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At least 1,351 records · Page 75Linked to original sources
PubMed2005

An attempt at data verification in the EACTS Congenital Database.

OBJECTIVE: The multi-national and multi-institutional collection of data on outcomes in Congenital Heart Surgery (CHS) provides the possibility of analysis of results of treatment and may result in further improvement of the quality of care. The verification of data as far as the completeness and accuracy is necessary to give confidence to all sides--the patients, centers and regulatory authorities. The source data verification (SDV), although difficult, appears possible even in such a large-scale database. METHODS: Out of 5.274 patients and 5.612 procedures data of 2003, collected in the database, 1.703 (32.3%) and 1.895 (33.8%), respectively, have been verified at five sites on following fields: IPPV time, date of birth, date of admission, date of surgery, date of discharge/mortality, body weight, case category, CPB time, AoX time, Circulatory arrest time. SDV was performed at five sites by two database officers using the sources of information different to the local copy of the database (patients' files, operation notes, perfusion charts, OR Books). Verification was performed between June 1st and July 31st 2004. Statistical analysis was performed using R-project software, ver. 2.0.0. and Welch's t-test for comparison of continuous variables. P-value >0.05 was used as statistically significant difference between groups. RESULTS: Pre- and post-verification mortalities in all groups showed no significant differences although seven deaths out of 68 (10.27%) were missed. None of the other verified fields showed significant differences after verification. CONCLUSIONS: Source Data Verification showed no statistically significant differences between verified and non-verified data on 30 days mortality, LOS, age, body weight, CPB time, AoX and Circulatory arrest time. IPPV time was not available in 58.6% procedures.

Data Collection↗
PubMed2016

Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking.

The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry (MS) techniques are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Molecular Networking (GNPS; http://gnps.ucsd.edu), an open-access knowledge base for community-wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of 'living data' through continuous reanalysis of deposited data.

Biological Products↗
PubMed1996

Digital imaging of cephalometric radiography, Part 1: Advantages and limitations of digital imaging.

Digital imaging has several potential advantages over traditional cephalometry. These include storage, transmission, and enhancement of images, reduced exposure to radiation for patients, and the possibility of automated cephalometric analysis. This paper outlines some of these advantages, discusses the limitations of digital imaging and suggests possible future developments.

Cephalometry↗
PubMed2005

Can Cochrane Reviews in controversial areas be biased? A sensitivity analysis based on the protocol of a Systematic Cochrane Review on low-level laser therapy in osteoarthritis.

OBJECTIVE: The aim of this study was to test if a conclusion in a systematic review of low-level laser therapy (LLLT) for osteoarthritis from the Cochrane Library was valid and robust. BACKGROUND DATA: Health policy decisions often rely on conclusions from the Cochrane Database of Systematic Reviews for approval of new therapies, although their validity for controversial non-pharmacological treatment has been questioned. METHODS: Validity was tested against a nine-item checklist for systematic reviews. Review selections were analyzed for possible discrepancies between trial and review reports, and omissions of relevant trials and data. Alternative data from discrepancies and omissions were then imputed in a sensitivity analysis, to test if review conclusions were robust. RESULTS: Only clinicians who had performed LLLT trials with negative results were invited into the review group. Review quality was sound in areas of literature search and methodological assessments, and some of the limitations were mentioned. The statistical analysis held 18 questionable selections such as omissions of trials, data, and subgroup analyses. These selections systematically favored the negative review conclusion. Without altering the review protocol, the sensitivity analysis of combined results changed to significantly positive for continuous and categorical data when data from all included trials were combined. Further sensitivity analyses with inclusion of valid non-included trials, performance of missing follow-up, and subgroup analyses revealed consistent and highly significant results in favor of active LLLT. CONCLUSIONS: In this example, the Cochrane review conclusion was neither robust nor valid. Representation of experts and different views on efficacy in the review group and extensive use of sensitivity analyses could probably improve quality control of reviews in areas of controversy.

Bias↗
PubMed2001

Exploiting big biology: integrating large-scale biological data for function inference.

The amount of data produced by molecular biologists is growing at an exponential rate. Some of the fastest growing sets of data are measurements of gene expression, comparable in quantity only to gene sequences and the vast biological literature. Both gene expression data and sequence data offer hints as to the functions of thousands of newly discovered genes, but neither give complete answers. Therefore, much effort is being focused on integrating these large data sets and combining them with all available functional data to draw inferences about the functions of uncharacterised genes. This review discusses the most pertinent functional data for genome-wide functional inference and describes several methods by which these disparate data types are being integrated.

Data Collection↗
PubMed2006

Enrichment analysis in high-throughput genomics - accounting for dependency in the NULL.

Translating the overwhelming amount of data generated in high-throughput genomics experiments into biologically meaningful evidence, which may for example point to a series of biomarkers or hint at a relevant pathway, is a matter of great interest in bioinformatics these days. Genes showing similar experimental profiles, it is hypothesized, share biological mechanisms that if understood could provide clues to the molecular processes leading to pathological events. It is the topic of further study to learn if or how a priori information about the known genes may serve to explain coexpression. One popular method of knowledge discovery in high-throughput genomics experiments, enrichment analysis (EA), seeks to infer if an interesting collection of genes is 'enriched' for a Consortium particular set of a priori Gene Ontology Consortium (GO) classes. For the purposes of statistical testing, the conventional methods offered in EA software implicitly assume independence between the GO classes. Genes may be annotated for more than one biological classification, and therefore the resulting test statistics of enrichment between GO classes can be highly dependent if the overlapping gene sets are relatively large. There is a need to formally determine if conventional EA results are robust to the independence assumption. We derive the exact null distribution for testing enrichment of GO classes by relaxing the independence assumption using well-known statistical theory. In applications with publicly available data sets, our test results are similar to the conventional approach which assumes independence. We argue that the independence assumption is not detrimental.

Data Interpretation, Statistical↗
PubMed2005

Self-organizing and self-correcting classifications of biological data.

MOTIVATION: Rapid, automated means of organizing biological data are required if we hope to keep abreast of the flood of data emanating from sequencing, microarray and similar high-throughput analyses. Faced with the need to validate the annotation of thousands of sequences and to generate biologically meaningful classifications based on the sequence data, we turned to statistical methods in order to automate these processes. RESULTS: An algorithm for automated classification based on evolutionary distance data was written in S. The algorithm was tested on a dataset of 1436 small subunit ribosomal RNA sequences and was able to classify the sequences according to an extant scheme, use statistical measurements of group membership to detect sequences that were misclassified within this scheme and produce a new classification. In this study, the use of the algorithm to address problems in prokaryotic taxonomy is discussed. AVAILABILITY: S-Plus is available from Insightful, Inc. An S-Plus implementation of the algorithm and the associated data are available at http://taxoweb.mmg.msu.edu/datasets

Algorithms↗
PubMed2006

Detecting potential labeling errors in microarrays by data perturbation.

MOTIVATION: Classification is widely used in medical applications. However, the quality of the classifier depends critically on the accurate labeling of the training data. But for many medical applications, labeling a sample or grading a biopsy can be subjective. Existing studies confirm this phenomenon and show that even a very small number of mislabeled samples could deeply degrade the performance of the obtained classifier, particularly when the sample size is small. The problem we address in this paper is to develop a method for automatically detecting samples that are possibly mislabeled. RESULTS: We propose two algorithms, a classification-stability algorithm and a leave-one-out-error-sensitivity algorithm for detecting possibly mislabeled samples. For both algorithms, the key structure is the computation of the leave-one-out perturbation matrix. The classification-stability algorithm is based on measuring the stability of the label of a sample with respect to label changes of other samples and the version of this algorithm based on the support vector machine appears to be quite accurate for three real datasets. The suspect list produced by the version is of high quality. Furthermore, when human intervention is not available, the correction heuristic appears to be beneficial.

Artifacts↗
PubMed2003

The MetaFMF website: a high quality tool for meta-analysis of FMF.

We present here the MetaFMF database (freely accessible at http://fmf.igh.cnrs.fr/metaFMF/index_us.html) that attempts to gather and unify, in a common resource, data on phenotype-genotype correlation in familial Mediterranean fever (FMF). A single accession form, including a large number of quality controls, has been implemented such that data, collected worldwide, are included in an homogeneous manner. The inclusion criterion has the objective to avoid interpretational bias: patients will be included only if they bear at least two mutations. The clinical form has been set up by an International editorial board (12 FMF expert centres), which guarantees the validity of the data. Data are anonymous and submitted by a secure interface, in which the researcher is logged in with a specific ID and password. A pilot study on 211 patients has shown the feasibility and relevance of this project. We anticipate that the use of MetaFMF will enable reliable assessment of phenotype-genotype correlations in FMF, and define a set of severe versus mild mutations/genotypes. It should also highlight reasons for previous inconsistencies in such correlations.

Data Collection↗
PubMed2003

Data assimilation in the decision support system rodos.

Model predictions for a rapid assessment and prognosis of possible radiological consequences after an accidental release of radionuclides play an important role in nuclear emergency management. Radiological observations, e.g. dose rate measurements, can be used to improve such model predictions. The process of combining model predictions and observations, usually referred to as data assimilation, is described in this article within the framework of the real time on-line decision support system (RODOS) for off-site nuclear emergency management in Europe. Data assimilation capabilities, based on Kalman filters, are under development for several modules of the RODOS system, including the atmospheric dispersion, deposition, food chain and hydrological models. The use of such a generic data assimilation methodology enables the propagation of uncertainties throughout the various modules of the system. This would in turn provide decision makers with uncertainty estimates taking into account both model and observation errors. This paper describes the methodology employed as well as results of some preliminary studies based on simulated data.

Algorithms↗
PubMedDate not supplied

Data mining as a tool for research and knowledge development in nursing.

The ability to collect and store data has grown at a dramatic rate in all disciplines over the past two decades. Healthcare has been no exception. The shift toward evidence-based practice and outcomes research presents significant opportunities and challenges to extract meaningful information from massive amounts of clinical data to transform it into the best available knowledge to guide nursing practice. Data mining, a step in the process of Knowledge Discovery in Databases, is a method of unearthing information from large data sets. Built upon statistical analysis, artificial intelligence, and machine learning technologies, data mining can analyze massive amounts of data and provide useful and interesting information about patterns and relationships that exist within the data that might otherwise be missed. As domain experts, nurse researchers are in ideal positions to use this proven technology to transform the information that is available in existing data repositories into useful and understandable knowledge to guide nursing practice and for active interdisciplinary collaboration and research.

Algorithms↗
PubMedDate not supplied

Retrieval failure versus memory loss in experimental amnesia: definitions and processes.

For at least 40 years, there has been a recurring argument concerning the nature of experimental amnesia, with one side arguing that amnesic treatments interfere with the formation of enduring memories and the other side arguing that these treatments interfere with the expression of memories that were effectively encoded. The argument appears to stem from a combination of (1) unclear definitions and (2) real differences in the theoretical vantages that underlie the interpretation of relevant data. Here we speak to how the field might avoid arguments that are definitional in nature and how various hypotheses fare in light of published data. Existing but often overlooked data favor very rapid (milliseconds) synaptic consolidation, with experimental amnesia reflecting, at least in part, deficits in retrieval rather than in the initial storage of information.

Amnesia↗
PubMed2004

Super-resolution reconstruction of compressed video using transform-domain statistics.

Considerable attention has been directed to the problem of producing high-resolution video and still images from multiple low-resolution images. This multiframe reconstruction, also known as super-resolution reconstruction, is beginning to be applied to compressed video. Super-resolution techniques that have been designed for raw (i.e., uncompressed) video may not be effective when applied to compressed video because they do not incorporate the compression process into their models. The compression process introduces quantization error, which is the dominant source of error in some cases. In this paper, we propose a stochastic framework where quantization information as well as other statistical information about additive noise and image prior can be utilized effectively.

Algorithms↗
PubMed2004

Automatic detection and recognition of signs from natural scenes.

In this paper, we present an approach to automatic detection and recognition of signs from natural scenes, and its application to a sign translation task. The proposed approach embeds multiresolution and multiscale edge detection, adaptive searching, color analysis, and affine rectification in a hierarchical framework for sign detection, with different emphases at each phase to handle the text in different sizes, orientations, color distributions and backgrounds. We use affine rectification to recover deformation of the text regions caused by an inappropriate camera view angle. The procedure can significantly improve text detection rate and optical character recognition (OCR) accuracy. Instead of using binary information for OCR, we extract features from an intensity image directly. We propose a local intensity normalization method to effectively handle lighting variations, followed by a Gabor transform to obtain local features, and finally a linear discriminant analysis (LDA) method for feature selection. We have applied the approach in developing a Chinese sign translation system, which can automatically detect and recognize Chinese signs as input from a camera, and translate the recognized text into English.

Algorithms↗