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Title |
Drug-Drug Interaction Prediction Using Krill Herd Algorithm Based on Deep Learning Method |
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Author |
Abdulsamad Al-Marghilani |
| Citation |
Vol. 21 No. 7 pp. 319-328 |
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Abstract |
Parallel administration of numerous drugs increases Drug-Drug Interaction (DDI) because one drug might affect the activity of other drugs. DDI causes negative or positive impacts on therapeutic output. So there is a need to discover DDI to enhance the safety of consuming drugs. Though there are several DDI system exist to predict an interaction but nowadays it becomes impossible to maintain with a large number of biomedical texts which is getting increased rapidly. Mostly the existing DDI system address classification issues, and especially rely on handcrafted features, and some features which are based on particular domain tools. The objective of this paper to predict DDI in a way to avoid adverse effects caused by the consumed drugs, to predict similarities among the drug, Drug pair similarity calculation is performed. The best optimal weight is obtained with the support of KHA. LSTM function with weight obtained from KHA and makes bets prediction of DDI. Our methodology depends on (LSTM-KHA) for the detection of DDI. Similarities among the drugs are measured with the help of drug pair similarity calculation. KHA is used to find the best optimal weight which is used by LSTM to predict DDI. The experimental result was conducted on three kinds of dataset DS1 (CYP), DS2 (NCYP), and DS3 taken from the DrugBank database. To evaluate the performance of proposed work in terms of performance metrics like accuracy, recall, precision, F-measures, AUPR, AUC, and AUROC. Experimental results express that the proposed method outperforms other existing methods for predicting DDI. LSTM-KHA produces reasonable performance metrics when compared to the existing DDI prediction model. |
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Keywords |
Drug-Drug Interaction, Long Short Term Memory, Krill Herd Algorithm, Deep Learning, Machine Learning |
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URL |
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Title |
Drug-Drug Interaction Prediction Using Krill Herd Algorithm Based on Deep Learning Method |
|
Author |
Abdulsamad Al-Marghilani |
| Citation |
Vol. 21 No. 7 pp. 319-328 |
|
Abstract |
Parallel administration of numerous drugs increases Drug-Drug Interaction (DDI) because one drug might affect the activity of other drugs. DDI causes negative or positive impacts on therapeutic output. So there is a need to discover DDI to enhance the safety of consuming drugs. Though there are several DDI system exist to predict an interaction but nowadays it becomes impossible to maintain with a large number of biomedical texts which is getting increased rapidly. Mostly the existing DDI system address classification issues, and especially rely on handcrafted features, and some features which are based on particular domain tools. The objective of this paper to predict DDI in a way to avoid adverse effects caused by the consumed drugs, to predict similarities among the drug, Drug pair similarity calculation is performed. The best optimal weight is obtained with the support of KHA. LSTM function with weight obtained from KHA and makes bets prediction of DDI. Our methodology depends on (LSTM-KHA) for the detection of DDI. Similarities among the drugs are measured with the help of drug pair similarity calculation. KHA is used to find the best optimal weight which is used by LSTM to predict DDI. The experimental result was conducted on three kinds of dataset DS1 (CYP), DS2 (NCYP), and DS3 taken from the DrugBank database. To evaluate the performance of proposed work in terms of performance metrics like accuracy, recall, precision, F-measures, AUPR, AUC, and AUROC. Experimental results express that the proposed method outperforms other existing methods for predicting DDI. LSTM-KHA produces reasonable performance metrics when compared to the existing DDI prediction model. |
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Keywords |
Drug-Drug Interaction, Long Short Term Memory, Krill Herd Algorithm, Deep Learning, Machine Learning |
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URL |
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Title |
Drug-Drug Interaction Prediction Using Krill Herd Algorithm Based on Deep Learning Method |
|
Author |
Abdulsamad Al-Marghilani |
| Citation |
Vol. 21 No. 7 pp. 319-328 |
|
Abstract |
Parallel administration of numerous drugs increases Drug-Drug Interaction (DDI) because one drug might affect the activity of other drugs. DDI causes negative or positive impacts on therapeutic output. So there is a need to discover DDI to enhance the safety of consuming drugs. Though there are several DDI system exist to predict an interaction but nowadays it becomes impossible to maintain with a large number of biomedical texts which is getting increased rapidly. Mostly the existing DDI system address classification issues, and especially rely on handcrafted features, and some features which are based on particular domain tools. The objective of this paper to predict DDI in a way to avoid adverse effects caused by the consumed drugs, to predict similarities among the drug, Drug pair similarity calculation is performed. The best optimal weight is obtained with the support of KHA. LSTM function with weight obtained from KHA and makes bets prediction of DDI. Our methodology depends on (LSTM-KHA) for the detection of DDI. Similarities among the drugs are measured with the help of drug pair similarity calculation. KHA is used to find the best optimal weight which is used by LSTM to predict DDI. The experimental result was conducted on three kinds of dataset DS1 (CYP), DS2 (NCYP), and DS3 taken from the DrugBank database. To evaluate the performance of proposed work in terms of performance metrics like accuracy, recall, precision, F-measures, AUPR, AUC, and AUROC. Experimental results express that the proposed method outperforms other existing methods for predicting DDI. LSTM-KHA produces reasonable performance metrics when compared to the existing DDI prediction model. |
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Keywords |
Drug-Drug Interaction, Long Short Term Memory, Krill Herd Algorithm, Deep Learning, Machine Learning |
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URL |
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Title |
Drug-Drug Interaction Prediction Using Krill Herd Algorithm Based on Deep Learning Method |
|
Author |
Abdulsamad Al-Marghilani |
| Citation |
Vol. 21 No. 7 pp. 319-328 |
|
Abstract |
Parallel administration of numerous drugs increases Drug-Drug Interaction (DDI) because one drug might affect the activity of other drugs. DDI causes negative or positive impacts on therapeutic output. So there is a need to discover DDI to enhance the safety of consuming drugs. Though there are several DDI system exist to predict an interaction but nowadays it becomes impossible to maintain with a large number of biomedical texts which is getting increased rapidly. Mostly the existing DDI system address classification issues, and especially rely on handcrafted features, and some features which are based on particular domain tools. The objective of this paper to predict DDI in a way to avoid adverse effects caused by the consumed drugs, to predict similarities among the drug, Drug pair similarity calculation is performed. The best optimal weight is obtained with the support of KHA. LSTM function with weight obtained from KHA and makes bets prediction of DDI. Our methodology depends on (LSTM-KHA) for the detection of DDI. Similarities among the drugs are measured with the help of drug pair similarity calculation. KHA is used to find the best optimal weight which is used by LSTM to predict DDI. The experimental result was conducted on three kinds of dataset DS1 (CYP), DS2 (NCYP), and DS3 taken from the DrugBank database. To evaluate the performance of proposed work in terms of performance metrics like accuracy, recall, precision, F-measures, AUPR, AUC, and AUROC. Experimental results express that the proposed method outperforms other existing methods for predicting DDI. LSTM-KHA produces reasonable performance metrics when compared to the existing DDI prediction model. |
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Keywords |
Drug-Drug Interaction, Long Short Term Memory, Krill Herd Algorithm, Deep Learning, Machine Learning |
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URL |
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Title |
Drug-Drug Interaction Prediction Using Krill Herd Algorithm Based on Deep Learning Method |
|
Author |
Abdulsamad Al-Marghilani |
| Citation |
Vol. 21 No. 7 pp. 319-328 |
|
Abstract |
Parallel administration of numerous drugs increases Drug-Drug Interaction (DDI) because one drug might affect the activity of other drugs. DDI causes negative or positive impacts on therapeutic output. So there is a need to discover DDI to enhance the safety of consuming drugs. Though there are several DDI system exist to predict an interaction but nowadays it becomes impossible to maintain with a large number of biomedical texts which is getting increased rapidly. Mostly the existing DDI system address classification issues, and especially rely on handcrafted features, and some features which are based on particular domain tools. The objective of this paper to predict DDI in a way to avoid adverse effects caused by the consumed drugs, to predict similarities among the drug, Drug pair similarity calculation is performed. The best optimal weight is obtained with the support of KHA. LSTM function with weight obtained from KHA and makes bets prediction of DDI. Our methodology depends on (LSTM-KHA) for the detection of DDI. Similarities among the drugs are measured with the help of drug pair similarity calculation. KHA is used to find the best optimal weight which is used by LSTM to predict DDI. The experimental result was conducted on three kinds of dataset DS1 (CYP), DS2 (NCYP), and DS3 taken from the DrugBank database. To evaluate the performance of proposed work in terms of performance metrics like accuracy, recall, precision, F-measures, AUPR, AUC, and AUROC. Experimental results express that the proposed method outperforms other existing methods for predicting DDI. LSTM-KHA produces reasonable performance metrics when compared to the existing DDI prediction model. |
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Keywords |
Drug-Drug Interaction, Long Short Term Memory, Krill Herd Algorithm, Deep Learning, Machine Learning |
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URL |
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Title |
Drug-Drug Interaction Prediction Using Krill Herd Algorithm Based on Deep Learning Method |
|
Author |
Abdulsamad Al-Marghilani |
| Citation |
Vol. 21 No. 7 pp. 319-328 |
|
Abstract |
Parallel administration of numerous drugs increases Drug-Drug Interaction (DDI) because one drug might affect the activity of other drugs. DDI causes negative or positive impacts on therapeutic output. So there is a need to discover DDI to enhance the safety of consuming drugs. Though there are several DDI system exist to predict an interaction but nowadays it becomes impossible to maintain with a large number of biomedical texts which is getting increased rapidly. Mostly the existing DDI system address classification issues, and especially rely on handcrafted features, and some features which are based on particular domain tools. The objective of this paper to predict DDI in a way to avoid adverse effects caused by the consumed drugs, to predict similarities among the drug, Drug pair similarity calculation is performed. The best optimal weight is obtained with the support of KHA. LSTM function with weight obtained from KHA and makes bets prediction of DDI. Our methodology depends on (LSTM-KHA) for the detection of DDI. Similarities among the drugs are measured with the help of drug pair similarity calculation. KHA is used to find the best optimal weight which is used by LSTM to predict DDI. The experimental result was conducted on three kinds of dataset DS1 (CYP), DS2 (NCYP), and DS3 taken from the DrugBank database. To evaluate the performance of proposed work in terms of performance metrics like accuracy, recall, precision, F-measures, AUPR, AUC, and AUROC. Experimental results express that the proposed method outperforms other existing methods for predicting DDI. LSTM-KHA produces reasonable performance metrics when compared to the existing DDI prediction model. |
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Keywords |
Drug-Drug Interaction, Long Short Term Memory, Krill Herd Algorithm, Deep Learning, Machine Learning |
|
URL |
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|
Title |
Drug-Drug Interaction Prediction Using Krill Herd Algorithm Based on Deep Learning Method |
|
Author |
Abdulsamad Al-Marghilani |
| Citation |
Vol. 21 No. 7 pp. 319-328 |
|
Abstract |
Parallel administration of numerous drugs increases Drug-Drug Interaction (DDI) because one drug might affect the activity of other drugs. DDI causes negative or positive impacts on therapeutic output. So there is a need to discover DDI to enhance the safety of consuming drugs. Though there are several DDI system exist to predict an interaction but nowadays it becomes impossible to maintain with a large number of biomedical texts which is getting increased rapidly. Mostly the existing DDI system address classification issues, and especially rely on handcrafted features, and some features which are based on particular domain tools. The objective of this paper to predict DDI in a way to avoid adverse effects caused by the consumed drugs, to predict similarities among the drug, Drug pair similarity calculation is performed. The best optimal weight is obtained with the support of KHA. LSTM function with weight obtained from KHA and makes bets prediction of DDI. Our methodology depends on (LSTM-KHA) for the detection of DDI. Similarities among the drugs are measured with the help of drug pair similarity calculation. KHA is used to find the best optimal weight which is used by LSTM to predict DDI. The experimental result was conducted on three kinds of dataset DS1 (CYP), DS2 (NCYP), and DS3 taken from the DrugBank database. To evaluate the performance of proposed work in terms of performance metrics like accuracy, recall, precision, F-measures, AUPR, AUC, and AUROC. Experimental results express that the proposed method outperforms other existing methods for predicting DDI. LSTM-KHA produces reasonable performance metrics when compared to the existing DDI prediction model. |
|
Keywords |
Drug-Drug Interaction, Long Short Term Memory, Krill Herd Algorithm, Deep Learning, Machine Learning |
|
URL |
|
|
Title |
Drug-Drug Interaction Prediction Using Krill Herd Algorithm Based on Deep Learning Method |
|
Author |
Abdulsamad Al-Marghilani |
| Citation |
Vol. 21 No. 7 pp. 319-328 |
|
Abstract |
Parallel administration of numerous drugs increases Drug-Drug Interaction (DDI) because one drug might affect the activity of other drugs. DDI causes negative or positive impacts on therapeutic output. So there is a need to discover DDI to enhance the safety of consuming drugs. Though there are several DDI system exist to predict an interaction but nowadays it becomes impossible to maintain with a large number of biomedical texts which is getting increased rapidly. Mostly the existing DDI system address classification issues, and especially rely on handcrafted features, and some features which are based on particular domain tools. The objective of this paper to predict DDI in a way to avoid adverse effects caused by the consumed drugs, to predict similarities among the drug, Drug pair similarity calculation is performed. The best optimal weight is obtained with the support of KHA. LSTM function with weight obtained from KHA and makes bets prediction of DDI. Our methodology depends on (LSTM-KHA) for the detection of DDI. Similarities among the drugs are measured with the help of drug pair similarity calculation. KHA is used to find the best optimal weight which is used by LSTM to predict DDI. The experimental result was conducted on three kinds of dataset DS1 (CYP), DS2 (NCYP), and DS3 taken from the DrugBank database. To evaluate the performance of proposed work in terms of performance metrics like accuracy, recall, precision, F-measures, AUPR, AUC, and AUROC. Experimental results express that the proposed method outperforms other existing methods for predicting DDI. LSTM-KHA produces reasonable performance metrics when compared to the existing DDI prediction model. |
|
Keywords |
Drug-Drug Interaction, Long Short Term Memory, Krill Herd Algorithm, Deep Learning, Machine Learning |
|
URL |
|
|
Title |
Drug-Drug Interaction Prediction Using Krill Herd Algorithm Based on Deep Learning Method |
|
Author |
Abdulsamad Al-Marghilani |
| Citation |
Vol. 21 No. 7 pp. 319-328 |
|
Abstract |
Parallel administration of numerous drugs increases Drug-Drug Interaction (DDI) because one drug might affect the activity of other drugs. DDI causes negative or positive impacts on therapeutic output. So there is a need to discover DDI to enhance the safety of consuming drugs. Though there are several DDI system exist to predict an interaction but nowadays it becomes impossible to maintain with a large number of biomedical texts which is getting increased rapidly. Mostly the existing DDI system address classification issues, and especially rely on handcrafted features, and some features which are based on particular domain tools. The objective of this paper to predict DDI in a way to avoid adverse effects caused by the consumed drugs, to predict similarities among the drug, Drug pair similarity calculation is performed. The best optimal weight is obtained with the support of KHA. LSTM function with weight obtained from KHA and makes bets prediction of DDI. Our methodology depends on (LSTM-KHA) for the detection of DDI. Similarities among the drugs are measured with the help of drug pair similarity calculation. KHA is used to find the best optimal weight which is used by LSTM to predict DDI. The experimental result was conducted on three kinds of dataset DS1 (CYP), DS2 (NCYP), and DS3 taken from the DrugBank database. To evaluate the performance of proposed work in terms of performance metrics like accuracy, recall, precision, F-measures, AUPR, AUC, and AUROC. Experimental results express that the proposed method outperforms other existing methods for predicting DDI. LSTM-KHA produces reasonable performance metrics when compared to the existing DDI prediction model. |
|
Keywords |
Drug-Drug Interaction, Long Short Term Memory, Krill Herd Algorithm, Deep Learning, Machine Learning |
|
URL |
|
|
Title |
Drug-Drug Interaction Prediction Using Krill Herd Algorithm Based on Deep Learning Method |
|
Author |
Abdulsamad Al-Marghilani |
| Citation |
Vol. 21 No. 7 pp. 319-328 |
|
Abstract |
Parallel administration of numerous drugs increases Drug-Drug Interaction (DDI) because one drug might affect the activity of other drugs. DDI causes negative or positive impacts on therapeutic output. So there is a need to discover DDI to enhance the safety of consuming drugs. Though there are several DDI system exist to predict an interaction but nowadays it becomes impossible to maintain with a large number of biomedical texts which is getting increased rapidly. Mostly the existing DDI system address classification issues, and especially rely on handcrafted features, and some features which are based on particular domain tools. The objective of this paper to predict DDI in a way to avoid adverse effects caused by the consumed drugs, to predict similarities among the drug, Drug pair similarity calculation is performed. The best optimal weight is obtained with the support of KHA. LSTM function with weight obtained from KHA and makes bets prediction of DDI. Our methodology depends on (LSTM-KHA) for the detection of DDI. Similarities among the drugs are measured with the help of drug pair similarity calculation. KHA is used to find the best optimal weight which is used by LSTM to predict DDI. The experimental result was conducted on three kinds of dataset DS1 (CYP), DS2 (NCYP), and DS3 taken from the DrugBank database. To evaluate the performance of proposed work in terms of performance metrics like accuracy, recall, precision, F-measures, AUPR, AUC, and AUROC. Experimental results express that the proposed method outperforms other existing methods for predicting DDI. LSTM-KHA produces reasonable performance metrics when compared to the existing DDI prediction model. |
|
Keywords |
Drug-Drug Interaction, Long Short Term Memory, Krill Herd Algorithm, Deep Learning, Machine Learning |
|
URL |
|
|
Title |
Drug-Drug Interaction Prediction Using Krill Herd Algorithm Based on Deep Learning Method |
|
Author |
Abdulsamad Al-Marghilani |
| Citation |
Vol. 21 No. 7 pp. 319-328 |
|
Abstract |
Parallel administration of numerous drugs increases Drug-Drug Interaction (DDI) because one drug might affect the activity of other drugs. DDI causes negative or positive impacts on therapeutic output. So there is a need to discover DDI to enhance the safety of consuming drugs. Though there are several DDI system exist to predict an interaction but nowadays it becomes impossible to maintain with a large number of biomedical texts which is getting increased rapidly. Mostly the existing DDI system address classification issues, and especially rely on handcrafted features, and some features which are based on particular domain tools. The objective of this paper to predict DDI in a way to avoid adverse effects caused by the consumed drugs, to predict similarities among the drug, Drug pair similarity calculation is performed. The best optimal weight is obtained with the support of KHA. LSTM function with weight obtained from KHA and makes bets prediction of DDI. Our methodology depends on (LSTM-KHA) for the detection of DDI. Similarities among the drugs are measured with the help of drug pair similarity calculation. KHA is used to find the best optimal weight which is used by LSTM to predict DDI. The experimental result was conducted on three kinds of dataset DS1 (CYP), DS2 (NCYP), and DS3 taken from the DrugBank database. To evaluate the performance of proposed work in terms of performance metrics like accuracy, recall, precision, F-measures, AUPR, AUC, and AUROC. Experimental results express that the proposed method outperforms other existing methods for predicting DDI. LSTM-KHA produces reasonable performance metrics when compared to the existing DDI prediction model. |
|
Keywords |
Drug-Drug Interaction, Long Short Term Memory, Krill Herd Algorithm, Deep Learning, Machine Learning |
|
URL |
|
|
Title |
Drug-Drug Interaction Prediction Using Krill Herd Algorithm Based on Deep Learning Method |
|
Author |
Abdulsamad Al-Marghilani |
| Citation |
Vol. 21 No. 7 pp. 319-328 |
|
Abstract |
Parallel administration of numerous drugs increases Drug-Drug Interaction (DDI) because one drug might affect the activity of other drugs. DDI causes negative or positive impacts on therapeutic output. So there is a need to discover DDI to enhance the safety of consuming drugs. Though there are several DDI system exist to predict an interaction but nowadays it becomes impossible to maintain with a large number of biomedical texts which is getting increased rapidly. Mostly the existing DDI system address classification issues, and especially rely on handcrafted features, and some features which are based on particular domain tools. The objective of this paper to predict DDI in a way to avoid adverse effects caused by the consumed drugs, to predict similarities among the drug, Drug pair similarity calculation is performed. The best optimal weight is obtained with the support of KHA. LSTM function with weight obtained from KHA and makes bets prediction of DDI. Our methodology depends on (LSTM-KHA) for the detection of DDI. Similarities among the drugs are measured with the help of drug pair similarity calculation. KHA is used to find the best optimal weight which is used by LSTM to predict DDI. The experimental result was conducted on three kinds of dataset DS1 (CYP), DS2 (NCYP), and DS3 taken from the DrugBank database. To evaluate the performance of proposed work in terms of performance metrics like accuracy, recall, precision, F-measures, AUPR, AUC, and AUROC. Experimental results express that the proposed method outperforms other existing methods for predicting DDI. LSTM-KHA produces reasonable performance metrics when compared to the existing DDI prediction model. |
|
Keywords |
Drug-Drug Interaction, Long Short Term Memory, Krill Herd Algorithm, Deep Learning, Machine Learning |
|
URL |
|
|
Title |
Drug-Drug Interaction Prediction Using Krill Herd Algorithm Based on Deep Learning Method |
|
Author |
Abdulsamad Al-Marghilani |
| Citation |
Vol. 21 No. 7 pp. 319-328 |
|
Abstract |
Parallel administration of numerous drugs increases Drug-Drug Interaction (DDI) because one drug might affect the activity of other drugs. DDI causes negative or positive impacts on therapeutic output. So there is a need to discover DDI to enhance the safety of consuming drugs. Though there are several DDI system exist to predict an interaction but nowadays it becomes impossible to maintain with a large number of biomedical texts which is getting increased rapidly. Mostly the existing DDI system address classification issues, and especially rely on handcrafted features, and some features which are based on particular domain tools. The objective of this paper to predict DDI in a way to avoid adverse effects caused by the consumed drugs, to predict similarities among the drug, Drug pair similarity calculation is performed. The best optimal weight is obtained with the support of KHA. LSTM function with weight obtained from KHA and makes bets prediction of DDI. Our methodology depends on (LSTM-KHA) for the detection of DDI. Similarities among the drugs are measured with the help of drug pair similarity calculation. KHA is used to find the best optimal weight which is used by LSTM to predict DDI. The experimental result was conducted on three kinds of dataset DS1 (CYP), DS2 (NCYP), and DS3 taken from the DrugBank database. To evaluate the performance of proposed work in terms of performance metrics like accuracy, recall, precision, F-measures, AUPR, AUC, and AUROC. Experimental results express that the proposed method outperforms other existing methods for predicting DDI. LSTM-KHA produces reasonable performance metrics when compared to the existing DDI prediction model. |
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Keywords |
Drug-Drug Interaction, Long Short Term Memory, Krill Herd Algorithm, Deep Learning, Machine Learning |
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URL |
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Title |
A Component Model for Managing Covid-19 Crisis |
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Author |
Faris M. Taweel |
| Citation |
Vol. 21 No. 7 pp. 365-373 |
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Abstract |
Covid-19 posed a serious threat to public health worldwide, especially in the absence of vaccines or medicines. The only viable strategies to combat a virus with a high infection rate were to apply lock-down strategies, transport ban, social and physical distancing. In this work, we provide a domain-specific component model for crisis management. The model allows for building a plan for managing Covid-19 crisis and use the plan as a template to generate a system specific for managing that crisis. The crisis component model is derived from X-MAN II, a generic component model that we have developed for the aircraft industry. |
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Keywords |
X-MAN II, Covid-19, Crisis Management, Component model, Crisis component model, Domain-specific component model, Lock-down; Physical distancing. |
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URL |
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Title |
A Component Model for Managing Covid-19 Crisis |
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Author |
Faris M. Taweel |
| Citation |
Vol. 21 No. 7 pp. 365-373 |
|
Abstract |
Covid-19 posed a serious threat to public health worldwide, especially in the absence of vaccines or medicines. The only viable strategies to combat a virus with a high infection rate were to apply lock-down strategies, transport ban, social and physical distancing. In this work, we provide a domain-specific component model for crisis management. The model allows for building a plan for managing Covid-19 crisis and use the plan as a template to generate a system specific for managing that crisis. The crisis component model is derived from X-MAN II, a generic component model that we have developed for the aircraft industry. |
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Keywords |
X-MAN II, Covid-19, Crisis Management, Component model, Crisis component model, Domain-specific component model, Lock-down; Physical distancing. |
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URL |
