Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: Identifying drug–target interactions is an important task in drug discovery. To reduce heavy time and financial cost in experimental way, many computational approaches have been proposed. Although these approaches have used many different principles, their performance is far from satisfactory, especially in predicting drug–target interactions of new candidate drugs or targets.#R##N##R##N#Methods: Approaches based on machine learning for this problem can be divided into two types: feature-based and similarity-based methods. Learning to rank is the most porful technique in the feature-based methods. Similarity-based methods are ll accepted, due to their idea of connecting the chemical and genomic spaces, represented by drug and target similarities, respectively.  propose a new method, DrugE-Rank, to improve the prediction performance by nicely combining the advantages of the two different types of methods. That is, DrugE-Rank uses LTR, for which multiple ll-known similarity-based methods can be used as components of ensemble learning.#R##N##R##N#Results: The performance of DrugE-Rank is thoroughly examined by three main experiments using data from DrugBank: (i) cross-validation on FDA (US Food and Drug Administration) approved drugs before March 2014; (ii) independent test on FDA approved drugs after March 2014; and (iii) independent test on FDA experimental drugs. Experimental results show that DrugE-Rank outperforms competing methods significantly, especially achieving more than 30% improvement in Area under Prediction Recall curve for FDA approved new drugs and FDA experimental drugs.#R##N##R##N#Availability: http://datamining-iip.fudan.edu.cn/service/DrugE-Rank#R##N##R##N#Contact: nc.ude.naduf@fsuhz#R##N##R##N#Supplementary information: Supplementary data are available at Bioinformatics online.
['Qingjun Yuan', 'Junning Gao', 'Dongliang Wu', 'Shihua Zhang', 'Hiroshi Mamitsuka', 'Shanfeng Zhu']
I am writing a research paper on a idea DrugE-Rank: improving drug–target interaction prediction of new candidate drugs or targets by ensemble learning to rank
[{"from": "human", "value": "I am writing a research paper on a idea DrugE-Rank: improving drug\u2013target interaction prediction of new candidate drugs or targets by ensemble learning to rank"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: Identifying drug\u2013target interactions is an important task in drug discovery. To reduce heavy time and financial cost in experimental way, many computational approaches have been proposed. Although these approaches have used many different principles, their performance is far from satisfactory, especially in predicting drug\u2013target interactions of new candidate drugs or targets.#R##N##R##N#Methods: Approaches based on machine learning for this problem can be divided into two types: feature-based and similarity-based methods. Learning to rank is the most porful technique in the feature-based methods. Similarity-based methods are ll accepted, due to their idea of connecting the chemical and genomic spaces, represented by drug and target similarities, respectively.  propose a new method, DrugE-Rank, to improve the prediction performance by nicely combining the advantages of the two different types of methods. That is, DrugE-Rank uses LTR, for which multiple ll-known similarity-based methods can be used as components of ensemble learning.#R##N##R##N#Results: The performance of DrugE-Rank is thoroughly examined by three main experiments using data from DrugBank: (i) cross-validation on FDA (US Food and Drug Administration) approved drugs before March 2014; (ii) independent test on FDA approved drugs after March 2014; and (iii) independent test on FDA experimental drugs. Experimental results show that DrugE-Rank outperforms competing methods significantly, especially achieving more than 30% improvement in Area under Prediction Recall curve for FDA approved new drugs and FDA experimental drugs.#R##N##R##N#Availability: http://datamining-iip.fudan.edu.cn/service/DrugE-Rank#R##N##R##N#Contact: nc.ude.naduf@fsuhz#R##N##R##N#Supplementary information: Supplementary data are available at Bioinformatics online."}]