A network embedding framework based on integrating multiplex network for drug combination prediction

2021 ◽  
Author(s):  
Liang Yu ◽  
Mingfei Xia ◽  
Qi An
Keyword(s):  
Drug Combination ◽  
Characteristic Curve ◽  
Drug Combinations ◽  
Alternative Methods ◽  
Network Embedding ◽  
Disease Treatment ◽  
Multiplex Network ◽  
External Sources ◽  
Precision Recall Curve ◽  
Novel Drug

Abstract Drug combination is a sensible strategy for disease treatment because it improves the treatment efficacy and reduces concomitant side effects. Due to the large number of possible combinations among candidate compounds, exhaustive screening is prohibitive. Currently, a large number of studies have focused on predicting potential drug combinations. However, these methods are not entirely satisfactory in terms of performance and scalability. In this paper, we proposed a Network Embedding frameWork in MultIplex Network (NEWMIN) to predict synthetic drug combinations. Based on a multiplex drug similarity network, we offered alternative methods to integrate useful information from different aspects and to decide the quantitative importance of each network. For drug combination prediction, we found seven novel drug combinations that have been validated by external sources among the top-ranked predictions of our model. To verify the feasibility of NEWMIN, we compared NEWMIN with other five methods, for which it showed better performance than other methods in terms of the area under the precision-recall curve and receiver operating characteristic curve.

Prediction of Drug Combinations with a Network Embedding Method

2019 ◽  
Vol 21 (10) ◽  
pp. 789-797 ◽  
Author(s):  
Tianyun Wang ◽  
Lei Chen ◽  
Xian Zhao
Keyword(s):  
Computational Methods ◽  
Drug Combination ◽  
Chemical Interaction ◽  
Drug Combinations ◽  
Computational Techniques ◽  
Svm Classifier ◽  
Network Embedding ◽  
Embedding Method ◽  
Topological Features ◽  
Individual Features

Aim and Objective: There are several diseases having a complicated mechanism. For such complicated diseases, a single drug cannot treat them very well because these diseases always involve several targets and single targeted drugs cannot modulate these targets simultaneously. Drug combination is an effective way to treat such diseases. However, determination of effective drug combinations is time- and cost-consuming via traditional methods. It is urgent to build quick and cheap methods in this regard. Designing effective computational methods incorporating advanced computational techniques to predict drug combinations is an alternative and feasible way. Method: In this study, we proposed a novel network embedding method, which can extract topological features of each drug combination from a drug network that was constructed using chemical-chemical interaction information retrieved from STITCH. These topological features were combined with individual features of drug combination reported in one previous study. Several advanced computational methods were employed to construct an effective prediction model, such as synthetic minority oversampling technique (SMOTE) that was used to tackle imbalanced dataset, minimum redundancy maximum relevance (mRMR) and incremental feature selection (IFS) methods that were adopted to analyze features and extract optimal features for building an optimal support machine vector (SVM) classifier. Results and Conclusion: The constructed optimal SVM classifier yielded an MCC of 0.806, which is superior to the classifier only using individual features with or without SMOTE. The performance of the classifier can be improved by combining the topological features and essential features of a drug combination.

scholarly journals Predicting Tumor Cell Response to Synergistic Drug Combinations Using a Novel Simplified Deep Learning Model

2020 ◽  
Author(s):  
Heming Zhang ◽  
Jiarui Feng ◽  
Amanda Zeng ◽  
Philip Payne ◽  
Fuhai Li
Keyword(s):  
Deep Learning ◽  
Signaling Pathways ◽  
Drug Combination ◽  
Computational Models ◽  
Learning Model ◽  
Drug Combinations ◽  
Small Set ◽  
Novel Drug ◽  
Deep Learning Model ◽  
Synergistic Drug Combinations

AbstractDrug combinations targeting multiple targets/pathways are believed to be able to reduce drug resistance. Computational models are essential for novel drug combination discovery. In this study, we proposed a new simplified deep learning model, DeepSignalingSynergy, for drug combination prediction. Compared with existing models that use a large number of chemical-structure and genomics features in densely connected layers, we built the model on a small set of cancer signaling pathways, which can mimic the integration of multi-omics data and drug target/mechanism in a more biological meaningful and explainable manner. The evaluation results of the model using the NCI ALMANAC drug combination screening data indicated the feasibility of drug combination prediction using a small set of signaling pathways. Interestingly, the model analysis suggested the importance of heterogeneity of the 46 signaling pathways, which indicates that some new signaling pathways should be targeted to discover novel synergistic drug combinations.

scholarly journals Machine learning methods, databases and tools for drug combination prediction

2021 ◽  
Author(s):  
Lianlian Wu ◽  
Yuqi Wen ◽  
Dongjin Leng ◽  
Qinglong Zhang ◽  
Chong Dai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Combination ◽  
Predictive Performance ◽  
Search Space ◽  
Drug Combinations ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
High Throughput Screens ◽  
Novel Drug ◽  
Future Work

Abstract Combination therapy has shown an obvious efficacy on complex diseases and can greatly reduce the development of drug resistance. However, even with high-throughput screens, experimental methods are insufficient to explore novel drug combinations. In order to reduce the search space of drug combinations, there is an urgent need to develop more efficient computational methods to predict novel drug combinations. In recent decades, more and more machine learning (ML) algorithms have been applied to improve the predictive performance. The object of this study is to introduce and discuss the recent applications of ML methods and the widely used databases in drug combination prediction. In this study, we first describe the concept and controversy of synergism between drug combinations. Then, we investigate various publicly available data resources and tools for prediction tasks. Next, ML methods including classic ML and deep learning methods applied in drug combination prediction are introduced. Finally, we summarize the challenges to ML methods in prediction tasks and provide a discussion on future work.

scholarly journals Prevalence of Antimicrobial Resistant Bacteria from Conjunctival Flora in an Eye Infection Prone Breed (Saint Bernard)

Molecules ◽  
2021 ◽  
Vol 26 (8) ◽  
pp. 2219
Author(s):  
George Cosmin Nadăș ◽  
Cristiana Ștefania Novac ◽  
Ioana Adriana Matei ◽  
Cosmina Maria Bouari ◽  
Zoltan Miklos Gal ◽  
...  
Keyword(s):  
Multidrug Resistant ◽  
Ocular Disease ◽  
Resistant Bacteria ◽  
Human Populations ◽  
Disease Treatment ◽  
Pseudomonas Spp ◽  
History Of ◽  
External Sources ◽  
Staphylococcus Spp ◽  
Saint Bernard

The conjunctival bacterial resident and opportunistic flora of dogs may represent a major source of dissemination of pathogens throughout the environment or to other animals and humans. Nevertheless, contamination with bacteria from external sources is common. In this context, the study of the antimicrobial resistance (AMR) pattern may represent an indicator of multidrug resistant (MDR) strains exchange. The present study was focused on a single predisposed breed—Saint Bernard. The evaluated animals were healthy, but about half had a history of ocular disease/treatment. The swabs collected from conjunctival sacs were evaluated by conventional microbiological cultivation and antimicrobial susceptibility testing (AST). The most prevalent Gram-positive was Staphylococcus spp.; regardless of the history, while Gram-negative was Pseudomonas spp.; exclusively from dogs with a history of ocular disease/treatment. Other identified genera were represented by Bacillus, Streptococcus, Trueperella, Aeromonas and Neisseria. The obtained results suggest a possible association between the presence of mixed flora and a history of ocular disease/treatment. A high AMR was generally observed (90%) in all isolates, especially for kanamycin, doxycycline, chloramphenicol and penicillin. MDR was recorded in Staphylococcus spp. and Pseudomonas spp. This result together with a well-known zoonotic potential may suggest an exchange of these strains within animal human populations and the environment.

scholarly journals MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Léo Pio-Lopez ◽  
Alberto Valdeolivas ◽  
Laurent Tichit ◽  
Élisabeth Remy ◽  
Anaïs Baudot
Keyword(s):  
Heterogeneous Networks ◽  
Heterogeneous Network ◽  
Link Prediction ◽  
Network Embedding ◽  
Multiplex Networks ◽  
Multiplex Network ◽  
Gene Associations ◽  
Different Types ◽  
Embedding Methods ◽  
Node Embeddings

AbstractNetwork embedding approaches are gaining momentum to analyse a large variety of networks. Indeed, these approaches have demonstrated their effectiveness in tasks such as community detection, node classification, and link prediction. However, very few network embedding methods have been specifically designed to handle multiplex networks, i.e. networks composed of different layers sharing the same set of nodes but having different types of edges. Moreover, to our knowledge, existing approaches cannot embed multiple nodes from multiplex-heterogeneous networks, i.e. networks composed of several multiplex networks containing both different types of nodes and edges. In this study, we propose MultiVERSE, an extension of the VERSE framework using Random Walks with Restart on Multiplex (RWR-M) and Multiplex-Heterogeneous (RWR-MH) networks. MultiVERSE is a fast and scalable method to learn node embeddings from multiplex and multiplex-heterogeneous networks. We evaluate MultiVERSE on several biological and social networks and demonstrate its performance. MultiVERSE indeed outperforms most of the other methods in the tasks of link prediction and network reconstruction for multiplex network embedding, and is also efficient in link prediction for multiplex-heterogeneous network embedding. Finally, we apply MultiVERSE to study rare disease-gene associations using link prediction and clustering. MultiVERSE is freely available on github at https://github.com/Lpiol/MultiVERSE.

scholarly journals Biomolecular Network-Based Synergistic Drug Combination Discovery

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Xiangyi Li ◽  
Guangrong Qin ◽  
Qingmin Yang ◽  
Lanming Chen ◽  
Lu Xie
Keyword(s):  
Drug Combination ◽  
Complex Disease ◽  
Recent Decade ◽  
Drug Combinations ◽  
Network Medicine ◽  
Disease Therapy ◽  
Biomolecular Network ◽  
Disease Related Genes ◽  
Disease Specific ◽  
Synergistic Drug Combinations

Drug combination is a powerful and promising approach for complex disease therapy such as cancer and cardiovascular disease. However, the number of synergistic drug combinations approved by the Food and Drug Administration is very small. To bridge the gap between urgent need and low yield, researchers have constructed various models to identify synergistic drug combinations. Among these models, biomolecular network-based model is outstanding because of its ability to reflect and illustrate the relationships among drugs, disease-related genes, therapeutic targets, and disease-specific signaling pathways as a system. In this review, we analyzed and classified models for synergistic drug combination prediction in recent decade according to their respective algorithms. Besides, we collected useful resources including databases and analysis tools for synergistic drug combination prediction. It should provide a quick resource for computational biologists who work with network medicine or synergistic drug combination designing.

scholarly journals Using Yeast Synthetic Lethality To Inform Drug Combination for Malaria

2018 ◽  
Vol 62 (4) ◽  
Author(s):  
Suvitha Subramaniam ◽  
Christoph D. Schmid ◽  
Xue Li Guan ◽  
Pascal Mäser
Keyword(s):  
Plasmodium Falciparum ◽  
Drug Combination ◽  
Synthetic Lethality ◽  
Drug Combinations ◽  
Synthetic Lethal Interaction ◽  
Content Type ◽  
Synthetic Lethal ◽  
Gene Pairs ◽  
Phosphatidylinositol 4

ABSTRACT Combinatorial chemotherapy is necessary for the treatment of malaria. However, finding a suitable partner drug for a new candidate is challenging. Here we develop an algorithm that identifies all of the gene pairs of Plasmodium falciparum that possess orthologues in yeast that have a synthetic lethal interaction but are absent in humans. This suggests new options for drug combinations, particularly for inhibitors of targets such as P. falciparum calcineurin, cation ATPase 4, or phosphatidylinositol 4-kinase.

scholarly journals Seq-SymRF: a random forest model predicts potential miRNA-disease associations based on information of sequences and clinical symptoms

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jinlong Li ◽  
Xingyu Chen ◽  
Qixing Huang ◽  
Yang Wang ◽  
Yun Xie ◽  
...  
Keyword(s):  
Random Forest ◽  
Breast Neoplasms ◽  
Clinical Symptoms ◽  
Characteristic Curve ◽  
Vital Role ◽  
Sequence Information ◽  
Source Codes ◽  
Drug Identification ◽  
Disease Associations ◽  
Precision Recall Curve

Abstract Increasing evidence indicates that miRNAs play a vital role in biological processes and are closely related to various human diseases. Research on miRNA-disease associations is helpful not only for disease prevention, diagnosis and treatment, but also for new drug identification and lead compound discovery. A novel sequence- and symptom-based random forest algorithm model (Seq-SymRF) was developed to identify potential associations between miRNA and disease. Features derived from sequence information and clinical symptoms were utilized to characterize miRNA and disease, respectively. Moreover, the clustering method by calculating the Euclidean distance was adopted to construct reliable negative samples. Based on the fivefold cross-validation, Seq-SymRF achieved the accuracy of 98.00%, specificity of 99.43%, sensitivity of 96.58%, precision of 99.40% and Matthews correlation coefficient of 0.9604, respectively. The areas under the receiver operating characteristic curve and precision recall curve were 0.9967 and 0.9975, respectively. Additionally, case studies were implemented with leukemia, breast neoplasms and hsa-mir-21. Most of the top-25 predicted disease-related miRNAs (19/25 for leukemia; 20/25 for breast neoplasms) and 15 of top-25 predicted miRNA-related diseases were verified by literature and dbDEMC database. It is anticipated that Seq-SymRF could be regarded as a powerful high-throughput virtual screening tool for drug research and development. All source codes can be downloaded from https://github.com/LeeKamlong/Seq-SymRF.

Pharmacokinetics and Pharmacodynamics of Amoxicillin-Sulbactam, a Novel Aminopenicillin–β-Lactamase Inhibitor Combination, against Escherichia coli

1999 ◽  
Vol 43 (6) ◽  
pp. 1503-1504 ◽  
Author(s):  
Carlos Bantar ◽  
Federico Nicola ◽  
Hector J. Arenoso ◽  
Marcelo Galas ◽  
Liliana Soria ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Oral Dose ◽  
Single Oral Dose ◽  
Drug Combination ◽  
Pharmacokinetics And Pharmacodynamics ◽  
E Coli ◽  
Inhibitory Activities ◽  
Novel Drug ◽  
Inhibitor Combination ◽  
Lactamase Inhibitor

ABSTRACT We evaluated the pharmacokinetics of amoxicillin-sulbactam (AMX-SUL), a novel drug combination, and its pharmacodynamics againstEscherichia coli in 12 volunteers receiving a single oral dose (1,000 mg). Peak serum bactericidal and urine inhibitory activities in most volunteers were observed against E. colistrains for which AMX-SUL MICs were low (2- to 4-mg/liter) (2 strains) and high (≥16-mg/liter) (47 strains), respectively.

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