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PubMed2000

Problems in shortening the time to confirmation of ALS diagnosis: lessons from the 1st Consensus Conference, Chicago, May 1998.

The 2nd Consensus Conference (Versailles) on the early diagnosis of amyotrophic lateral sclerosis (ALS) developed themes identified at the 1st Consensus Conference (Chicago) on defining optimal management in ALS. These themes included describing the problems and limitations in current diagnostic practices, identifying consequences of early diagnosis on patient management, establishing recommendations to help healthcare personnel achieve the early diagnosis and proposing solutions to facilitate early diagnosis of ALS. Lessons from the ISIS Survey and the 1st Consensus Conference focused on the variability of the first-contact physician, supply factors for specialists and variability of application of medical techniques. The recently introduced concept of 'ALS health states or stages' was reviewed in terms of ongoing and potential prospective studies. The relative contribution of neuroimaging or clinical neurophysiological investigations to accelerating the diagnosis of ALS in clinical practice was debated. The role of a common ALS knowledge-base among patients, initial healthcare providers, diagnosing neurologists and confirming neurologists was critically appraised with regard to simplified 'ALS diagnostic algorithm', 'ten aphorisms in the diagnosis of ALS' and 'ALS axioms of referral'. Refining this ALS knowledge-base is required to identify a minimum dataset required for the evaluation and diagnosis of ALS.

Diagnostic Imaging↗
PubMed1999

FramePlus: aligning DNA to protein sequences.

MOTIVATION: Automated annotation of Expressed Sequence Tags (ESTs) is becoming increasingly important as EST databases continue to grow rapidly. A common approach to annotation is to align the gene fragments against well-documented databases of protein sequences. The sensitivity of the alignment algorithm is key to the success of such methods. RESULTS: This paper introduces a new algorithm, FramePlus, for DNA-protein sequence alignment. The SCOP database was used to develop a general framework for testing the sensitivity of such alignment algorithms when searching large databases. Using this framework, the performance of FramePlus was found to be somewhat better than other algorithms in the presence of moderate and high rates of frameshift errors, and comparable to Translated Search in the absence of sequencing errors. AVAILABILITY: The source code for FramePlus and the testing datasets are freely available at ftp.compugen.co.il/pub/research. CONTACT: raveh@compugen.co.il.

Algorithms↗
PubMed1999

Scaling a data retrieval and mining application to the enterprise-wide level.

Most medical institutions have had difficulty in adopting practices that use stored clinical and administrative data effectively. This stems in part from the lack of available tools to easily and accurately retrieve datasets of interest. In this work, we describe the development of a data retrieval and mining application, Goldminer, which allows authorized personnel at our institution to query clinical and demographic data stores through a graphical, non-programmer interface. It builds upon DXtractor, our previously described tool that retrieves data from a smaller, more specialized dataset. We discuss the difficulties encountered in scaling this application to the enterprise-wide level, and our solutions.

Databases as Topic↗
PubMed2007

Missing data imputation in two phase III trials treating HIV1 infection.

In most longitudinal clinical trials, some patients drop out before the end of the planned follow-up, and, in order to allow an all-patient intent-to-treat analysis to be performed, it is common practice to use some method of imputation to estimate values for missing data. However, different imputation methods may provide different results, and it is essential to investigate the sensitivity of the analysis using different imputation rules. In our analysis of two trials of the new HIV1 fusion inhibitor enfuvirtide, we compared some standard methods of imputing and analyzing HIV1-RNA data with two novel alternatives, to check the robustness of the primary endpoint results. The standard methods were: (1) last-observation-carried-forward, (2) baseline carried forward, and (3) multiple imputation. These were compared with a nearest-neighbour hot-deck method, specifically proposed for imputation of missing HIV1-RNA data, and with a heuristic approach: censored regression analysis of the last-observation-carried-forward. To supplement this analysis of real clinical trial data, we investigated the performance of the same imputation methods on simulated datasets designed to cover a broader range of missing data patterns.

Algorithms↗
PubMed1994

An anaesthetic minimum dataset and report format. Society for Computing and Technology in Anaesthesia (SCATA). European Society for Computing and Technology in Anaesthesia (ESCTAIC).

The dataset necessary to produce reports for anaesthetic training purposes is described, together with appropriate definitions. The format for a standard report that may be used in a logbook is also described. These have been accepted by the Royal College of Anaesthetists. The German Anaesthetic Society (Deutsche Gesellschaft für Anaesthesiologie und Intensivmedizin, DGAI) has accepted the dataset and definitions.

Anesthesiology↗
PubMed2004

Weighted-support vector machines for predicting membrane protein types based on pseudo-amino acid composition.

Membrane proteins are generally classified into the following five types: (1) type I membrane proteins, (2) type II membrane proteins, (3) multipass transmembrane proteins, (4) lipid chain-anchored membrane proteins and (5) GPI-anchored membrane proteins. Prediction of membrane protein types has become one of the growing hot topics in bioinformatics. Currently, we are facing two critical challenges in this area: first, how to take into account the extremely complicated sequence-order effects, and second, how to deal with the highly uneven sizes of the subsets in a training dataset. In this paper, stimulated by the concept of using the pseudo-amino acid composition to incorporate the sequence-order effects, the spectral analysis technique is introduced to represent the statistical sample of a protein. Based on such a framework, the weighted support vector machine (SVM) algorithm is applied. The new approach has remarkable power in dealing with the bias caused by the situation when one subset in the training dataset contains many more samples than the other. The new method is particularly useful when our focus is aimed at proteins belonging to small subsets. The results obtained by the self-consistency test, jackknife test and independent dataset test are encouraging, indicating that the current approach may serve as a powerful complementary tool to other existing methods for predicting the types of membrane proteins.

Algorithms↗
PubMed2000

Response criteria for clinical trials on osteoarthritis of the knee and hip: a report of the Osteoarthritis Research Society International Standing Committee for Clinical Trials response criteria initiative.

BACKGROUND: The domains of pain, function and patient's global assessment are identified as core variables and frequently measured in clinical trials of patients with osteoarthritis (OA) of the hip and knee. OBJECTIVE: To develop response criteria for OA of hip and knee based on the domains of pain, function and patient's global assessment. METHODS: A methodology was developed by an interaction of the Osteoarthritis Research Society International Standing Committee on Clinical Trials, biostatisticians, pharmaceutical company representatives and health agency representatives. Data from previously conducted placebo-controlled clinical trials were normalized and collated. Data were subset by location of OA (knee, hip), active agent used in the clinical trial (non-steroidal anti-inflammatory drug, other agent) and route of administration (oral, intra-articular). Statistical analysis identified response criteria which best discriminate active agent from placebo. RESULTS: Based on the analysis of data from 14 studies (totaling 1886 patients) and consensus opinion, the optimal responder criteria set differed for location of OA, active agent to be used, and route of administration. Because of nearly identical statistical results, two sets of responder criteria are proposed: (1) 'high' pain response or, alternatively, a 'moderate' response for at least two of three domains: pain, function and patient's global assessment; (2) 'high' response for either pain or function or, alternatively, a 'moderate' response for at least two of three domains: pain, function and patient's global assessment. The sensitivity (i.e., the percentage of responders in the active group) ranged from 52 to 96% and the specificity (i.e., the percentage of nonresponders in the control group) from 47 to 73%. CONCLUSION: Based on data from clinical trials, two sets of responder criteria have been developed that can categorize an individual's responses to treatment in a clinical trial. These responder criteria require validation in additional datasets.

Aged↗
PubMed2001

NMR spectral quantitation by principal component analysis.

The use of principal component analysis (PCA) for simultaneous spectral quantitation of a single resonant peak across a series of spectra has gained popularity among the NMR community. The approach is fast, requires no assumptions regarding the peak lineshape and provides quantitation even for peaks with very low signal-to-noise ratio. PCA produces estimates of all peak parameters: area, frequency, phase and linewidth. If desired, these estimates can be used to correct the original data so that the peak in all spectra has the same lineshape. This ability makes PCA useful not only for direct peak quantitation, but also for processing spectral data prior to application of pattern recognition/classification techniques. This article briefly reviews the theoretical basis of PCA for spectral quantitation, addresses issues of data processing prior to PCA, describes suitable and unsuitable datasets for PCA applications and summarizes the developments and the limitations of the method.

Magnetic Resonance Spectroscopy↗
PubMed1997

An artificial intelligence approach to motif discovery in protein sequences: application to steriod dehydrogenases.

MEME (Multiple Expectation-maximization for Motif Elicitation) is a unique new software tool that uses artificial intelligence techniques to discover motifs shared by a set of protein sequences in a fully automated manner. This paper is the first detailed study of the use of MEME to analyse a large, biologically relevant set of sequences, and to evaluate the sensitivity and accuracy of MEME in identifying structurally important motifs. For this purpose, we chose the short-chain alcohol dehydrogenase superfamily because it is large and phylogenetically diverse, providing a test of how well MEME can work on sequences with low amino acid similarity. Moreover, this dataset contains enzymes of biological importance, and because several enzymes have known X-ray crystallographic structures, we can test the usefulness of MEME for structural analysis. The first six motifs from MEME map onto structurally important alpha-helices and beta-strands on Streptomyces hydrogenans 20beta-hydroxysteroid dehydrogenase. We also describe MAST (Motif Alignment Search Tool), which conveniently uses output from MEME for searching databases such as SWISS-PROT and Genpept. MAST provides statistical measures that permit a rigorous evaluation of the significance of database searches with individual motifs or groups of motifs. A database search of Genpept90 by MAST with the log-odds matrix of the first six motifs obtained from MEME yields a bimodal output, demonstrating the selectivity of MAST. We show for the first time, using primary sequence analysis, that bacterial sugar epimerases are homologs of short-chain dehydrogenases. MEME and MAST will be increasingly useful as genome sequencing provides large datasets of phylogenetically divergent sequences of biomedical interest.

Alcohol Dehydrogenase↗
PubMed2001

Identifying 'non-medical' datasets to monitor community health and well-being.

BACKGROUND: The aim of the study was to identify 'non-medical' datasets holding routinely collected information that might be used to measure and monitor the wider determinants of community health and well-being. METHODS: An expert panel discussion, involving public health and environmental health academics and professionals with expertise in a variety of backgrounds (including environmental health, housing, transport, community safety, public health, primary and secondary care), and interrogation of the Office for National Statistics database were carried out for the West Midlands region. The aim was to identify routinely collected 'non-medical' datasets containing information on the following factors: physical environment, crime, housing and homelessness, social services, socio-economic environment including employment, lifestyles, education, leisure and culture, transport and accidents. RESULTS: Fifty-six datasets were identified. Although 43 (77 per cent) were collected at least annually, few (17; 30 per cent) held data that were disaggregated and routinely available at the sub-local authority level. CONCLUSIONS: This study has identified a number of datasets that hold information relevant to health. However, no single dataset is likely to provide information on all dimensions of health and the determinants of health, and local agencies should consider carefully the strengths and weaknesses of each. Through the development of inter-sectoral working and multi-agency involvement at the local level there is now considerable scope to improve the quality of many of these datasets and to promote their use in the measurement and monitoring of community health.

Accidents↗
PubMed2006

The Candida Genome Database: facilitating research on Candida albicans molecular biology.

The Candida Genome Database (CGD; http://www.candidagenome.org) is a resource for information about the Candida albicans genomic sequence and the molecular biology of its encoded gene products. CGD collects and organizes data from the biological literature concerning C. albicans, and provides tools for viewing, searching, analysing, and downloading these data. CGD also serves as an organizing centre for the C. albicans research community, providing a gene-name registry, contact information, and research community news. This article describes the information contained in CGD and how to access it, either from the perspective of a bench scientist interested in the function of one or a few genes, or from the perspective of a biologist or bioinformatician interpreting large-scale functional genomic datasets.

Alleles↗
PubMed1996

Robust three-dimensional object definition in CT and MRI.

This work describes the application of an object definition algorithm to the medical imaging environment for the task of automated detection of anatomical boundaries in three dimensions in the presence of low spatial frequency nonstationarities. We have chosen the Liou-Jain algorithm and have modified it for use with 3D medical image datasets and extended it by including a recruitment operator that corrects for the algorithm's inherent volume underestimation. The algorithm avoids problems in both traditional statistical segmentation and 2D techniques and elegantly bridges the gap between traditional gradient-based edge finding and regression-based segmentation techniques. Results are shown for MRI datasets from the human abdomen and brain and for a CT dataset of a liver tumor, as well as an MRI scan of a glioma in a rat brain. For comparison, the human abdomen dataset was processed by a multivariate, statistical classifier. The results demonstrate the statistical technique's susceptibility to low spatial frequency nonstationarities due to rf field inhomogeneity; the Liou-Jain algorithm is shown to be immune to this effect. Further, the results show spatial consistency as a result of inherent characteristics of the algorithm. Volumes identified by the algorithm are visualized and assessed qualitatively in three dimensions. Quantitative accuracy of the algorithm's volume estimates is assessed by the use of a phantom. This work demonstrates that this technique is effective in automatically detecting anatomical organ and lesion surfaces in 3D medical datasets that are corrupted by low spatial frequency nonstationarity and in obtaining volume estimates.

Algorithms↗
PubMed2006

Empirical research in bioethical journals. A quantitative analysis.

OBJECTIVES: The objective of this research is to analyse the evolution and nature of published empirical research in the fields of medical ethics and bioethics. DESIGN: Retrospective quantitative study of nine peer reviewed journals in the field of bioethics and medical ethics (Bioethics, Cambridge Quarterly of Healthcare Ethics, Hastings Center Report, Journal of Clinical Ethics, Journal of Medical Ethics, Kennedy Institute of Ethics Journal, Nursing Ethics, Christian Bioethics, and Theoretical Medicine and Bioethics). RESULTS: In total, 4029 articles published between 1990 and 2003 were retrieved from the journals studied. Over this period, 435 (10.8%) studies used an empirical design. The highest percentage of empirical research articles appeared in Nursing Ethics (n = 145, 39.5%), followed by the Journal of Medical Ethics (n = 128, 16.8%) and the Journal of Clinical Ethics (n = 93, 15.4%). These three journals account for 84.1% of all empirical research in bioethics published in this period. The results of the chi2 test for two independent samples for the entire dataset indicate that the period 1997-2003 presented a higher number of empirical studies (n = 309) than did the period 1990-1996 (n = 126). This increase is statistically significant (chi2 = 49.0264, p < .0001). Most empirical studies employed a quantitative paradigm (64.6%, n = 281). The main topic of research was prolongation of life and euthanasia (n = 68). CONCLUSIONS: We conclude that the proportion of empirical research in the nine journals increased steadily from 5.4% in 1990 to 15.4% in 2003. It is likely that the importance of empirical methods in medical ethics and bioethics will continue to increase.

Bioethics↗
PubMed2006

Sufficiency and stability of evidence for public health interventions using cumulative meta-analysis.

We propose cumulative meta-analysis as the procedure of completing a new meta-analysis at each successive wave in a research database. Two facets of cumulative knowledge are considered: the first, sufficiency, refers to whether the meta-analytic database adequately demonstrates that a public health intervention works. The second, stability, refers to the shifts over time in the accruing evidence about whether a public health intervention works. We used a hypothetical data set to develop the indicators of sufficiency and stability, and then applied them to existing, published datasets. Our discussion centers on the implications of the use of this procedure in evaluating public health interventions.

Health Services Research↗
PubMed2000

Coming to terms with datasets for diabetes care.

The development of datasets for the recording, transfer, monitoring and improvement of diabetes care has to take into account the perspectives of different stakeholders. Success of a dataset is dependent on shared ownership, professional support, clear objectives and development by consensus. Different types of concepts are discernible that share similar features, including the classes of disease, qualitative observations, quantitative observations, procedures and record specific concepts. The identification of these types assists the development of a dataset by clarifying the characteristics that need to be defined. The specification of the component data items ideally requires adherence to established terminology principles where each concept is uniquely labelled by an unambiguous term. These identified concepts should also be exclusive, stable over time, defined, reusable and relevant. Attention should also be given to the scope of each concept and consistency in handling additional detail. The dataset overall should adhere to principles of time representation and be manageable, implementable, regulated, extendible, piloted and ethical. Consideration of these principles should improve the likelihood that a dataset will be widely adopted, integrated and be further developed in existing patterns care.

Computer Communication Networks↗
PubMed2005

Evaluation of sequest result filter-Xcorr and Unified Score.

OBJECTIVE: To estimate the effect of two simple filters, two or more positive peptide filter and Unified Score filter on the true positive rate of protein and peptide. METHODS: Twenty-two LC-MS/MS datasets were from 18 known protein mixture. Two or more positive peptide filter and Unified Score filter were applied to the 22 datasets. The filters effect was evaluated according to the true positive rate of protein and peptide for each filter. RESULTS: The positive rates of protein and peptide from two or more peptide filter raised from 56.49% to 92.86%-99.12% (for protein) and from 90.67% to 97.74%-99.62% (for peptide), but many positive proteins were filtered out. The positive rates of protein and peptide from Unified Score (ThermoFinnigan value 2400) were only about 35.51% and 82.99%, but after adjusted the value (3900) according to the number of false positive peptide, those positive rate raised to 63.61% (for protein) and 91.97% (for peptide). CONCLUSIONS: Two or more peptides requirement could significantly decrease false positive rate, but it also may filter out many true positive proteins especially low molecular weight and less abundant proteins. Unified Score may be a better filter than Xcorr and DeltaCn combination and the value of 3900 is found to be more suitable for this particular datasets.

Algorithms↗
PubMed2003

Evolutionary computing for knowledge discovery in medical diagnosis.

One of the major challenges in medical domain is the extraction of comprehensible knowledge from medical diagnosis data. In this paper, a two-phase hybrid evolutionary classification technique is proposed to extract classification rules that can be used in clinical practice for better understanding and prevention of unwanted medical events. In the first phase, a hybrid evolutionary algorithm (EA) is utilized to confine the search space by evolving a pool of good candidate rules, e.g. genetic programming (GP) is applied to evolve nominal attributes for free structured rules and genetic algorithm (GA) is used to optimize the numeric attributes for concise classification rules without the need of discretization. These candidate rules are then used in the second phase to optimize the order and number of rules in the evolution for forming accurate and comprehensible rule sets. The proposed evolutionary classifier (EvoC) is validated upon hepatitis and breast cancer datasets obtained from the UCI machine-learning repository. Simulation results show that the evolutionary classifier produces comprehensible rules and good classification accuracy for the medical datasets. Results obtained from t-tests further justify its robustness and invariance to random partition of datasets.

Adolescent↗
PubMed2006

Detection of gene copy number changes in CGH microarrays using a spatially correlated mixture model.

MOTIVATION: Comparative genomic hybridization array experiments that investigate gene copy number changes present new challenges for statistical analysis and call for methods that incorporate spatial dependence between sequences along the chromosome. For this purpose, we propose a novel method called CGHmix. It is based on a spatially structured mixture model with three states corresponding to genomic sequences that are either unmodified, deleted or amplified. Inference is performed in a Bayesian framework. From the output, posterior probabilities of belonging to each of the three states are estimated for each genomic sequence and used to classify them. RESULTS: Using simulated data, CGHmix is validated and compared with both a conventional unstructured mixture model and with a recently proposed data mining method. We demonstrate the good performance of CGHmix for classifying copy number changes. In addition, the method provides a good estimate of the false discovery rate. We also present the analysis of a cancer related dataset. SUPPLEMENTARY INFORMATION: http://www.bgx.org.uk/papers.html

Algorithms↗