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Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

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PubMed2004

HeiDATAProVIT-Heidelberg data archiving, tag assembling, processing and visualization tool.

The demands that have to be met by software tools for biomedical data evaluation strongly differ depending on the background of their application. In clinical routine emphasis is placed on ease of handling and application of standardized procedures, whereas in biomedical research the main focus lies on flexibility and extensibility. These contradictory requirements are reflected by the design principles of existing software solutions: programs for routine application are barely extensible or modifiable by the user and the complexity of highly flexible data processing tools for research purposes hampers the application of new methods to larger data volumes. This gap poses technical difficulties to the transfer of methods from research into clinical routine. The software we present in this paper bridges this discrepancy by incorporating two different levels of application. The lower level offers options to integrate custom written MATLAB((R)) processing routines and to add new evaluation schemes to a pool of existing procedures. The higher level allows for performing standard evaluations by accessing and applying these previously defined procedures. Four basic concepts were introduced to ensure that the program is both maximally flexible on a lower level and readily applicable on a higher level: the tag concept, the concept of modularized visualization, the dummy file concept, and the batchjob concept. These concepts are the key to flexibly assemble and apply the three universal stages of data evaluation: (1) archiving of acquired data, (2) processing the data using signal processing algorithms and (3) visualizing the results in appropriate graphical formats. The present paper illustrates the four concepts within the two levels of the software architecture. The basic functionality and usefulness of the program are demonstrated using an evaluation of gait analysis data as sample application. In summary, this software tool closely integrates a database for biomedical datasets and an extensible pool of evaluation and visualization procedures realized using MATLAB((R)). It is well suited both for data processing in clinical routine and for evaluation of measurement data in any medical research project.

Information Storage and Retrieval↗

PubMed2004

Exact reconstruction in 2D dynamic CT: compensation of time-dependent affine deformations.

This work is dedicated to the reduction of reconstruction artefacts due to motion occurring during the acquisition of computerized tomographic projections. This problem has to be solved when imaging moving organs such as the lungs or the heart. The proposed method belongs to the class of motion compensation algorithms, where the model of motion is included in the reconstruction formula. We address two fundamental questions. First what conditions on the deformation are required for the reconstruction of the object from projections acquired sequentially during the deformation, and second how do we reconstruct the object from those projections. Here we answer these questions in the particular case of 2D general time-dependent affine deformations, assuming the motion parameters are known. We treat the problem of admissibility conditions on the deformation in the parallel-beam and fan-beam cases. Then we propose exact reconstruction methods based on rebinning or sequential FBP formulae for each of these geometries and present reconstructed images obtained with the fan-beam algorithm on simulated data.

Algorithms↗

PubMed2003

Web-based exchange of biochemical information.

In this paper we present a web-based system for the semantic integration of biological data using a database model and an XML exchange format. The prototype developed integrates data related to enzymes.

Databases, Factual↗

PubMed1999

World Wide Web resources for perinatal nursing.

The article provides practical guidelines on how to access and search for information resources on topics related to perinatal nursing on the World Wide Web. Categorized listings of key information sites include major relevant databases and bibliographies, electronic journals, sites relevant to perinatal nursing, and community health resources. The Web provides an excellent information resource for health care professionals and the public to gain practical clinical knowledge and to access research resources. Internet technology has altered the way data are perceived and presented because of the speed with which complex data manipulations are now possible and on account of the vast quantities of data involved.

Databases, Bibliographic↗

PubMed2005

Principal surfaces from unsupervised kernel regression.

We propose a nonparametric approach to learning of principal surfaces based on an unsupervised formulation of the Nadaraya-Watson kernel regression estimator. As compared with previous approaches to principal curves and surfaces, the new method offers several advantages: First, it provides a practical solution to the model selection problem because all parameters can be estimated by leave-one-out cross-validation without additional computational cost. In addition, our approach allows for a convenient incorporation of nonlinear spectral methods for parameter initialization, beyond classical initializations based on linear PCA. Furthermore, it shows a simple way to fit principal surfaces in general feature spaces, beyond the usual data space setup. The experimental results illustrate these convenient features on simulated and real data.

Algorithms↗

PubMed2006

From sample similarity to ensemble similarity: probabilistic distance measures in reproducing kernel Hilbert space.

This paper addresses the problem of characterizing ensemble similarity from sample similarity in a principled manner. Using reproducing kernel as a characterization of sample similarity, we suggest a probabilistic distance measure in the reproducing kernel Hilbert space (RKHS) as the ensemble similarity. Assuming normality in the RKHS, we derive analytic expressions for probabilistic distance measures that are commonly used in many applications, such as Chernoff distance (or the Bhattacharyya distance as its special case), Kullback-Leibler divergence, etc. Since the reproducing kernel implicitly embeds a nonlinear mapping, our approach presents a new way to study these distances whose feasibility and efficiency is demonstrated using experiments with synthetic and real examples. Further, we extend the ensemble similarity to the reproducing kernel for ensemble and study the ensemble similarity for more general data representations.

Algorithms↗

PubMed2004

Should systematic reviews include searches for published errata?

Our objective was to perform a pilot study to estimate the proportion of published errata linked to randomized controlled trials (RCTs) that are worthwhile obtaining when doing a systematic review. medline was searched for records that had both 'randomized-controlled-trial' in the publication type field and 'erratum' in the comments field. One hundred records from four general medical journals were examined independently from two different perspectives. From the information specialist's perspective, 74% of the errata were considered worthwhile obtaining; these were mainly errors in tables or figures. Another 9% described less serious errors, but were worth obtaining if easily available. The other 17% were minor errors. From the perspective of the experienced reviewer/public health consultant, 5% of errata were classified as likely to affect a meta-analysis, and 10% as having significant errors that would affect the interpretation of the RCT, but no effect on a meta-analysis; 85% were not considered important enough to affect either. About 5% of errata to RCTs appeared to matter in terms of changing the final conclusions of a systematic review. However, the majority of errata were considered to be worthwhile obtaining, on the basis that having full and accurate data can reduce confusion and save reviewers time.

Information Storage and Retrieval↗

PubMed2004

A filtered backprojection algorithm for cone beam reconstruction using rotational filtering under helical source trajectory.

With the evolution from multi-detector-row CT to cone beam (CB) volumetric CT, maintaining reconstruction accuracy becomes more challenging. To combat the severe artifacts caused by a large cone angle in CB volumetric CT, three-dimensional reconstruction algorithms have to be utilized. In practice, filtered backprojection (FBP) reconstruction algorithms are more desirable due to their computational structure and image generation efficiency. One of the CB-FBP reconstruction algorithms is the well-known FDK algorithm that was originally derived for a circular x-ray source trajectory by heuristically extending its two-dimensional (2-D) counterpart. Later on, a general CB-FBP reconstruction algorithm was derived for noncircular, such as helical, source trajectories. It has been recognized that a filtering operation in the projection data along the tangential direction of a helical x-ray source trajectory can significantly improve the reconstruction accuracy of helical CB volumetric CT. However, the tangential filtering encounters latitudinal data truncation, resulting in degraded noise characteristics or data manipulation inefficiency. A CB-FBP reconstruction algorithm using one-dimensional rotational filtering across detector rows (namely CB-RFBP) is proposed in this paper. Although the proposed CB-RFBP reconstruction algorithm is approximate, it approaches the reconstruction accuracy that can be achieved by exact helical CB-FBP reconstruction algorithms for moderate cone angles. Unlike most exact CB-FBP reconstruction algorithms in which the redundant data are usually discarded, the proposed CB-RFBP reconstruction algorithm make use of all available projection data, resulting in significantly improved noise characteristics and dose efficiency. Moreover, the rotational filtering across detector rows not only survives the so-called long object problem, but also avoids latitudinal data truncation existing in other helical CB-FBP reconstruction algorithm in which a tangential filtering is carried out, providing better noise characteristics, dose efficiency and data manipulation efficiency.

Algorithms↗

PubMed2006

Algorithms for incorporating prior topological information in HMMs: application to transmembrane proteins.

BACKGROUND: Hidden Markov Models (HMMs) have been extensively used in computational molecular biology, for modelling protein and nucleic acid sequences. In many applications, such as transmembrane protein topology prediction, the incorporation of limited amount of information regarding the topology, arising from biochemical experiments, has been proved a very useful strategy that increased remarkably the performance of even the top-scoring methods. However, no clear and formal explanation of the algorithms that retains the probabilistic interpretation of the models has been presented so far in the literature. RESULTS: We present here, a simple method that allows incorporation of prior topological information concerning the sequences at hand, while at the same time the HMMs retain their full probabilistic interpretation in terms of conditional probabilities. We present modifications to the standard Forward and Backward algorithms of HMMs and we also show explicitly, how reliable predictions may arise by these modifications, using all the algorithms currently available for decoding HMMs. A similar procedure may be used in the training procedure, aiming at optimizing the labels of the HMM's classes, especially in cases such as transmembrane proteins where the labels of the membrane-spanning segments are inherently misplaced. We present an application of this approach developing a method to predict the transmembrane regions of alpha-helical membrane proteins, trained on crystallographically solved data. We show that this method compares well against already established algorithms presented in the literature, and it is extremely useful in practical applications. CONCLUSION: The algorithms presented here, are easily implemented in any kind of a Hidden Markov Model, whereas the prediction method (HMM-TM) is freely available for academic users at http://bioinformatics.biol.uoa.gr/HMM-TM, offering the most advanced decoding options currently available.

Algorithms↗

PubMed2001

A data compression method for image time series.

Image time series for functional studies frequently vary relatively little from one time point to the next. We describe here a method of lossless file compression that calculates the change in signal intensity as a char (one byte), as opposed to a short or long integer (usually two or four bytes), thus resulting in a compressed file size just over half the size of the original (a 2:1 compression ratio). The calculations and format are exceedingly simple; the compression time is comparable to the time required for a file copy, and is thus transparent to most users. The approach works equally well on any integer-based scanning modality, such as PET, CT, or DSA. Our purpose in this communication is to advocate it as a potential standard, to promote free interchange of scan data.

Algorithms↗

PubMed2004