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Experimenting with new paradigms for medical education and the emergence of a distance learning degree using the internet: teaching evidence-based medicine.

This paper is focused on designing and developing a teaching environment associated with an introductory course in evidence-based medicine designed for undergraduates. Attempting to break away from the traditional educational model based on acquisition of factual knowledge, we developed a software tool centred on content management, student assessment and feedback. We ran this course on an educational website, using chat rooms and electronic mail for trainer-trainee communication over one academic year. The website served as a repository for knowledge and information and presented freestanding web-based interactive study modules and a non-interactive degree course. Trainers and trainees responded favourably to the computer applications and the Internet. This project demonstrated the feasibility of computer-aided learning and the advantages of distance teaching over the Internet.

Computer-Assisted Instruction↗

A multipurpose phantom for use with electronic portal imaging devices.

A simple, low-cost, multipurpose phantom has been designed for use with electronic portal imaging devices (EPIDS). Making use of the high spatial resolution of an EPID, together with the built in software tools for measuring distances, it is possible to verify x-ray/light field size and congruence, at any gantry angle, for fields up to 200 mm x 200 mm. Being of an accurate construction, it can help analyse the distance measuring capabilities of an EPID, ensuring that they are accurate and remain so with time. It can also be used to quantify, and monitor, image displacement and rotation with gantry angle.

Equipment Design↗

Using voxel-dependent importance factors for interactive DVH-based dose optimization.

Intensity modulated radiation therapy (IMRT) inverse planning is usually performed by pre-selecting parameters such as beam modality, beam configuration and importance factors and then optimizing the fluence profiles or beamlet weights. In reality, the IMRT dose optimization problem may be ill-conditioned and there may not be a physical solution to account for the chosen parameters and constraints. Planner intervention is often required to conduct a multiple trial-and-error process where several parameters are sequentially varied until an acceptable compromise is achieved. The resulting solution reflects a balance between the conflicting requirements of the target and the sensitive structures. A major problem of the conventional inverse planning formalism is that there exists no effective mechanism for a planner to fine-tune the dose distribution on a local level or to differentially modify the dose-volume histograms (DVHs) of the involved structures. In this paper we introduce a new inverse planning scheme with voxel-dependent importance factors and demonstrate that it provides us with an effective link between the system parameters and the dosimetric behaviour at a local level. The planning proceeds in two steps. After a conventional trial-and-error inverse planning procedure is completed, we identify the dose interval at which the fractional volume on the DVH curve needs to be changed. The voxels that receive dose in the selected range are then located and their voxel-dependent importance factors are adjusted accordingly. The fine-tuning of the DVHs is iterative in nature and, using widely available computer graphic software tools, the process can be made graphically interactive. The new IMRT planning scheme is applied to two test cases and the results indicate that our control over the differential shapes of the DVHs of the involved structures is,greatly enhanced. Thus the technique may have significant practical implications in facilitating the IMRT treatment planning process.

Algorithms↗

On dose distribution comparison.

In radiotherapy practice, one often needs to compare two dose distributions. Especially with the wide clinical implementation of intensity-modulated radiation therapy, software tools for quantitative dose (or fluence) distribution comparison are required for patient-specific quality assurance. Dose distribution comparison is not a trivial task since it has to be performed in both dose and spatial domains in order to be clinically relevant. Each of the existing comparison methods has its own strengths and weaknesses and there is room for improvement. In this work, we developed a general framework for comparing dose distributions. Using a new concept called maximum allowed dose difference (MADD), the comparison in both dose and spatial domains can be performed entirely in the dose domain. Formulae for calculating MADD values for various comparison methods, such as composite analysis and gamma index, have been derived. For convenience in clinical practice, a new measure called normalized dose difference (NDD) has also been proposed, which is the dose difference at a point scaled by the ratio of MADD to the predetermined dose acceptance tolerance. Unlike the simple dose difference test, NDD works in both low and high dose gradient regions because it considers both dose and spatial acceptance tolerances through MADD. The new method has been applied to a test case and a clinical example. It was found that the new method combines the merits of the existing methods (accurate, simple, clinically intuitive and insensitive to dose grid size) and can easily be implemented into any dose/intensity comparison tool.

Algorithms↗

Pathways database system.

During the next phase of the Human Genome Project, research will focus on functional studies of attributing functions to genes, their regulatory elements, and other DNA sequences. To facilitate the use of genomic information in such studies, a new modeling perspective is needed to examine and study genome sequences in the context of many kinds of biological information. Pathways are the logical format for modeling and presenting such information in a manner that is familiar to biological researchers. In this paper, we introduce an integrated system, called "Pathways Database System," with a set of software tools for modeling, storing, analyzing, visualizing, and querying biological pathways data at different levels of genetic, molecular, biochemical and organismal detail.

Computational Biology↗

Nanopore cheminformatics.

A cheminformatics method is described for classification, and biophysical examination, of individual molecules. A novel molecular detector is used--one based on current blockade measurements through a nanometer-scale ion channel (alpha-hemolysin). Classification results are described for blockades caused by DNA molecules in the alpha-hemolysin nanopore detector, with signal analysis and pattern recognition performed using a combination of methods from bioinformatics and machine learning. Due to the size of the alpha-hemolysin protein channel, the blockade events report on one DNA molecule at a time, which enables a variety of reproducible, single-molecule biophysical experiments. To capture the full sensitivity of the nanopore detector's blockade signal, Hidden Markov Models (HMMs) were used with Expectation/Maximization for denoising and for associating a feature vector with the ionic current blockade of each captured DNA molecule. Support Vector Machines (SVMs) that employ novel kernel designs were then used as discriminators. With SVM training performed off-line, and economical HMM processing on-line, blockade classification was possible during capture. HMMs were also used in conjunction with a time-domain finite state automaton (off-line) for feature discovery and kinetics analysis. Analysis of the DNA data indicates a variety of binding (DNA-protein), fraying, and conformational shifts that are consistent with data obtained from thermodynamic analyses (melting curves), X-ray crystallography, and NMR studies. The software tools are designed for analysis of generic blockades in ionic channels, including those in other biological pore-forming toxins, other biological channels in general, and semiconductor-based channels.

Base Sequence↗

Quality assessment of Affymetrix GeneChip data.

Affymetrix GeneChips are one of the best established microarray platforms. This powerful technique allows users to measure the expression of thousands of genes simultaneously. However, a microarray experiment is a sophisticated and time consuming endeavor with many potential sources of unwanted variation that could compromise the results if left uncontrolled. Increasing data volume and data complexity have triggered growing concern and awareness of the importance of assessing the quality of generated microarray data. In this review, we give an overview of current methods and software tools for quality assessment of Affymetrix GeneChip data. We focus on quality metrics, diagnostic plots, probe-level methods, pseudo-images, and classification methods to identify corrupted chips. We also describe RNA quality assessment methods which play an important role in challenging RNA sources like formalin embedded biopsies, laser-micro dissected samples, or single cells. No wet-lab methods are discussed in this paper.

Animals↗

Normalization and quantification of differential expression in gene expression microarrays.

Array-based gene expression studies frequently serve to identify genes that are expressed differently under two or more conditions. The actual analysis of the data, however, may be hampered by a number of technical and statistical problems. Possible remedies on the level of computational analysis lie in appropriate preprocessing steps, proper normalization of the data and application of statistical testing procedures in the derivation of differentially expressed genes. This review summarizes methods that are available for these purposes and provides a brief overview of the available software tools.

Algorithms↗

BioWAP, mobile Internet service for bioinformatics.

UNLABELLED: We have developed a new Internet service, which provides mobile access to bioinformatics databases and software tools. The BioWAP service facilitates access to basic bioinformatics databases and analysis tools from everywhere without a PC or a laptop computer. Both open source bioinformatics program suites and Internet services, which are not designed for mobile Internet access, were utilized in the BioWAP service. AVAILABILITY: The BioWAP service starting page can be browsed with any WAP terminal from http://bioinf.uta.fi/wml/welcome.wml.

Computational Biology↗

Evaluation of computational metabolic-pathway predictions for Helicobacter pylori.

MOTIVATION: We seek to determine the accuracy of computational methods for predicting metabolic pathways in sequenced genomes, and to understand the contributions of both the prediction algorithms, and the reference pathway databases used by those algorithms, to the prediction accuracy. RESULTS: The comparisons we performed were as follows. (1) We compared two predictions of the pathway complements of Helicobacter pylori that were computed by an early version of our pathway-prediction algorithm: prediction A used the EcoCyc E. coli pathway DB as the reference database (DB) for prediction, and prediction B used the MetaCyc pathway DB (a superset of EcoCyc) as the reference pathway DB. The MetaCyc-based prediction contained 75% more pathway predictions, but we believe a significant number of those predictions were false positives. (2) We compared two predictions of the pathway complement of H. pylori that used MetaCyc as the reference pathway DB, but that used different algorithms: the original PathoLogic algorithm, and an enhanced version of the algorithm designed to eliminate false-positive pathway predictions. The improved algorithm predicted 30\% fewer metabolic pathways than the original algorithm; all of the eliminated pathways are believed to be false-positive predictions. (3) We compared the 98 pathways predicted by the enhanced algorithm with the results of a manual analysis of the pathways of H. pylori. Results: 40 of the computationally predicted pathways were consistent with the manual analysis, 13 pathways are considered false-positive predictions, and four pathways had partially overlapping topologies. Twenty-six predicted pathways were not mentioned in the manual analysis; we believe these are correct predictions by PathoLogic that were not found by the manual analysis. Five pathways from the manual analysis were not found computationally. Agreement between the computational and manual predictions was good overall, with the computational analysis inferring many pathways that the manual analysis did not identify. Ultimately the manual analysis is also partially speculative, and therefore is not an absolute measure of correctness. The algorithm is designed to err on the side of more false positives to bring more potential pathways to the user's attention. The resulting H. pylori pathway DB is freely available at http://ecocyc.org:1555/HPY/organism-summary?object=HPY. AVAILABILITY: The Pathway Tools software is freely available to academic users, and is available to commercial users for a fee. Contact pkarp@ai.sri.com for information on obtaining the software.

Algorithms↗

Theoretical analysis of alternative splice forms using computational methods.

Nowadays understanding alternative splicing is one of the greatest challenges in biology, because it is a genetic process much more important than thought at the time of its discovery. In this paper, we explain the approach of using the different available databases and software tools to start a large scale investigation of alternative splice forms. To collect information about alternative splicing we investigated known data in the databases using different computational methods. The investigations proceeded from the genomic sequence data to structural protein data. Then, we interpreted those data to find the relationship between alternative splice forms and protein function and structure. We found some interesting features of alternative splicing which are presented here. We discuss the results of one chosen example. They concern the coverage quality of the protein sequence of a known structure, an EST analysis, the validation of splice variants, the determination of the alternative splice type, and finally the link between alternative splicing and disease.

Algorithms↗

Efficient selective screening of haplotype tag SNPs.

UNLABELLED: Haplotypes defined by common single nucleotide polymorphisms (SNPs) have important implications for mapping of disease genes and human traits. Often only a small subset of the SNPs is sufficient to capture the full haplotype information. Such subsets of markers are called haplotype tagging SNPs (htSNPs). Although htSNPs can be identified by eye, efficient computer algorithms and flexible interactive software tools are required for large datasets such as the human genome haplotype map. We describe a java-based program, SNPtagger, which screens for minimal sets of SNP markers to represent given haplotypes according to various user requirements. The program offers several options for inclusion/exclusion of specific markers and presents alternative panels for final selection. AVAILABILITY: The www-based program is available at http://www.well.ox.ac.uk/~xiayi/haplotype/index.html.

Algorithms↗

Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs.

SUMMARY: Biological and engineered networks have recently been shown to display network motifs: a small set of characteristic patterns that occur much more frequently than in randomized networks with the same degree sequence. Network motifs were demonstrated to play key information processing roles in biological regulation networks. Existing algorithms for detecting network motifs act by exhaustively enumerating all subgraphs with a given number of nodes in the network. The runtime of such algorithms increases strongly with network size. Here, we present a novel algorithm that allows estimation of subgraph concentrations and detection of network motifs at a runtime that is asymptotically independent of the network size. This algorithm is based on random sampling of subgraphs. Network motifs are detected with a surprisingly small number of samples in a wide variety of networks. Our method can be applied to estimate the concentrations of larger subgraphs in larger networks than was previously possible with exhaustive enumeration algorithms. We present results for high-order motifs in several biological networks and discuss their possible functions. AVAILABILITY: A software tool for estimating subgraph concentrations and detecting network motifs (mfinder 1.1) and further information is available at http://www.weizmann.ac.il/mcb/UriAlon/

Algorithms↗

A method for estimating stochastic noise in large genetic regulatory networks.

MOTIVATION: Genetic regulatory networks are often affected by stochastic noise, due to the low number of molecules taking part in certain reactions. The networks can be simulated using stochastic techniques that model each reaction as a stochastic event. As models become increasingly large and sophisticated, however, the solution time can become excessive; particularly if one wishes to determine the effect on noise of changes to a series of parameters, or the model structure. Methods are therefore required to rapidly estimate stochastic noise. RESULTS: This paper presents an algorithm, based on error growth techniques from non-linear dynamics, to rapidly estimate the noise characteristics of genetic networks of arbitrary size. The method can also be used to determine analytical solutions for simple sub-systems. It is demonstrated on a number of cases, including a prototype model of the galactose regulatory pathway in yeast. AVAILABILITY: A software tool which incorporates the algorithm is available for use as part of the stochastic simulation package Dizzy. It is available for download at http://labs.systemsbiology.net/bolouri/software/Dizzy/ CONTACT: dorrell@systemsbiology.org SUPPLEMENTARY INFORMATION: A conceptual model of the regulatory part of the galactose utilization pathway in yeast, used as an example in the paper, is available at http://labs.systemsbiology.net/bolouri/models/galconcept.dizzy

Algorithms↗

Dynamic model based algorithms for screening and genotyping over 100 K SNPs on oligonucleotide microarrays.

MOTIVATION: A high density of single nucleotide polymorphism (SNP) coverage on the genome is desirable and often an essential requirement for population genetics studies. Region-specific or chromosome-specific linkage studies also benefit from the availability of as many high quality SNPs as possible. The availability of millions of SNPs from both Perlegen and the public domain and the development of an efficient microarray-based assay for genotyping SNPs has brought up some interesting analytical challenges. Effective methods for the selection of optimal subsets of SNPs spanning the genome and methods for accurately calling genotypes from probe hybridization patterns have enabled the development of a new microarray-based system for robustly genotyping over 100,000 SNPs per sample. RESULTS: We introduce a new dynamic model-based algorithm (DM) for screening over 3 million SNPs and genotyping over 100,000 SNPs. The model is based on four possible underlying states: Null, A, AB and B for each probe quartet. We calculate a probe-level log likelihood for each model and then select between the four competing models with an SNP-level statistical aggregation across multiple probe quartets to provide a high-quality genotype call along with a quality measure of the call. We assess performance with HapMap reference genotypes, informative Mendelian inheritance relationship in families, and consistency between DM and another genotype classification method. At a call rate of 95.91% the concordance with reference genotypes from the HapMap Project is 99.81% based on over 1.5 million genotypes, the Mendelian error rate is 0.018% based on 10 trios, and the consistency between DM and MPAM is 99.90% at a comparable rate of 97.18%. We also develop methods for SNP selection and optimal probe selection. AVAILABILITY: The DM algorithm is available in Affymetrix's Genotyping Tools software package and in Affymetrix's GDAS software package. See http://www.affymetrix.com for further information. 10 K and 100 K mapping array data are available on the Affymetrix website.

Algorithms↗

Molecular complexes at a glance: automated generation of two-dimensional complex diagrams.

MOTIVATION: In this paper a new algorithmic approach is presented, which automatically generates structure diagrams of molecular complexes. A complex diagram contains the ligand, the amino acids of the protein interacting with the ligand and the hydrophilic interactions schematized as dashed lines between the corresponding atoms. The algorithm is based on a combinatorial optimization strategy which solves parts of the layout problem non-heuristically. The depicted molecules are represented as structure diagrams according to the chemical nomenclature. Due to the frequent usage of complex diagrams in the scientific literature as well as in text books dealing with structural biology, biochemistry and medicinal chemistry, the new algorithm is a key element for computer applications in these areas. RESULTS: The method was implemented in the new software tool PoseView. It was tested on a representative dataset containing 305 protein-ligand complexes in total from the Brookhaven Protein Data Bank. PoseView was able to find collision-free layouts for more than three quarters of all complexes. In the following the layout generation algorithm is presented and, additional to the statistical results, representative test cases demonstrating the challenges of the layout generation will be discussed. AVAILABILITY: The method is available as a webservice at http://www.zbh.uni-hamburg.de/poseview.

Algorithms↗

Visualization of genomic aberrations using Affymetrix SNP arrays.

MOTIVATION: DNA copy number aberrations are frequently found in different types of cancer. Recent developments of microarray-based approaches have broadened the knowledge on number and structure of such aberrations. High-density single nucleotide polymorphism (SNP) microarrays provide an extremely high resolution with up to 500,000 SNPs per genome. Owing to the enormous amount of data the detection of common aberrations in large datasets is a great challenge. We describe a novel open source software tool--IdeogramBrowser--which was specifically designed for use with the Affymetrix SNP arrays. It provides an interactive karyotypic visualization of multiple aberration profiles and direct links to GeneCards. Visualization of consensus regions together with gene representation allows the explorative assessment of the data. AVAILABILITY: IdeogramBrowser and its source code are freely available under a creative commons license and can be obtained from http://www.informatik.uni-ulm.de/ni/staff/HKestler/ideo/. IdeogramBrowser is a platform independent Java application.

Algorithms↗

Predicting postoperative nausea and vomiting with the application of an artificial neural network.

BACKGROUND: Several medications have proved to be useful in preventing postoperative nausea and vomiting (PONV). However, routine antiemetic prophylaxis is not cost-effective. We evaluated the accuracy and discriminating power of an artificial neural network (ANN) to predict PONV. METHODS: We analysed data from 1086 in-patients who underwent various surgical procedures under general anaesthesia without antiemetic prophylaxis. Predictors used for ANN training were selected by computing the value of chi(2) statistic and information gain with respect to PONV. The configuration of the ANN was chosen by using a software tool. Then the training of the ANN was performed based on data from a training set (n=656). Testing validation was performed with the remaining patients (n=430) whose outcome regarding PONV was unknown to the ANN. Area under the receiver operating characteristic (ROC) curves were used to quantify predictive performance. ANN performance was compared with those of the Naïve Bayesian classifier model, logistic regression model, simplified Apfel score and Koivuranta score. RESULTS: ANN accuracy was 83.3%, sensitivity 77.9% and specificity 85.0% in predicting PONV. The areas under the ROC curve follow: ANN, 0.814 (0.774-0.850); Naïve Bayesian classifier, 0.570 (0.522-0.617); logistic regression, 0.669 (0.623-0.714); Koivuranta score, 0.626 (0.578-0.672); simplified Apfel score, 0.624 (0.576-0.670). ANN discriminatory power was superior to those of the other predicting models (P<0.05). CONCLUSIONS: The ANN provided the best predictive performance among all tested models.

Adult↗