Predicting Drug-Drug Interactions Through Large-Scale Similarity-Based Link Prediction

2016 ◽  
pp. 774-789 ◽  
Author(s):  
Achille Fokoue ◽  
Mohammad Sadoghi ◽  
Oktie Hassanzadeh ◽  
Ping Zhang
Keyword(s):  
Drug Interactions ◽  
Link Prediction ◽  
Large Scale ◽  
Scale Similarity

scholarly journals An information theoretic approach to link prediction in multiplex networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Seyed Hossein Jafari ◽  
Amir Mahdi Abdolhosseini-Qomi ◽  
Masoud Asadpour ◽  
Maseud Rahgozar ◽  
Naser Yazdani
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Large Scale ◽  
Similarity Measures ◽  
Prediction Method ◽  
General Purpose ◽  
Fast Method ◽  
Theoretic Approach ◽  
Multiplex Networks ◽  
Wide Range

AbstractThe entities of real-world networks are connected via different types of connections (i.e., layers). The task of link prediction in multiplex networks is about finding missing connections based on both intra-layer and inter-layer correlations. Our observations confirm that in a wide range of real-world multiplex networks, from social to biological and technological, a positive correlation exists between connection probability in one layer and similarity in other layers. Accordingly, a similarity-based automatic general-purpose multiplex link prediction method—SimBins—is devised that quantifies the amount of connection uncertainty based on observed inter-layer correlations in a multiplex network. Moreover, SimBins enhances the prediction quality in the target layer by incorporating the effect of link overlap across layers. Applying SimBins to various datasets from diverse domains, our findings indicate that SimBins outperforms the compared methods (both baseline and state-of-the-art methods) in most instances when predicting links. Furthermore, it is discussed that SimBins imposes minor computational overhead to the base similarity measures making it a potentially fast method, suitable for large-scale multiplex networks.

scholarly journals Pharmacokinetic and safety study of co-administration of albendazole, diethylcarbamazine, Ivermectin and azithromycin for the integrated treatment of Neglected Tropical Diseases

2020 ◽  
Author(s):  
Lucy N John ◽  
Catherine Bjerum ◽  
Pere Millat Martinez ◽  
Rhoda Likia ◽  
Linda Silus ◽  
...  
Keyword(s):  
Drug Interactions ◽  
Large Scale ◽  
Neglected Tropical Diseases ◽  
Pharmacokinetic Interaction ◽  
Integrated Treatment ◽  
Secondary Outcome ◽  
Pharmacokinetic Data ◽  
Tropical Diseases ◽  
Open Label ◽  
Significant Difference

Abstract Background Pharmacokinetic data are a pre-requisite to integrated implementation of large-scale mass drug administration (MDA) for neglected tropical diseases (NTDs). We investigated the safety and drug interactions of a combination of azithromycin (AZI) targeting yaws and trachoma, with the newly approved ivermectin, albendazole, diethylcarbamazine (IDA) regime for Lymphatic Filariasis. Methodology An open-label, randomized, 3-arm pharmacokinetic interaction study in adult volunteers was carried out in Lihir Island, Papua New Guinea. Healthy adult participants were recruited and randomized to (I) IDA alone, (II) IDA combined with AZI, (III) AZI alone. The primary outcome was lack of a clinically relevant drug interaction. The secondary outcome was the overall difference in the proportion of AEs between treatment arms. Results Thirty-seven participants, eighteen men and nineteen women, were randomized and completed the study. There were no significant drug-drug interactions between the study arms. The GMR of Cmax, AUC0–t, and AUC0–∞ for IVM, DEC, ALB-SOX, and AZI were within the range of 80–125% (GMR for AUC0–∞ for IVM, 87.9; DEC, 92.9; ALB-SOX, 100.0; and AZI, 100.1). There was no significant difference in the frequency of AEs across study arms (AZI and IDA alone arms 9/12 (75%), co-administration arm 12/13 (92%); p = 0.44). All AEs were grade 1 and self-limiting. Conclusions Co-administration of AZI with IDA did not show evidence of significant drug-interactions. There were no serious AEs in any of the study arms. Our data support further evaluation of the safety of integrated MDA for NTDs. Clinical Trials Registration. NCT03664063

scholarly journals OpenBioLink: a benchmarking framework for large-scale biomedical link prediction

2020 ◽  
Vol 36 (13) ◽  
pp. 4097-4098 ◽  
Author(s):  
Anna Breit ◽  
Simon Ott ◽  
Asan Agibetov ◽  
Matthias Samwald
Keyword(s):  
Link Prediction ◽  
Large Scale ◽  
Source Code ◽  
Machine Learning Algorithms ◽  
Knowledge Networks ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Biomedical Knowledge ◽  
High Quality ◽  
Baseline Evaluation

Abstract Summary Recently, novel machine-learning algorithms have shown potential for predicting undiscovered links in biomedical knowledge networks. However, dedicated benchmarks for measuring algorithmic progress have not yet emerged. With OpenBioLink, we introduce a large-scale, high-quality and highly challenging biomedical link prediction benchmark to transparently and reproducibly evaluate such algorithms. Furthermore, we present preliminary baseline evaluation results. Availability and implementation Source code and data are openly available at https://github.com/OpenBioLink/OpenBioLink. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A Modified Skip-Gram Algorithm for Extracting Drug-Drug Interactions from AERS Reports

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Li Wang ◽  
Wenjie Pan ◽  
QingHua Wang ◽  
Heming Bai ◽  
Wei Liu ◽  
...  
Keyword(s):  
Adverse Event ◽  
Drug Interactions ◽  
Large Scale ◽  
Characteristic Curve ◽  
Language Model ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Concept Representation ◽  
Average Accuracy ◽  
Tenfold Cross Validation

Drug-drug interactions (DDIs) are one of the indispensable factors leading to adverse event reactions. Considering the unique structure of AERS (Food and Drug Administration Adverse Event Reporting System (FDA AERS)) reports, we changed the scope of the window value in the original skip-gram algorithm, then propose a language concept representation model and extract features of drug name and reaction information from large-scale AERS reports. The validation of our scheme was tested and verified by comparing with vectors originated from the cooccurrence matrix in tenfold cross-validation. In the verification of description enrichment of the DrugBank DDI database, accuracy was calculated for measurement. The average area under the receiver operating characteristic curve of logistic regression classifiers based on the proposed language model is 6% higher than that of the cooccurrence matrix. At the same time, the average accuracy in five severe adverse event classes is 88%. These results indicate that our language model can be useful for extracting drug and reaction features from large-scale AERS reports.

scholarly journals Nonparametric link prediction in large scale dynamic networks

2014 ◽  
Vol 8 (2) ◽  
pp. 2022-2065 ◽  
Author(s):  
Purnamrita Sarkar ◽  
Deepayan Chakrabarti ◽  
Michael Jordan
Keyword(s):  
Link Prediction ◽  
Large Scale ◽  
Dynamic Networks

scholarly journals ToxDBScan: Large-Scale Similarity Screening of Toxicological Databases for Drug Candidates

2014 ◽  
Vol 15 (10) ◽  
pp. 19037-19055 ◽  
Author(s):  
Michael Römer ◽  
Linus Backert ◽  
Johannes Eichner ◽  
Andreas Zell
Keyword(s):  
Large Scale ◽  
Drug Candidates ◽  
Scale Similarity

scholarly journals Large-Scale Identification and Analysis of Suppressive Drug Interactions

2014 ◽  
Vol 21 (4) ◽  
pp. 541-551 ◽  
Author(s):  
Murat Cokol ◽  
Zohar B. Weinstein ◽  
Kaan Yilancioglu ◽  
Murat Tasan ◽  
Allison Doak ◽  
...  
Keyword(s):  
Drug Interactions ◽  
Large Scale
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