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DDR: efficient computational method to predict drug–target interactions using graph mining and machine learning approaches

Rawan S Olayan, Haitham Ashoor, Vladimir B Bajic
2018 Bioinformatics  
., 2018) , we performed 96 computational experiments, including six that are related to the COSINE method (Lim et al., 2016) .  ...  The repeated analysis confirms that the original qualitative conclusions regarding the newly introduced DDR method stands unaltered.  ...  Acknowledgment We are grateful to Aleksandar Poleksic and Lei Xie, the authors of the COSINE method, for bringing to our attention the potential problem with the reported COSINE results on the DrugBank_FDA  ... 
doi:10.1093/bioinformatics/bty417 pmid:29917050 fatcat:3hnn6d3juvfwlkz4nn3bttzbla

DDR: efficient computational method to predict drug–target interactions using graph mining and machine learning approaches

Rawan S Olayan, Haitham Ashoor, Vladimir B Bajic, Jonathan Wren
2017 Bioinformatics  
error relative to the next best start-of-the-art method for predicting DTIs by 31% when the drugs are new, by 23% when targets are new and by 34% when the drugs and the targets are known but not all DTIs  ...  Using independent sources of evidence, we verify as correct 22 out of the top 25 DDR novel predictions. This suggests that DDR can be used as an efficient method to identify correct DTIs.  ...  Funding Research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST) Base Research Funds to VBB (BAS/1/1606-01-01) and by KAUST Office of Sponsored  ... 
doi:10.1093/bioinformatics/btx731 pmid:29186331 pmcid:PMC5998943 fatcat:4ibnmou6wfdavmxhaqktsst7jm

DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques

Maha A. Thafar, Rawan S. Olayan, Haitham Ashoor, Somayah Albaradei, Vladimir B. Bajic, Xin Gao, Takashi Gojobori, Magbubah Essack
2020 Journal of Cheminformatics  
DTiGEMS+ combines different computational techniques to provide the final drug target prediction, these techniques include graph embeddings, graph mining, and machine learning.  ...  Here we introduce DTiGEMS+, a computational method that predicts Drug-Target interactions using Graph Embedding, graph Mining, and Similarity-based techniques.  ...  Acknowledgements The research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST). 1  ... 
doi:10.1186/s13321-020-00447-2 pmid:33431036 fatcat:ueicotpg3vdvjeb4yzgs24j4eq

DTI-CDF: a CDF model towards the prediction of DTIs based on hybrid features [article]

Yanyi Chu, Yufang Zhang, Wei Wang, Xiaoqi Shan, Xiangeng Wang, Yi Xiong, Dongqing Wei
2019 bioRxiv   pre-print
Drug-target interactions play a crucial role in target-based drug discovery and exploitation.  ...  Computational prediction of DTIs has become a popular alternative strategy to the experimental methods for identification of DTIs of which are both time and resource consuming.  ...  [32] exploited a novel method called DDR to solve the above problem and improve the prediction accuracy which executes graph mining technique firstly to acquire the comprehensive feature vectors and  ... 
doi:10.1101/657973 fatcat:xjbejaosifgctpqymor6q2ssmq

DTi2Vec: Drug–target interaction prediction using network embedding and ensemble learning

Maha A. Thafar, Rawan S. Olayan, Somayah Albaradei, Vladimir B. Bajic, Takashi Gojobori, Magbubah Essack, Xin Gao
2021 Journal of Cheminformatics  
Using machine learning (ML) models for this task, specifically network-based approaches, is effective and has shown great advantages over the other computational methods.  ...  DTi2Vec demonstrated its ability in drugtarget link prediction compared to several state-of-the-art network-based methods, using four benchmark datasets and large-scale data compiled from DrugBank.  ...  The chemogenomic-based approaches incorporate DTI prediction models based on network-, machine learning (ML)-, and deep learning (DL)-based methods.  ... 
doi:10.1186/s13321-021-00552-w pmid:34551818 fatcat:xjqvs4b6fbbcjbsznktkwajho4

A two-tiered unsupervised clustering approach for drug repositioning through heterogeneous data integration

Pathima Nusrath Hameed, Karin Verspoor, Snezana Kusljic, Saman Halgamuge
2018 BMC Bioinformatics  
Moreover, the two existing approaches failed to produce useful cluster separations when using graph clustering algorithms while our approach is able to identify useful clusters for drug repositioning.  ...  Furthermore, we provide clinical evidence for four predicted results (Chlorthalidone, Indomethacin, Metformin and Thioridazine) to support that our proposed approach can be reliably used to infer ATC code  ...  Computational methods like network based inferencing [1, 5, 6, 8, 10] , machine learning [2, 11, 12] , and text mining approaches [13, 14] are widely used for drug repositioning.  ... 
doi:10.1186/s12859-018-2123-4 pmid:29642848 pmcid:PMC5896044 fatcat:qxkewmokzzd3vlbbsj6uxeal3a

Novel prediction methods for virtual drug screening [article]

Josip Mesarić
2022 arXiv   pre-print
One of key parts of the early drug discovery process has become virtual drug screening -- a method used to narrow down search for potential drugs by running computer simulations of drug-target interactions  ...  As these methods are known to demand huge amounts of computational power to get accurate results, prediction models based on machine learning techniques became a popular solution requiring less computational  ...  Another example is the computational method DDR [56] where authors use a heterogeneous graph containing known DTIs with multiple similarities between drugs and between target proteins.  ... 
arXiv:2202.06635v1 fatcat:cab5pvnvw5httnuksmb4ke2piy

Novel drug-target interactions via link prediction and network embedding

E. Amiri Souri, R. Laddach, S. N. Karagiannis, L. G. Papageorgiou, S. Tsoka
2022 BMC Bioinformatics  
Background As many interactions between the chemical and genomic space remain undiscovered, computational methods able to identify potential drug-target interactions (DTIs) are employed to accelerate drug  ...  It maps drug-drug and protein–protein similarity networks to low-dimensional features and the DTI prediction is formulated as binary classification based on a strategy of concatenating the drug and target  ...  Acknowledgements We thank two anonymous reviewers for helpful comments and suggestions.  ... 
doi:10.1186/s12859-022-04650-w pmid:35379165 pmcid:PMC8978405 fatcat:glk7nxpwsjhfhev222vksoc4sa

Predicting drug-target interactions using multi-label learning with community detection method (DTI-MLCD) [article]

Yanyi Chu, Yi Xiong, Dong-Qing Wei, Xiaoqi Shan
2020 bioRxiv   pre-print
To reduce heavily experiment cost, booming machine learning has been applied to this field and developed many computational methods, especially binary classification methods.  ...  Identifying drug-target interactions (DTIs) is an important step for drug discovery and drug repositioning.  ...  DDR executes the graph mining technique firstly to acquire the comprehensive feature vectors and then applies the random forest model by using different graph-based features extracted from the drug-target  ... 
doi:10.1101/2020.05.11.087734 fatcat:pps7c62if5fshhyxhy4thwkxie

Harnessing Synthetic Lethal Interactions for Personalized Medicine

Grace S. Shieh
2022 Journal of Personalized Medicine  
Topics are (1) exploiting the SL concept for drug combinations to circumvent tumor resistance, (2) using synthetic lethality to identify prognostic and predictive biomarkers, (3) applying SL interactions  ...  to stratify patients for targeted and immunotherapy, and (4) discussions on challenges and future directions.  ...  Acknowledgments: The author is grateful to the editor and reviewers for constructive comments.  ... 
doi:10.3390/jpm12010098 pmid:35055413 pmcid:PMC8779047 fatcat:iraha3ibojcizkljixj63yhs2a

Utilising Graph Machine Learning within Drug Discovery and Development [article]

Thomas Gaudelet, Ben Day, Arian R. Jamasb, Jyothish Soman, Cristian Regep, Gertrude Liu, Jeremy B. R. Hayter, Richard Vickers, Charles Roberts, Jian Tang, David Roblin, Tom L. Blundell (+2 others)
2021 arXiv   pre-print
After introducing key terms and modelling approaches, we move chronologically through the drug development pipeline to identify and summarise work incorporating: target identification, design of small  ...  Graph Machine Learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional relationships between  ...  Bozhilova, and Andrew Anighoro.  ... 
arXiv:2012.05716v2 fatcat:kre2kx3x4ff43mmuh7khrxmmzy

Optimizing Area Under the Curve Measures via Matrix Factorization for Predicting Drug-Target Interaction with Multiple Similarities [article]

Bin Liu, Grigorios Tsoumakas
2022 arXiv   pre-print
Computational methods efficiently predict DTIs and recommend a small part of potential interacting pairs for further experimental confirmation, accelerating the drug discovery process.  ...  Both three proposed approaches incorporate a novel local interaction consistency aware similarity interaction method to generate fused drug and target similarities that preserve vital information from  ...  The prevalent chemogenomic computational methods usually rely on machine learning techniques, such as matrix factorization (MF) [5] , kernel machines [6] , network mining [7] , and deep learning [8  ... 
arXiv:2105.01545v2 fatcat:ja2fcyiyajc2bgj46bridbhuw4

Utilizing graph machine learning within drug discovery and development

Thomas Gaudelet, Ben Day, Arian R Jamasb, Jyothish Soman, Cristian Regep, Gertrude Liu, Jeremy B R Hayter, Richard Vickers, Charles Roberts, Jian Tang, David Roblin, Tom L Blundell (+2 others)
2021 Briefings in Bioinformatics  
After introducing key terms and modelling approaches, we move chronologically through the drug development pipeline to identify and summarize work incorporating: target identification, design of small  ...  Graph machine learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional relationships between  ...  Bozhilova and Andrew Anighoro.  ... 
doi:10.1093/bib/bbab159 pmid:34013350 pmcid:PMC8574649 fatcat:qli5weqbsbhlvhhjjmgze4sjou

DTI-HeNE: a novel method for drug-target interaction prediction based on heterogeneous network embedding

Yang Yue, Shan He
2021 BMC Bioinformatics  
A recent trend is to use graph-based machine learning, e.g., graph embedding to extract features from drug-target networks and then predict new drug-target interactions.  ...  In recent studies, many machine-learning-based methods have been proposed to discover unknown interactions between drugs and protein targets.  ...  Acknowledgements We are grateful to the anonymous reviewers for their constructive comments on the original manuscript.  ... 
doi:10.1186/s12859-021-04327-w pmid:34479477 fatcat:37rpum4bifaalax4qtmcr4i6si

Integrative methods for analyzing big data in precision medicine

Vladimir Gligorijević, Noël Malod-Dognin, Nataša Pržulj
2016 Proteomics  
Fluxomics refers to a range of methods in experimental and computational biology that attempt to identify, or predict the rates of metabolic reactions in biological systems 56 .  ...  Drug repurposing is not only about identifying new targets for known drugs; preclinical evaluations also include predicting therapeutic regimens (i.e., dose and frequency) and safety of the treatment (  ...  Innovation (CDI) OIA-1028394, the ARRS project J1-5454, and the Serbian Ministry of Education and Science Project III44006.  ... 
doi:10.1002/pmic.201500396 pmid:26677817 fatcat:rwqiuxxgmffrppkz2ccj7ffm5m
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