scholarly journals Synteny Approach of Drug Target Prediction among Unique Hypothetical Proteins of Streptococcus Gordonii Causing Infective Endocarditis

2014 ◽  
Vol 2 (4) ◽  
pp. 34
Author(s):  
S Telkar ◽  
HSS Kumar ◽  
R Mahmood
Keyword(s):  
Infective Endocarditis ◽  
Drug Target ◽  
Target Prediction ◽  
Hypothetical Proteins ◽  
Streptococcus Gordonii ◽  
Drug Target Prediction

Drug–target prediction utilizing heterogeneous bio-linked network embeddings

2019 ◽  
Author(s):  
Nansu Zong ◽  
Rachael Sze Nga Wong ◽  
Yue Yu ◽  
Andrew Wen ◽  
Ming Huang ◽  
...  
Keyword(s):  
Drug Target ◽  
Target Prediction ◽  
Machine Learning Algorithms ◽  
Association Mining ◽  
Drug Target Prediction ◽  
Specific Prediction ◽  
Series Of Experiments ◽  
Inference Methods ◽  
Novel Drug ◽  
Prediction Strategy

Abstract To enable modularization for network-based prediction, we conducted a review of known methods conducting the various subtasks corresponding to the creation of a drug–target prediction framework and associated benchmarking to determine the highest-performing approaches. Accordingly, our contributions are as follows: (i) from a network perspective, we benchmarked the association-mining performance of 32 distinct subnetwork permutations, arranging based on a comprehensive heterogeneous biomedical network derived from 12 repositories; (ii) from a methodological perspective, we identified the best prediction strategy based on a review of combinations of the components with off-the-shelf classification, inference methods and graph embedding methods. Our benchmarking strategy consisted of two series of experiments, totaling six distinct tasks from the two perspectives, to determine the best prediction. We demonstrated that the proposed method outperformed the existing network-based methods as well as how combinatorial networks and methodologies can influence the prediction. In addition, we conducted disease-specific prediction tasks for 20 distinct diseases and showed the reliability of the strategy in predicting 75 novel drug–target associations as shown by a validation utilizing DrugBank 5.1.0. In particular, we revealed a connection of the network topology with the biological explanations for predicting the diseases, ‘Asthma’ ‘Hypertension’, and ‘Dementia’. The results of our benchmarking produced knowledge on a network-based prediction framework with the modularization of the feature selection and association prediction, which can be easily adapted and extended to other feature sources or machine learning algorithms as well as a performed baseline to comprehensively evaluate the utility of incorporating varying data sources.

scholarly journals Drug Target Prediction Based on the Herbs Components: The Study on the Multitargets Pharmacological Mechanism of Qishenkeli Acting on the Coronary Heart Disease

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Yong Wang ◽  
Zhongyang Liu ◽  
Chun Li ◽  
Dong Li ◽  
Yulin Ouyang ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Angiotensin Ii ◽  
Drug Target ◽  
Drug Targets ◽  
Target Prediction ◽  
Coronary Artery Ligation ◽  
Potential Drug ◽  
Drug Target Prediction ◽  
Potential Drug Targets

In this paper, we present a case study of Qishenkeli (QSKL) to research TCM’s underlying molecular mechanism, based on drug target prediction and analyses of TCM chemical components and following experimental validation. First, after determining the compositive compounds of QSKL, we use drugCIPHER-CS to predict their potential drug targets. These potential targets are significantly enriched with known cardiovascular disease-related drug targets. Then we find these potential drug targets are significantly enriched in the biological processes of neuroactive ligand-receptor interaction, aminoacyl-tRNA biosynthesis, calcium signaling pathway, glycine, serine and threonine metabolism, and renin-angiotensin system (RAAS), and so on. Then, animal model of coronary heart disease (CHD) induced by left anterior descending coronary artery ligation is applied to validate predicted pathway. RAAS pathway is selected as an example, and the results show that QSKL has effect on both rennin and angiotensin II receptor (AT1R), which eventually down regulates the angiotensin II (AngII). Bioinformatics combing with experiment verification can provide a credible and objective method to understand the complicated multitargets mechanism for Chinese herbal formula.

Biochemical network-based drug-target prediction

2010 ◽  
Vol 21 (4) ◽  
pp. 511-516 ◽  
Author(s):  
Edda Klipp ◽  
Rebecca C Wade ◽  
Ursula Kummer
Keyword(s):  
Drug Target ◽  
Target Prediction ◽  
Biochemical Network ◽  
Drug Target Prediction

scholarly journals Industry-scale application and evaluation of deep learning for drug target prediction

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Noé Sturm ◽  
Andreas Mayr ◽  
Thanh Le Van ◽  
Vladimir Chupakhin ◽  
Hugo Ceulemans ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Target ◽  
Target Prediction ◽  
Drug Target Prediction

scholarly journals Drug target prediction using adverse event report systems: a pharmacogenomic approach

2012 ◽  
Vol 28 (18) ◽  
pp. i611-i618 ◽  
Author(s):  
M. Takarabe ◽  
M. Kotera ◽  
Y. Nishimura ◽  
S. Goto ◽  
Y. Yamanishi
Keyword(s):  
Adverse Event ◽  
Drug Target ◽  
Target Prediction ◽  
Adverse Event Report ◽  
Event Report ◽  
Drug Target Prediction

scholarly journals Interpretable Drug Target Predictions using Self-Expressiveness

2021 ◽  
Author(s):  
Diego Galeano ◽  
Santiago Noto ◽  
Ruben Jimenez ◽  
Alberto Paccanaro
Keyword(s):  
Drug Target ◽  
Drug Targets ◽  
Prediction Models ◽  
Matrix Completion ◽  
Closed Form Solution ◽  
Target Prediction ◽  
Form Solution ◽  
Protein Targets ◽  
Drug Target Prediction ◽  
Similarity Matrices

AbstractThe identification of missing drug targets is critical for the development of treatments and for the molecular elucidation of drug side effects. Drug targets have been predicted by exploiting molecular, biological or pharmacological features of drugs and protein targets. Yet, developing integrative and interpretable machine learning models for predicting drug targets remains a challenging task. We present Inception, an integrative and interpretable matrix completion model for predicting drug targets. Inception is a self-expressive model that learns two similarity matrices: one for drugs and another for protein targets. These learned similarity matrices are key for our models’ interpretability: they can explain how a predicted drug-target interaction can be explain in terms of a linear combination of chemical, biological and pharmacological similarities. We develop a novel objective function with efficient closed-form solution. To demonstrate the ability of Inception at recovering missing drug-target interactions (DTIs), we perform cross-validation experiments with stringent controls of data imbalance, chemical similarities between drugs and sequence similarities between targets. We also assess the performance of our model using a simulated prospective approach. Having trained our model with DTIs from a snapshot 2011 of the DrugBank database, we test whether we could predict DTIs from a 2020 snapshot of DrugBank. Inception outperforms two state-of-the-art drug target prediction models in all the scenarios. This suggests that Inception could be useful for predicting missing drug target interactions while providing interpretable predictions.

scholarly journals Scalable and Accurate Drug–target Prediction Based on Heterogeneous Bio-linked Network Mining

2019 ◽  
Author(s):  
Nansu Zong ◽  
Rachael Sze Nga Wong ◽  
Victoria Ngo ◽  
Yue Yu ◽  
Ning Li
Keyword(s):  
Drug Target ◽  
Sample Selection ◽  
Feature Learning ◽  
Prediction Method ◽  
Target Prediction ◽  
Accurate Solution ◽  
Classification Model ◽  
Learning Method ◽  
Drug Target Prediction ◽  
Novel Drug

AbstractMotivationDespite the existing classification- and inference-based machine learning methods that show promising results in drug-target prediction, these methods possess inevitable limitations, where: 1) results are often biased as it lacks negative samples in the classification-based methods, and 2) novel drug-target associations with new (or isolated) drugs/targets cannot be explored by inference-based methods. As big data continues to boom, there is a need to study a scalable, robust, and accurate solution that can process large heterogeneous datasets and yield valuable predictions.ResultsWe introduce a drug-target prediction method that improved our previously proposed method from the three aspects: 1) we constructed a heterogeneous network which incorporates 12 repositories and includes 7 types of biomedical entities (#20,119 entities, # 194,296 associations), 2) we enhanced the feature learning method with Node2Vec, a scalable state-of-art feature learning method, 3) we integrate the originally proposed inference-based model with a classification model, which is further fine-tuned by a negative sample selection algorithm. The proposed method shows a better result for drug–target association prediction: 95.3% AUC ROC score compared to the existing methods in the 10-fold cross-validation tests. We studied the biased learning/testing in the network-based pairwise prediction, and conclude a best training strategy. Finally, we conducted a disease specific prediction task based on 20 diseases. New drug-target associations were successfully predicted with AUC ROC in average, 97.2% (validated based on the DrugBank 5.1.0). The experiments showed the reliability of the proposed method in predicting novel drug-target associations for the disease treatment.

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