Identification and ranking of important bio-elements in drug-drug interaction by Market Basket Analysis

Ferdousi, Reza; Jamali, Ali Akbar; Sedaghat, Reza

doi:10.34172/bi.2020.12

Cited by 8 publications

(3 citation statements)

References 43 publications

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“…The relationship between drug-drug can be determined from different resources. Numerous statistical methods have been successfully applied in drug-drug similarity analytics focused on product characteristics [14,15]. Brown et al extend methods to determine significantly co-occurring Drug-Mesh term pairs in the literature database and cluster drugs based on their pair-wise similarities [16].…”

Section: Introductionmentioning

confidence: 99%

Analysis and identification of drug similarity through drug side effects and indications data

Torab-Miandoab

Asghari

Hashemzadeh

et al. 2023

BMC Med Inform Decis Mak

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Background The measurement of drug similarity has many potential applications for assessing drug therapy similarity, patient similarity, and the success of treatment modalities. To date, a family of computational methods has been employed to predict drug-drug similarity. Here, we announce a computational method for measuring drug-drug similarity based on drug indications and side effects. Methods The model was applied for 2997 drugs in the side effects category and 1437 drugs in the indications category. The corresponding binary vectors were built to determine the Drug-drug similarity for each drug. Various similarity measures were conducted to discover drug-drug similarity. Results Among the examined similarity methods, the Jaccard similarity measure was the best in overall performance results. In total, 5,521,272 potential drug pair's similarities were studied in this research. The offered model was able to predict 3,948,378 potential similarities. Conclusion Based on these results, we propose the current method as a robust, simple, and quick approach to identifying drug similarity.

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Section: Introductionmentioning

confidence: 99%

Analysis and identification of drug similarity through drug side effects and indications data

Torab-Miandoab

Asghari

Hashemzadeh

et al. 2023

BMC Med Inform Decis Mak

Self Cite

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“…In previous studies, a self-organization map (SOM) has been applied to tool wear monitoring 44 . Market basket analysis (MBA) has been applied to predict drug-drug interactions 45 . Bayesian optimization has been applied in real and virtual degradable experiments of bioplastics 46 .…”

Section: Introductionmentioning

confidence: 99%

Materials informatics approach using domain modelling for exploring structure–property relationships of polymers

Hara

Yamada

Kurotani

et al. 2022

Sci Rep

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In the development of polymer materials, it is an important issue to explore the complex relationships between domain structure and physical properties. In the domain structure analysis of polymer materials, 1H-static solid-state NMR (ssNMR) spectra can provide information on mobile, rigid, and intermediate domains. But estimation of domain structure from its analysis is difficult due to the wide overlap of spectra from multiple domains. Therefore, we have developed a materials informatics approach that combines the domain modeling (http://dmar.riken.jp/matrigica/) and the integrated analysis of meta-information (the elements, functional groups, additives, and physical properties) in polymer materials. Firstly, the 1H-static ssNMR data of 120 polymer materials were subjected to a short-time Fourier transform to obtain frequency, intensity, and T2 relaxation time for domains with different mobility. The average T2 relaxation time of each domain is 0.96 ms for Mobile, 0.55 ms for Intermediate (Mobile), 0.32 ms for Intermediate (Rigid), and 0.11 ms for Rigid. Secondly, the estimated domain proportions were integrated with meta-information such as elements, functional group and thermophysical properties and was analyzed using a self-organization map and market basket analysis. This proposed method can contribute to explore structure–property relationships of polymer materials with multiple domains.

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“…Peptides and proteins with different structures and various biological impress play an essential role in the human body’s main activities (e.g., hormones, neurotransmitters, modulators) and diagnoses/therapy of the disease [ 1 ], [ 2 ], [ 3 ]. The main part of target proteins and nearly more than half of drug targets are enzymes and G protein-coupled receptors (GPCRs).…”

Section: Introductionmentioning

confidence: 99%

A computational model for GPCR-ligand interaction prediction

Karimi

Ahmadi

Goudarzi

et al. 2020

Journal of Integrative Bioinformatics

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G protein-coupled receptors (GPCRs) play an essential role in critical human activities, and they are considered targets for a wide range of drugs. Accordingly, based on these crucial roles, GPCRs are mainly considered and focused on pharmaceutical research. Hence, there are a lot of investigations on GPCRs. Experimental laboratory research is very costly in terms of time and expenses, and accordingly, there is a marked tendency to use computational methods as an alternative method. In this study, a prediction model based on machine learning (ML) approaches was developed to predict GPCRs and ligand interactions. Decision tree (DT), random forest (RF), multilayer perceptron (MLP), support vector machine (SVM), and Naive Bayes (NB) were the algorithms that were investigated in this study. After several optimization steps, receiver operating characteristic (ROC) for DT, RF, MLP, SVM, and NB algorithm were 95.2, 98.1, 96.3, 95.5, and 97.3, respectively. Accordingly final model was made base on the RF algorithm. The current computational study compared with others focused on specific and important types of proteins (GPCR) interaction and employed/examined different types of sequence-based features to obtain more accurate results. Drug science researchers could widely use the developed prediction model in this study. The developed predictor was applied over 16,132 GPCR-ligand pairs and about 6778 potential interactions predicted.

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Identification and ranking of important bio-elements in drug-drug interaction by Market Basket Analysis

Cited by 8 publications

References 43 publications

Analysis and identification of drug similarity through drug side effects and indications data

Analysis and identification of drug similarity through drug side effects and indications data

Materials informatics approach using domain modelling for exploring structure–property relationships of polymers

A computational model for GPCR-ligand interaction prediction

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