Clustering Based Approach for Isolating the Drug Elements Causing Side Effects
Keywords:
Adverse drug reactions, genome wide association studies, single nucleotide polymorphisms, sedate pathway information, classification labelsAbstract
The truthful identification of drug side effects represents a major concern for public health. Medication symptoms or Adverse Drug responses (ADRs) are a vital and complex challenge. In the pharmaceutical business, ADRs are one of the main causes of failure during the time spent in the development of drugs and of drug withdrawal once a medication has achieved the market. Medication used in prescription depends on a balance between expected advantages and conceivable dangers. Adverse Drug Reactions (ADRs) are impacts that happen when a medication is not administered or controlled at the best possible measurements. It is basic to build up an investigation pipeline to computationally foresee drug side effect symptoms from various assorted sources.
References
- Wei Po Lee, Jhih Yuan Huang, Hsuan Hao Chang, King-The Kee, Chao-Ti Lai, (2017). Predicting drug side effects using data analytics and the integration of multiple data sources. IEEE Access, 5, 20449-20462.
- Dimitri, G. M., & Lió, P. (2017). Drugclust: A machine learning approach for drugs side effects prediction. Computational Biology and Chemistry, 68 , 204–210.
- Niu, Y., & Zhang, W. (2017). Quantitative prediction of drug side effects based on drug-related features. Interdisciplinary Sciences: Computational Life Sciences, 1–11.
- Liang, Z., Huang, J. X., Zeng, X., & Zhang, G. (2016). Dl-adr: a novel deep learning model for classifying genomic variants into adverse drug reactions. BMC medical genomics, 9 (2), 48.
- Zhou, Z.-W., Chen, X.-W., Sneed, K. B., Yang, Y.-X., Zhang, X., He, Z.-X., . . . Zhou, S.-F. (2015). Clinical association between pharmacogenomics and adverse drug reactions. Drugs, 75 (6), 589–631.
- Liu, M., Cai, R., Hu, Y., Matheny, M. E., Sun, J., Hu, J., & Xu, H. (2014). Determining molecular predictors of adverse drug reactions with causality analysis based on structure learning. Journal of the American Medical Informatics Association, 21 (2), 245–251.
- Bresso, E., Grisoni, R., Marchetti, G., Karaboga, A. S., Souchet, M., Devignes, M.-D., & Smaïl-Tabbone, M. (2013). Integrative relational machine-learning for understanding drug side-effect profiles. BMC bioinformatics, 14 (1), 207.
- Scheiber, J., Jenkins, J. L., Sukuru, S. C. K., Bender, A., Mikhailov, D., Milik, M. (2009). Mapping adverse drug reactions in chemical space. Journal of medicinal chemistry, 52 (9), 3103–3107.
- Leone, R., Sottosanti, L., Iorio, M. L., Santuccio, C., Conforti, A., Sabatini. V,Venegoni, M. (2008). Drug-related deaths. Drug Safety, 31 (8), 703–713.
- Hauben, M., Horn, S., & Reich, L. (2007). Potential use of data-mining algorithms for the detection of surprise adverse drug reactions. Drug safety, 30 (2), 143–155
- Dirks AC, van Hyfte DM "Recurrent hyponatremia after substitution of citalopram with duloxetine." J Clin Psychopharmacol 27 (2007): 313
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