请问用来垫高家具的东西在哪买？# Living
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现转让一篇英国皇家化学学会的杂志的稿子.
如果有相关的经验,又需要审稿,请把你的名字,单位,非个人的email发到我的邮箱.
Molecular BioSystems
TITLE: Prediction of drug-target interaction by label propagation with
mutual interaction information derived from heterogeneous network
ABSTRACT:
Identification of potential drug-target interaction pairs is very important,
which is not only for providing greater understanding of protein function,
but also for enhancing drug research, especially for drug function
repositioning. Recently, numerous machine learning-based algorithms (e.g.
kernel-based, matrix factorization-based and network-based inference methods
) have been developed for predicting drug-target interactions. All these
methods implicitly utilize the assumption that similar drugs tend to target
similar proteins. However, many approaches cannot be applied to drugs
without any known target information, and they just only use the chemical or
genomics information. To further improve the accuracy of prediction, a
novel method of network-based label propagation with mutual interaction
information derived from heterogeneous network, namely LPMIHN, is proposed
to infer the potential drug-target interactions. LPMIHN fuses the drug’s
chemical similarity, target protein sequence similarity with their
respective topological similarity in drug-target interaction network to
construct the drug-target heterogeneous network, and integrates drug/target
label information through bipartite graph matrix to obtain the initial
target label, then implements respectively the label propagation on drug
similarity network and target protein similarity network until convergence
to the global optimal solution. As a result, all targets are ranked by label
propagation for a query drug. Comparison with other recent state-of-the-art
methods on the four popular benchmark datasets of binary drug-target
interactions and two quantitative kinase bioactivity datasets, LPMIHN
achieves the best results in AUC and AUPR. In addition, many of the
promising drug-target pairs predicted from LPMIHN are also confirmed on the
latest publicly available drug-target databases such as ChEMBL, KEGG,
SuperTarget and Drugbank. Those results demonstrate the effectiveness of
LPMIHN and also indicate that LPMIHN has the great potential for predicting
drug-target interactions.
如果有相关的经验,又需要审稿,请把你的名字,单位,非个人的email发到我的邮箱.
Molecular BioSystems
TITLE: Prediction of drug-target interaction by label propagation with
mutual interaction information derived from heterogeneous network
ABSTRACT:
Identification of potential drug-target interaction pairs is very important,
which is not only for providing greater understanding of protein function,
but also for enhancing drug research, especially for drug function
repositioning. Recently, numerous machine learning-based algorithms (e.g.
kernel-based, matrix factorization-based and network-based inference methods
) have been developed for predicting drug-target interactions. All these
methods implicitly utilize the assumption that similar drugs tend to target
similar proteins. However, many approaches cannot be applied to drugs
without any known target information, and they just only use the chemical or
genomics information. To further improve the accuracy of prediction, a
novel method of network-based label propagation with mutual interaction
information derived from heterogeneous network, namely LPMIHN, is proposed
to infer the potential drug-target interactions. LPMIHN fuses the drug’s
chemical similarity, target protein sequence similarity with their
respective topological similarity in drug-target interaction network to
construct the drug-target heterogeneous network, and integrates drug/target
label information through bipartite graph matrix to obtain the initial
target label, then implements respectively the label propagation on drug
similarity network and target protein similarity network until convergence
to the global optimal solution. As a result, all targets are ranked by label
propagation for a query drug. Comparison with other recent state-of-the-art
methods on the four popular benchmark datasets of binary drug-target
interactions and two quantitative kinase bioactivity datasets, LPMIHN
achieves the best results in AUC and AUPR. In addition, many of the
promising drug-target pairs predicted from LPMIHN are also confirmed on the
latest publicly available drug-target databases such as ChEMBL, KEGG,
SuperTarget and Drugbank. Those results demonstrate the effectiveness of
LPMIHN and also indicate that LPMIHN has the great potential for predicting
drug-target interactions.
