MDIPA: a microRNA–drug interaction prediction approach based on non-negative matrix factorization

2020 ◽  
Vol 36 (20) ◽  
pp. 5061-5067
Author(s):  
Ali Akbar Jamali ◽  
Anthony Kusalik ◽  
Fang-Xiang Wu
Keyword(s):  
Drug Interaction ◽  
Drug Interactions ◽  
Matrix Factorization ◽  
Structural Information ◽  
Superior Performance ◽  
Supplementary Information ◽  
Similarity Matrix ◽  
Interaction Prediction ◽  
Prediction Approach ◽  
Drug Similarity

Abstract Motivation Evidence has shown that microRNAs, one type of small biomolecule, regulate the expression level of genes and play an important role in the development or treatment of diseases. Drugs, as important chemical compounds, can interact with microRNAs and change their functions. The experimental identification of microRNA–drug interactions is time-consuming and expensive. Therefore, it is appealing to develop effective computational approaches for predicting microRNA–drug interactions. Results In this study, a matrix factorization-based method, called the microRNA–drug interaction prediction approach (MDIPA), is proposed for predicting unknown interactions among microRNAs and drugs. Specifically, MDIPA utilizes experimentally validated interactions between drugs and microRNAs, drug similarity and microRNA similarity to predict undiscovered interactions. A path-based microRNA similarity matrix is constructed, while the structural information of drugs is used to establish a drug similarity matrix. To evaluate its performance, our MDIPA is compared with four state-of-the-art prediction methods with an independent dataset and cross-validation. The results of both evaluation methods confirm the superior performance of MDIPA over other methods. Finally, the results of molecular docking in a case study with breast cancer confirm the efficacy of our approach. In conclusion, MDIPA can be effective in predicting potential microRNA–drug interactions. Availability and implementation All code and data are freely available from https://github.com/AliJam82/MDIPA. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Narjes Rohani ◽  
Changiz Eslahchi
Keyword(s):  
Neural Network ◽  
Drug Interaction ◽  
Side Effect ◽  
Network Architecture ◽  
Selection Process ◽  
Superior Performance ◽  
Multiple Drug ◽  
Interaction Prediction ◽  
Benchmark Datasets ◽  
Drug Drug Interaction

Abstract Drug-Drug Interaction (DDI) prediction is one of the most critical issues in drug development and health. Proposing appropriate computational methods for predicting unknown DDI with high precision is challenging. We proposed "NDD: Neural network-based method for drug-drug interaction prediction" for predicting unknown DDIs using various information about drugs. Multiple drug similarities based on drug substructure, target, side effect, off-label side effect, pathway, transporter, and indication data are calculated. At first, NDD uses a heuristic similarity selection process and then integrates the selected similarities with a nonlinear similarity fusion method to achieve high-level features. Afterward, it uses a neural network for interaction prediction. The similarity selection and similarity integration parts of NDD have been proposed in previous studies of other problems. Our novelty is to combine these parts with new neural network architecture and apply these approaches in the context of DDI prediction. We compared NDD with six machine learning classifiers and six state-of-the-art graph-based methods on three benchmark datasets. NDD achieved superior performance in cross-validation with AUPR ranging from 0.830 to 0.947, AUC from 0.954 to 0.994 and F-measure from 0.772 to 0.902. Moreover, cumulative evidence in case studies on numerous drug pairs, further confirm the ability of NDD to predict unknown DDIs. The evaluations corroborate that NDD is an efficient method for predicting unknown DDIs. The data and implementation of NDD are available at https://github.com/nrohani/NDD.

scholarly journals Enhancing Drug-Drug Interaction Prediction Using Deep Attention Neural Networks

2021 ◽  
Author(s):  
Shichao Liu ◽  
Yang Zhang ◽  
Yuxin Cui ◽  
Yang Qiu ◽  
Yifan Deng ◽  
...  
Keyword(s):  
Neural Network ◽  
Drug Interaction ◽  
Drug Interactions ◽  
Data Sources ◽  
Multiple Drug ◽  
Interaction Prediction ◽  
Multiple Data ◽  
Drug Drug Interaction ◽  
Multiple Drugs ◽  
Novel Drug

AbstractDrug-drug interactions are one of the main concerns in drug discovery. Accurate prediction of drug-drug interactions plays a key role in increasing the efficiency of drug research and safety when multiple drugs are c o-prescribed. With various data sources that describe the relationships and properties between drugs, the comprehensive approach that integrates multiple data sources would be considerably effective in making high-accuracy prediction. In this paper, we propose a Deep Attention Neural Network based Drug-Drug Interaction prediction framework, abbreviated as DANN-DDI, to predict unobserved drug-drug interactions. First, we construct multiple drug feature networks and learn drug representations from these networks using the graph embedding method; then, we concatenate the learned drug embeddings and design an attention neural network to learn representations of drug-drug pairs; finally, we adopt a deep neural network to accurately predict drug-drug interactions. The experimental results demonstrate that our model DANN-DDI has improved prediction performance compared with state-of-the-art methods. Moreover, the proposed model can predict novel drug-drug interactions and drug-drug interaction-associated events.

Kajian Interaksi Obat Metformin pada Pasien Diabetes Mellitus

2019 ◽  
Vol 8 (2) ◽  
pp. 55-58
Author(s):  
Havizur Rahman ◽  
Teresia Anggi Octavia
Keyword(s):  
Diabetes Mellitus ◽  
Drug Interaction ◽  
Data Collection ◽  
Drug Interactions ◽  
Degenerative Disease ◽  
Moderate Severity ◽  
Purposive Sampling ◽  
Chronic Degenerative Disease ◽  
Over Time ◽  
Diabetes Melitus

Diabetes melitus merupakan penyakit degeneratif kronis yang apabila tidak ditangani dengan tepat, lama kelamaan bisa timbul berbagai komplikasi, ini cenderung menyebabkan pasien mendapatkan banyak obat dalam satu resep yang dapat menimbulkan interaksi antar obat. Tujuan dari penelitian ini adalah mengetahui persentase terjadinya interaksi obat metformin secara teori serta mengkaji efek yang mungkin timbul dan solusinya. Teknik pengambilan data dengan purpossive sampling, yaitu resep pasien rujuk balik yang menderita diabetes mellitus yang menggunakan metformin. Data yang diperoleh ditemukan bahwa obat yang berinteraksi dengan metformin dengan tingkat keparahan minor ialah sebesar 60%. Kemudian untuk tingkat keparahan moderat ialah sebesar 20%. Sedangkan untuk tingkat keparahan mayor tidak ditemukan. Dari tabel diatas juga dapat diketahui bahwa terdapat 4 obat yang saling berinteraksi dengan metformin, sedangkan untuk obat yang tidak saling berinteraksi dengan metformin terdapat 9 obat. Jumlah obat yang berinteraksi secara teori sebesar 6,85% dan yang tidak berinteraksi 93,15%. Terdapat interaksi obat metformin dengan beberapa obat yaitu furosemid, lisinopril, acarbose dan ramipril.   Kata kunci: interaksi obat, metformin, diabetes mellitus   STUDY OF METFORMIN INTERACTION IN MELLITUS DIABETES PATIENTS   ABSTRACT Mellitus is a chronic degenerative disease which if not handled properly, over time can arise various complications, this tends to cause patients to get many drugs in one recipe that can cause interactions between drugs. The purpose of this study is to determine percentage of metformin drug interactions in theory and examine the effects that may arise and solutions. Data collection techniques using purposive sampling, which is a recipe for reconciliation patients who suffer from diabetes mellitus using metformin. The data obtained it was found that drugs that interact with metformin with minor severity were 60%. Then for moderate severity is 20%. Whereas the major severity was not found. From the table above it can also be seen that there are 4 drugs that interact with metformin, while for drugs that do not interact with metformin there are 9 drugs. The number of drugs that interacted theoretically was 6.85% and 93.15% did not interact. An interaction of the drug metformin with several drugs namely furosemide, lisinopril, acarbose and ramipril.   Keywords: drug interaction, metformin, diabetes mellitus

Pharmacokinetic Drug-drug Interaction of Antibiotics Used in Sepsis Care in China

2020 ◽  
Vol 21 ◽  
Author(s):  
Xuan Yu ◽  
Zixuan Chu ◽  
Jian Li ◽  
Rongrong He ◽  
Yaya Wang ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Drug Interactions ◽  
Penicillin G ◽  
Literature Mining ◽  
Human Pharmacokinetics ◽  
P450 Enzyme ◽  
Drug Drug Interaction ◽  
Pharmacokinetic Drug ◽  
Sepsis Care

Background: Many antibiotics have a high potential for having an interaction with drugs, as perpetrator and/or victim, in critically ill patients, and particularly in sepsis patients. Methods: The aim of this review is to summarize the pharmacokinetic drug-drug interaction (DDI) of 45 antibiotics commonly used in sepsis care in China. Literature mining was conducted to obtain human pharmacokinetics/dispositions of the antibiotics, their interactions with drug metabolizing enzymes or transporters, and their associated clinical drug interactions. Potential DDI is indicated by a DDI index > 0.1 for inhibition or a treated-cell/untreated-cell ratio of enzyme activity being > 2 for induction. Results: The literature-mined information on human pharmacokinetics of the identified antibiotics and their potential drug interactions is summarized. Conclusion: Antibiotic-perpetrated drug interactions, involving P450 enzyme inhibition, have been reported for four lipophilic antibacterials (ciprofloxacin, erythromycin, trimethoprim, and trimethoprim-sulfamethoxazole) and three lipophilic antifungals (fluconazole, itraconazole, and voriconazole). In addition, seven hydrophilic antibacterials (ceftriaxone, cefamandole, piperacillin, penicillin G, amikacin, metronidazole, and linezolid) inhibit drug transporters in vitro. Despite no reported clinical PK drug interactions with the transporters, caution is advised in the use of these antibacterials. Eight hydrophilic antibacterials (all β-lactams; meropenem, cefotaxime, cefazolin, piperacillin, ticarcillin, penicillin G, ampicillin, and flucloxacillin), are potential victims of drug interactions due to transporter inhibition. Rifampin is reported to perpetrate drug interactions by inducing CYP3A or inhibiting OATP1B; it is also reported to be a victim of drug interactions, due to the dual inhibition of CYP3A4 and OATP1B by indinavir. In addition, three antifungals (caspofungin, itraconazole, and voriconazole) are reported to be victims of drug interactions because of P450 enzyme induction. Reports for other antibiotics acting as victims in drug interactions are scarce.

A zero-inflated non-negative matrix factorization for the deconvolution of mixed signals of biological data

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Yixin Kong ◽  
Ariangela Kozik ◽  
Cindy H. Nakatsu ◽  
Yava L. Jones-Hall ◽  
Hyonho Chun
Keyword(s):  
Matrix Factorization ◽  
Factor Model ◽  
R Package ◽  
Biological Data ◽  
Superior Performance ◽  
Sequencing Data ◽  
Fecal Microbiome ◽  
Brain Gene Expression ◽  
Cell Transcriptome ◽  
Non Negative Matrix Factorization

Abstract A latent factor model for count data is popularly applied in deconvoluting mixed signals in biological data as exemplified by sequencing data for transcriptome or microbiome studies. Due to the availability of pure samples such as single-cell transcriptome data, the accuracy of the estimates could be much improved. However, the advantage quickly disappears in the presence of excessive zeros. To correctly account for this phenomenon in both mixed and pure samples, we propose a zero-inflated non-negative matrix factorization and derive an effective multiplicative parameter updating rule. In simulation studies, our method yielded the smallest bias. We applied our approach to brain gene expression as well as fecal microbiome datasets, illustrating the superior performance of the approach. Our method is implemented as a publicly available R-package, iNMF.

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