the result shows in ten-fold cross-validation,a brilliant performance is achieved through the predictor model with an accuracy of 99.88%,and also achieved 0.9976 as the highest value of MCC.So,the predictor model is a very useful and helpful tool for lipoylation sites prediction.Some of the residues around lysine lipoylation sites play a vital part in prediction,as demonstrated during feature analysis.The wonderful results reported through the evaluation and prediction of this model can provide an informative and relative explanation for lipoylation and its molecular mechanisms.
文摘Disease diagnosis is a challenging task due to a large number of associated factors.Uncertainty in the diagnosis process arises frominaccuracy in patient attributes,missing data,and limitation in the medical expert’s ability to define cause and effect relationships when there are multiple interrelated variables.This paper aims to demonstrate an integrated view of deploying smart disease diagnosis using the Internet of Things(IoT)empowered by the fuzzy inference system(FIS)to diagnose various diseases.The Fuzzy Systemis one of the best systems to diagnose medical conditions because every disease diagnosis involves many uncertainties,and fuzzy logic is the best way to handle uncertainties.Our proposed system differentiates new cases provided symptoms of the disease.Generally,it becomes a time-sensitive task to discriminate symptomatic diseases.The proposed system can track symptoms firmly to diagnose diseases through IoT and FIS smartly and efficiently.Different coefficients have been employed to predict and compute the identified disease’s severity for each sign of disease.This study aims to differentiate and diagnose COVID-19,Typhoid,Malaria,and Pneumonia.This study used the FIS method to figure out the disease over the use of given data related to correlating with input symptoms.MATLAB tool is utilised for the implementation of FIS.Fuzzy procedure on the aforementioned given data presents that affectionate disease can derive from the symptoms.The results of our proposed method proved that FIS could be utilised for the diagnosis of other diseases.This study may assist doctors,patients,medical practitioners,and other healthcare professionals in early diagnosis and better treat diseases.
