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Bibliografías temáticas / Drug Side Effect Prediction

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Artículos de revistas

Literatura académica sobre el tema "Drug Side Effect Prediction"

Autor: Grafiati

Publicado: 10 de marzo de 2023

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Artículos de revistas sobre el tema "Drug Side Effect Prediction"

1

Hu, Baofang, Hong Wang, and Zhenmei Yu. "Drug Side-Effect Prediction Via Random Walk on the Signed Heterogeneous Drug Network." Molecules 24, no. 20 (2019): 3668. http://dx.doi.org/10.3390/molecules24203668.

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Drug side-effects have become a major public health concern as they are the underlying cause of over a million serious injuries and deaths each year. Therefore, it is of critical importance to detect side-effects as early as possible. Existing computational methods mainly utilize the drug chemical profile and the drug biological profile to predict the side-effects of a drug. In the utilized drug biological profile information, they only focus on drug–target interactions and neglect the modes of action of drugs on target proteins. In this paper, we develop a new method for predicting potential

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2

Seo, Sukyung, Taekeon Lee, Mi-hyun Kim, and Youngmi Yoon. "Prediction of Side Effects Using Comprehensive Similarity Measures." BioMed Research International 2020 (February 28, 2020): 1–10. http://dx.doi.org/10.1155/2020/1357630.

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Identifying the potential side effects of drugs is crucial in clinical trials in the pharmaceutical industry. The existing side effect prediction methods mainly focus on the chemical and biological properties of drugs. This study proposes a method that uses diverse information such as drug-drug interactions from DrugBank, drug-drug interactions from network, single nucleotide polymorphisms, and side effect anatomical hierarchy as well as chemical structures, indications, and targets. The proposed method is based on the assumption that properties used in drug repositioning studies could be util

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3

Kim, Jinwoo, and Miyoung Shin. "A Knowledge Graph Embedding Approach for Polypharmacy Side Effects Prediction." Applied Sciences 13, no. 5 (2023): 2842. http://dx.doi.org/10.3390/app13052842.

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Predicting the side effects caused by drug combinations may facilitate the prescription of multiple medications in a clinical setting. So far, several prediction models of multidrug side effects based on knowledge graphs have been developed, showing good performance under constrained test conditions. However, these models usually focus on relationships between neighboring nodes of constituent drugs rather than whole nodes, and do not fully exploit the information about the occurrence of single drug side effects. The lack of learning the information on such relationships and single drug data ma

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4

Arshed, Muhammad Asad, Shahzad Mumtaz, Omer Riaz, Waqas Sharif, and Saima Abdullah. "A Deep Learning Framework for Multi Drug Side Effects Prediction with Drug Chemical Substructure." Vol 4 Issue 1 4, no. 1 (2022): 19–31. http://dx.doi.org/10.33411/ijist/2022040102.

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Nowadays, side effects and adverse reactions of drugs are considered the major concern regarding public health. In the process of drug development, it is also considered the main cause of drug failure. Due to the major side effects, drugs are withdrawan from the market immediately. Therefore, in the drug discovery process, the prediction of side effects is a basic need to control the drug development cost and time as well as launching of an effective drug in the market in terms of patient health recovery. In this study, we have proposed a deep learning model named “DLMSE” for the prediction of

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5

Mohd Ali, Yousoff Effendy, Kiam Heong Kwa, and Kurunathan Ratnavelu. "Predicting new drug indications from network analysis." International Journal of Modern Physics C 28, no. 09 (2017): 1750118. http://dx.doi.org/10.1142/s0129183117501182.

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This work adapts centrality measures commonly used in social network analysis to identify drugs with better positions in drug-side effect network and drug-indication network for the purpose of drug repositioning. Our basic hypothesis is that drugs having similar phenotypic profiles such as side effects may also share similar therapeutic properties based on related mechanism of action and vice versa. The networks were constructed from Side Effect Resource (SIDER) 4.1 which contains 1430 unique drugs with side effects and 1437 unique drugs with indications. Within the giant components of these n

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6

Zhao, Xian, Lei Chen, Zi-Han Guo, and Tao Liu. "Predicting Drug Side Effects with Compact Integration of Heterogeneous Networks." Current Bioinformatics 14, no. 8 (2019): 709–20. http://dx.doi.org/10.2174/1574893614666190220114644.

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Background: The side effects of drugs are not only harmful to humans but also the major reasons for withdrawing approved drugs, bringing greater risks for pharmaceutical companies. However, detecting the side effects for a given drug via traditional experiments is time- consuming and expensive. In recent years, several computational methods have been proposed to predict the side effects of drugs. However, most of the methods cannot effectively integrate the heterogeneous properties of drugs. Methods: In this study, we adopted a network embedding method, Mashup, to extract essential and informa

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7

Duffy, Áine, Marie Verbanck, Amanda Dobbyn, et al. "Tissue-specific genetic features inform prediction of drug side effects in clinical trials." Science Advances 6, no. 37 (2020): eabb6242. http://dx.doi.org/10.1126/sciadv.abb6242.

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Adverse side effects often account for the failure of drug clinical trials. We evaluated whether a phenome-wide association study (PheWAS) of 1167 phenotypes in >360,000 U.K. Biobank individuals, in combination with gene expression and expression quantitative trait loci (eQTL) in 48 tissues, can inform prediction of drug side effects in clinical trials. We determined that drug target genes with five genetic features—tissue specificity of gene expression, Mendelian associations, phenotype- and tissue-level effects of genome-wide association (GWA) loci driven by eQTL, and genetic constraint—c

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8

Chen, Lei, Tao Huang, Jian Zhang, et al. "Predicting Drugs Side Effects Based on Chemical-Chemical Interactions and Protein-Chemical Interactions." BioMed Research International 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/485034.

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A drug side effect is an undesirable effect which occurs in addition to the intended therapeutic effect of the drug. The unexpected side effects that many patients suffer from are the major causes of large-scale drug withdrawal. To address the problem, it is highly demanded by pharmaceutical industries to develop computational methods for predicting the side effects of drugs. In this study, a novel computational method was developed to predict the side effects of drug compounds by hybridizing the chemical-chemical and protein-chemical interactions. Compared to most of the previous works, our m

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9

Zhou, Mengshi, Yang Chen, and Rong Xu. "A Drug-Side Effect Context-Sensitive Network approach for drug target prediction." Bioinformatics 35, no. 12 (2018): 2100–2107. http://dx.doi.org/10.1093/bioinformatics/bty906.

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10

Shaked, Itay, Matthew A. Oberhardt, Nir Atias, Roded Sharan, and Eytan Ruppin. "Metabolic Network Prediction of Drug Side Effects." Cell Systems 2, no. 3 (2016): 209–13. http://dx.doi.org/10.1016/j.cels.2016.03.001.

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