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Register a new userTuning a Multiple Classifier System for Side Effect Discovery using Genetic Algorithms
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Added by
Uwe Aickelin
Publication date
2014
fields
Informatics Engineering
and research's language is
English
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In previous work, a novel supervised framework implementing a binary classifier was presented that obtained excellent results for side effect discovery. Interestingly, unique side effects were identified when different binary classifiers were used within the framework, prompting the investigation of applying a multiple classifier system. In this paper we investigate tuning a side effect multiple classifying system using genetic algorithms. The results of this research show that the novel framework implementing a multiple classifying system trained using genetic algorithms can obtain a higher partial area under the receiver operating characteristic curve than implementing a single classifier. Furthermore, the framework is able to detect side effects efficiently and obtains a low false positive rate.
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Drugs are frequently prescribed to patients with the aim of improving each patients medical state, but an unfortunate consequence of most prescription drugs is the occurrence of undesirable side effects. Side effects that occur in more than one in a thousand patients are likely to be signalled efficiently by current drug surveillance methods, however, these same methods may take decades before generating signals for rarer side effects, risking medical morbidity or mortality in patients prescribed the drug while the rare side effect is undiscovered. In this paper we propose a novel computational meta-analysis framework for signalling rare side effects that integrates existing methods, knowledge from the web, metric learning and semi-supervised clustering. The novel framework was able to signal many known rare and serious side effects for the selection of drugs investigated, such as tendon rupture when prescribed Ciprofloxacin or Levofloxacin, renal failure with Naproxen and depression associated with Rimonabant. Furthermore, for the majority of the drug investigated it generated signals for rare side effects at a more stringent signalling threshold than existing methods and shows the potential to become a fundamental part of post marketing surveillance to detect rare side effects.
The electronic healthcare databases are starting to become more readily available and are thought to have excellent potential for generating adverse drug reaction signals. The Health Improvement Network (THIN) database is an electronic healthcare database containing medical information on over 11 million patients that has excellent potential for detecting ADRs. In this paper we apply four existing electronic healthcare database signal detecting algorithms (MUTARA, HUNT, Temporal Pattern Discovery and modified ROR) on the THIN database for a selection of drugs from six chosen drug families. This is the first comparison of ADR signalling algorithms that includes MUTARA and HUNT and enabled us to set a benchmark for the adverse drug reaction signalling ability of the THIN database. The drugs were selectively chosen to enable a comparison with previous work and for variety. It was found that no algorithm was generally superior and the algorithms natural thresholds act at variable stringencies. Furthermore, none of the algorithms perform well at detecting rare ADRs.
In medical sciences, a biomarker is a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. Molecular experiments are providing rapid and systematic approaches to search for biomarkers, but because single-molecule biomarkers have shown a disappointing lack of robustness for clinical diagnosis, researchers have begun searching for distinctive sets of molecules, called biosignatures. However, the most popular statistics are not appropriate for their identification, and the number of possible biosignatures to be tested is frequently intractable. In the present work, we developed a multivariate filter using genetic algorithms (GA) as a feature (gene) selector to optimize a measure of intra-group cohesion and inter-group dispersion. This method was implemented using Python and R (pyBioSig, available at https://github.com/fredgca/pybiosig under LGPL) and can be manipulated via graphical interface or Python scripts. Using it, we were able to identify putative biosignatures composed by just a few genes and capable of recovering multiple groups simultaneously in a hierarchical clustering, even ones that were not recovered using the whole transcriptome, within a feasible length of time using a personal computer. Our results allowed us to conclude that using GA to optimize our new intra-group cohesion and inter-group dispersion measure is a clear, effective, and computationally feasible strategy for the identification of putative omical biosignatures that could support discrimination among multiple groups simultaneously.
Background: Children are frequently prescribed medication off-label, meaning there has not been sufficient testing of the medication to determine its safety or effectiveness. The main reason this safety knowledge is lacking is due to ethical restrictions that prevent children from being included in the majority of clinical trials. Objective: The objective of this paper is to investigate whether an ensemble of simple study designs can be implemented to signal acutely occurring side effects effectively within the paediatric population by using historical longitudinal data. The majority of pharmacovigilance techniques are unsupervised, but this research presents a supervised framework. Methods: Multiple measures of association are calculated for each drug and medical event pair and these are used as features that are fed into a classiffier to determine the likelihood of the drug and medical event pair corresponding to an adverse drug reaction. The classiffier is trained using known adverse drug reactions or known non-adverse drug reaction relationships. Results: The novel ensemble framework obtained a false positive rate of 0:149, a sensitivity of 0:547 and a specificity of 0:851 when implemented on a reference set of drug and medical event pairs. The novel framework consistently outperformed each individual simple study design. Conclusion: This research shows that it is possible to exploit the mechanism of causality and presents a framework for signalling adverse drug reactions effectively.
Background and Objectives: Substitution-box (s-box) is one of the essential components to create confusion and nonlinear properties in cryptography. To strengthening a cipher against various attacks, including side channel attacks, these boxes need to have numerous security properties. In this paper, a novel method to generate s-boxes is introduced aimed at improving the resistance of s-boxes against side channel attacks. Methods: In the preprocessing phase of this approach, a suitable initial s-box which has some basic security properties is generated by adopting a fast algorithm. Then, in the main stage, using the initial s-box, we generate new s-boxes which not only have the properties of the initial S-box but also have been significantly improved under another set of security properties. To do this, new s-boxes are generated using a genetic algorithm on a particular subset of the linear combination set of coordinate functions of the initial s-box in the preprocessing stage. Results: The performed experiments demonstrate that the values of all security properties of these new s-boxes, especially the measures of transparency order, signal-to-noise ratio, confusion coefficient, bijection property, fixed point, and opposite fixed points, have been substantially improved. For example, our experiments indicate that 70, 220, 2071, 43, and 406 s-boxes are found better than the initial s-box, respectively, in the dimensions of 4x4 through 8x8 Conclusion: In this article, a new s-box construction method is introduced in which the properties related to side channel attacks are improved, without reducing other security properties. Besides, some results obtained from generated s-boxes in the dimensions of 4x4 through 8x8 demonstrated that the generated s-boxes are not only improved relative to the initial s-box, but in some cases, considerably better than some well-known s-boxes.
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