Drug–target interaction prediction through domain-tuned network-based inference

Alaimo, Salvatore; Pulvirenti, Alfredo; Giugno, Rosalba; Ferro, Alfredo

doi:10.1093/bioinformatics/btt307

Cited by 155 publications

(111 citation statements)

References 22 publications

(44 reference statements)

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“…Cheng et al (2012) propose three infer methods including drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI) to predict drug-target interactions. Similarity work has been accomplished by Alaimo et al (2013). They present a DT-hybrid approach which extends a networkbased inference method by using domain-based knowledge to detect drug-target interactions.…”

Section: Introductionmentioning

confidence: 93%

Predicting drug-target interaction based on sequence and structure information

Lan

Wang

et al. 2015

IFAC-PapersOnLine

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It is well known that discovering a new drug is a cumbersome, time-consuming and expensive process. Computational approaches for identifying interactions between drug compounds and target proteins have become important in drug discovery which is helpful to reduce these obstacles. The difficulties of drug-target interaction identification include the lack of known drug-target associations and no experimentally verified negative examples. In this study, we present a method, called PUDT, to predict drug-target interactions. Instead of treating unknown interactions as negative examples, we consider unknown interactions as unlabeled examples. The unlabeled examples are divided into two parts: reliable negative examples and likely negative examples based on protein structure similarity.Then, a weighted support vector machine is used to build a classifier to predict drug-target interactions based on protein sequence and drug structure information. Four data sets (enzymes, ion channels, GPCRs and nuclear receptors) are used to evaluate the performance of the proposed method PUDT. The experimental results demonstrate that our method PUDT outperforms recent state-of-the-art approaches.

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Section: Introductionmentioning

confidence: 93%

Predicting drug-target interaction based on sequence and structure information

Lan

Wang

et al. 2015

IFAC-PapersOnLine

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“…Moreover, in the network biology and medicine context, various network-based tools and packages covering different programming languages are available, including GeneMANIA (http://www.genemania.org), a web-server and MATLAB standalone application for gene function prediction and prioritization; Aleph (http://aleph-ml.sourceforge.net), a Java tool for machine learning on graphs including node label prediction with label propagation (LP ) and random walks (RW ); DTHybrid (http://alpha.dmi.unict.it/dtweb/dthybrid.php), an R package to infer Drug-Target interactions implementing the homonym algorithm [12]; GUILDify (http://sbi.imim.es/web/GUILDify.php) and PhenoPred (http://www.phenopred.org), web-servers to discover gene-disease associations.…”

Section: Problem and Backgroundmentioning

confidence: 99%

COSNet: An R package for label prediction in unbalanced biological networks

Frasca

Valentini

2017

Neurocomputing

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Several problems in computational biology and medicine are modelled as learning problems in graphs, where nodes represent the biological entities to be studied, e.g. proteins, and connections different kinds of relationships among them, e.g. protein-protein interactions. In this context, classes are usually characterized by a high imbalance, i.e. positive examples for a class are much less than those negative. Although several works studied this problem, no graphbased software designed to explicitly take into account the label imbalance in biological networks is available. We propose COSNet , an R package to serve this purpose. COSNet deals with the label imbalance problem by implementing a novel parametric model of Hopfield Network (HN), whose output levels and activation thresholds of neurons are parameters to be automatically learnt. Due to the quasi linear time complexity, COSNet nicely scales when the number of instances is large, and application examples to challenging problems in biomedicine show the efficiency and the accuracy of the proposed library.

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“…Therefore, how to deal with this problem becomes an emerging issue. Over decades, different computational methods and tools [5][6][7][8][9][10][11][12][13] have been developed to predict large-scale potential DTIs and drug repositing through the unremitting efforts of a large number of researchers and organizations under the development of computing technology.…”

Section: Introductionmentioning

confidence: 99%

SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures

Cheng

Lan

et al. 2017

Complexity

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It is well known that drug discovery for complex diseases via biological experiments is a time-consuming and expensive process. Alternatively, the computational methods provide a low-cost and high-efficiency way for predicting drug-target interactions (DTIs) from biomolecular networks. However, the current computational methods mainly deal with DTI predictions of known drugs; there are few methods for large-scale prediction of failed drugs and new chemical entities that are currently stored in some biological databases may be effective for other diseases compared with their originally targeted diseases. In this study, we propose a method (called SDTRLS) which predicts DTIs through RLS-Kron model with chemical substructure similarity fusion and Gaussian Interaction Profile (GIP) kernels. SDTRLS can be an effective predictor for targets of old drugs, failed drugs, and new chemical entities from large-scale biomolecular network databases. Our computational experiments show that SDTRLS outperforms the state-of-the-art SDTNBI method; specifically, in the G protein-coupled receptors (GPCRs) external validation, the maximum and the average AUC values of SDTRLS are 0.842 and 0.826, respectively, which are superior to those of SDTNBI, which are 0.797 and 0.766, respectively. This study provides an important basis for new drug development and drug repositioning based on biomolecular networks.

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Drug–target interaction prediction through domain-tuned network-based inference

Cited by 155 publications

References 22 publications

Predicting drug-target interaction based on sequence and structure information

Predicting drug-target interaction based on sequence and structure information

COSNet: An R package for label prediction in unbalanced biological networks

SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures

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