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Lamy P.,Aarhus University Hospital | Grove J.,University of Aarhus | Wiuf C.,Bioinformatics Research Center
Human Genomics | Year: 2016

The focus of this review is software for the genotyping of microarray single nucleotide polymorphisms, in particular software for Affymetrix and Illumina arrays. Different statistical principles and ideas have been applied to the construction of genotyping algorithms -- for example, likelihood versus Bayesian modelling, and whether to genotype one or all arrays at a time. The release of new arrays is generally followed by new, or updated, algorithms. © 2011 Henry Stewart Publications. Source

Prince C.R.,Ministry Saint Josephs Hospital | Hines E.J.,Corporate Education | Hines E.J.,Dynamic Clinical Systems, Inc. | Chyou P.-H.,Bioinformatics Research Center | Heegeman D.J.,Marshfield Clinic
Clinical Medicine and Research | Year: 2014

Code teams respond to acute life threatening changes in a patient’s status 24 hours a day, 7 days a week. If any variable, whether a medical skill or non-medical quality, is lacking, the effectiveness of a code team’s resuscitation could be hindered. To improve the overall performance of our hospital’s code team, we implemented an evidence-based quality improvement restructuring plan. The code team restructure, which occurred over a 3-month period, included a defined number of code team participants, clear identification of team members and their primary responsibilities and position relative to the patient, and initiation of team training events and surprise mock codes (simulations). Team member assessments of the restructured code team and its performance were collected through self-administered electronic questionnaires. Time-to-defibrillation, defined as the time the code was called until the start of defibrillation, was measured for each code using actual time recordings from code summary sheets. Significant improvements in team member confidence in the skills specific to their role and clarity in their role’s position were identified. Smaller improvements were seen in team leadership and reduction in the amount of extra talking and noise during a code. The average time-to-defibrillation during real codes decreased each year since the code team restructure. This type of code team restructure resulted in improvements in several areas that impact the functioning of the team, as well as decreased the average time-to-defibrillation, making it beneficial to many, including the team members, medical institution, and patients. © 2014 Marshfield Clinic. Source

Qu L.,Max Planck Institute for Informatics | Ifrim G.,Bioinformatics Research Center | Weikum G.,Max Planck Institute for Informatics
Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference | Year: 2010

The problem addressed in this paper is to predict a user's numeric rating in a product review from the text of the review. Unigram and n-gram representations of text are common choices in opinion mining. However, unigrams cannot capture important expressions like "could have been better", which are essential for prediction models of ratings. N-grams of words, on the other hand, capture such phrases, but typically occur too sparsely in the training set and thus fail to yield robust predictors. This paper overcomes the limitations of these two models, by introducing a novel kind of bag-of-opinions representation, where an opinion, within a review, consists of three components: a root word, a set of modifier words from the same sentence, and one or more negation words. Each opinion is assigned a numeric score which is learned, by ridge regression, from a large, domain-independent corpus of reviews. For the actual test case of a domain-dependent review, the review's rating is predicted by aggregating the scores of all opinions in the review and combining it with a domaindependent unigram model. The paper presents a constrained ridge regression algorithm for learning opinion scores. Experiments show that the bag-of-opinions method outperforms prior state-of-the-art techniques for review rating prediction. Source

Ahn J.-H.,Institute for the BioCentury | Choi S.-J.,Institute for the BioCentury | Han J.-W.,Institute for the BioCentury | Park T.J.,Institute for the BioCentury | And 4 more authors.
Nano Letters | Year: 2010

A silicon nanowire field effect transistor (FET) straddled by the double-gate was demonstrated for biosensor application. The separated double-gates, G1 (primary) and G2 (secondary), allow independent voltage control to modulate channel potential. Therefore, the detection sensitivity was enhanced by the use of G2. By applying weakly positive bias to G2, the sensing window was significantly broadened compared to the case of employing G1 only, which is nominally used in conventional nanowire FET-based biosensors. The charge effect arising from biomolecules was also analyzed. Double-gate nanowire FET can pave the way for an electrically working biosensor without a labeling process. © 2010 American Chemical Society. Source

Ifrim G.,University College Cork | Wiuf C.,Bioinformatics Research Center
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining | Year: 2011

We present a framework for discriminative sequence classification where linear classifiers work directly in the explicit high-dimensional predictor space of all subsequences in the training set (as opposed to kernel-induced spaces). This is made feasible by employing a gradient-bounded coordinatedescent algorithm for efficiently selecting discriminative subsequences without having to expand the whole space. Our framework can be applied to a wide range of loss functions, including binomial log-likelihood loss of logistic regression and squared hinge loss of support vector machines. When applied to protein remote homology detection and remote fold recognition, our framework achieves comparable performance to the state-of-the-art (e.g., kernel support vector machines). In contrast to state-of-the-art sequence classifiers, our models are simply lists of weighted discriminative subsequences and can thus be interpreted and related to the biological problem - a crucial requirement for the bioinformatics and medical communities. Copyright 2011 ACM. Source

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