PROTEOME-3D: an interactive bioinformatics tool for large-scale data exploration and knowledge discovery.
Comprehensive understanding of biological systems requires efficient and systematic assimilation of high-throughput datasets in the context of the existing knowledge base. A major limitation in the field of proteomics is the lack of an appropriate software platform that can synthesize a large number of experimental datasets in the context of the existing knowledge base. Here, we describe a software platform, termed PROTEOME-3D, that utilizes three essential features for systematic analysis of proteomics data: creation of a scalable, queryable, customized database for identified proteins from published literature; graphical tools for displaying proteome landscapes and trends from multiple large-scale experiments; and interactive data analysis that facilitates identification of crucial networks and pathways. Thus, PROTEOME-3D offers a standardized platform to analyze high-throughput experimental datasets for the identification of crucial players in co-regulated pathways and cellular processes. (+info)
Generic, simple risk stratification model for heart valve surgery.
BACKGROUND: Heart valve surgery has an associated in-hospital mortality rate of 4% to 8%. This study aims to develop a simple risk model to predict the risk of in-hospital mortality for patients undergoing heart valve surgery to provide information to patients and clinicians and to facilitate institutional comparisons. METHODS AND RESULTS: Data on 32,839 patients were obtained from the Society of Cardiothoracic Surgeons of Great Britain and Ireland on patients who underwent heart valve surgery between April 1995 and March 2003. Data from the first 5 years (n=16,679) were used to develop the model; its performance was evaluated on the remaining data (n=16,160). The risk model presented here is based on the combined data. The overall in-hospital mortality was 6.4%. The risk model included, in order of importance (all P<0.01), operative priority, age, renal failure, operation sequence, ejection fraction, concomitant tricuspid valve surgery, type of valve operation, concomitant CABG surgery, body mass index, preoperative arrhythmias, diabetes, gender, and hypertension. The risk model exhibited good predictive ability (Hosmer-Lemeshow test, P=0.78) and discriminated between high- and low-risk patients reasonably well (receiver-operating characteristics curve area, 0.77). CONCLUSIONS: This is the first risk model that predicts in-hospital mortality for aortic and/or mitral heart valve patients with or without concomitant CABG. Based on a large national database of heart valve patients, this model has been evaluated successfully on patients who had valve surgery during a subsequent time period. It is simple to use, includes routinely collected variables, and provides a useful tool for patient advice and institutional comparisons. (+info)
An audit of lamotrigine, levetiracetam and topiramate usage for epilepsy in a district general hospital.
The aim of this audit was to ascertain outcomes for people who had taken or who were still taking three "new generation" broad-spectrum antiepileptic drugs (AEDs), namely lamotrigine, levetiracetam and topiramate. Thirteen percent of people became seizure free and approximately, one-third had a reduction of greater than 50% in their seizures. Two-thirds of people were still taking their audit AED. In addition, approximately one-third of people with a learning disability derived substantial benefit, although the rate of seizure freedom was lower. All three AEDs were most successful at treating primary generalised epilepsy and least successful with symptomatic generalised epilepsy. With some reservations the data suggests that levetiracetam and topiramate are the most efficacious AEDs, but topiramate is the least well tolerated. These results mean consideration of a "general prescribing policy" is important when using and choosing these AEDs. We conclude that lamotrigine, levetiracetam and topiramate are useful additions to the armamentarium of AEDs. (+info)
Incorporating biological knowledge into distance-based clustering analysis of microarray gene expression data.
MOTIVATION: Because co-expressed genes are likely to share the same biological function, cluster analysis of gene expression profiles has been applied for gene function discovery. Most existing clustering methods ignore known gene functions in the process of clustering. RESULTS: To take advantage of accumulating gene functional annotations, we propose incorporating known gene functions into a new distance metric, which shrinks a gene expression-based distance towards 0 if and only if the two genes share a common gene function. A two-step procedure is used. First, the shrinkage distance metric is used in any distance-based clustering method, e.g. K-medoids or hierarchical clustering, to cluster the genes with known functions. Second, while keeping the clustering results from the first step for the genes with known functions, the expression-based distance metric is used to cluster the remaining genes of unknown function, assigning each of them to either one of the clusters obtained in the first step or some new clusters. A simulation study and an application to gene function prediction for the yeast demonstrate the advantage of our proposal over the standard method. (+info)
A case study in pathway knowledgebase verification.
BACKGROUND: Biological databases and pathway knowledge-bases are proliferating rapidly. We are developing software tools for computer-aided hypothesis design and evaluation, and we would like our tools to take advantage of the information stored in these repositories. But before we can reliably use a pathway knowledge-base as a data source, we need to proofread it to ensure that it can fully support computer-aided information integration and inference. RESULTS: We design a series of logical tests to detect potential problems we might encounter using a particular knowledge-base, the Reactome database, with a particular computer-aided hypothesis evaluation tool, HyBrow. We develop an explicit formal language from the language implicit in the Reactome data format and specify a logic to evaluate models expressed using this language. We use the formalism of finite model theory in this work. We then use this logic to formulate tests for desirable properties (such as completeness, consistency, and well-formedness) for pathways stored in Reactome. We apply these tests to the publicly available Reactome releases (releases 10 through 14) and compare the results, which highlight Reactome's steady improvement in terms of decreasing inconsistencies. We also investigate and discuss Reactome's potential for supporting computer-aided inference tools. CONCLUSION: The case study described in this work demonstrates that it is possible to use our model theory based approach to identify problems one might encounter using a knowledge-base to support hypothesis evaluation tools. The methodology we use is general and is in no way restricted to the specific knowledge-base employed in this case study. Future application of this methodology will enable us to compare pathway resources with respect to the generic properties such resources will need to possess if they are to support automated reasoning. (+info)
First multi-centre evaluation of a knowledge-based implant-assistant for implantable cardioverter-defibrillators.
AIMS: Modern implantable cardioverter-defibrillators (ICDs) place increasing demands on the physician, as their complexity requires more and more knowledge and effort in handling them. To overcome this problem an implant-assistant has been developed, which transfers clinical data entered by the physician into a complete set of parameters for programming a dual-chamber ICD (Tachos-DR, Biotronik, Berlin, Germany) at DFT testing (DFT-Prog) and first permanent programming (Perm-Prog) after implant. METHODS AND RESULTS: Routine ICD implantations were initially evaluated by clinical experts at 19 centres in USA and Europe from 178 patient files. The rating of parameters was related to the number of parameters available in each patient. For DFT-Prog, 98.4% of parameter suggestions were identical to experts' expectations, an additional 1.0% were accepted, 0.5% were rejected, and none was considered harmful. This resulted in an overall acceptance of 94.4% of the DFT-Prog. For Perm-Prog, 96.1% of parameters were identical to those advised by experts, an additional 2.4% were accepted, 1.5% rejected, and seven parameters (0.04%) were considered potentially harmful by experts with an overall acceptance of 86.5%. Adaptation of the implant-assistant increased the overall acceptance to 100% for DFT-Prog and 90.6% for first Perm-Prog without any potentially harmful suggestions. CONCLUSION: The ICD implant-assistant, which allows the physician to programme ICDs directly from clinical data, is a promising method to simplify the programming of modern ICDs. (+info)
Qualitative pharmacokinetic modeling of drugs.
We hypothesize that a representation of drug-drug interactions (DDIs) based on physiologic, pharmacokinetic (PK) and pharmacodynamic (PD) mechanisms will provide more accurate and useful information to clinicians than current approaches that simply tabulate and index pairwise interactions of drugs. This paper explores the strengths, weaknesses, and difficulties of modeling drug mechanisms and reports on our initial work designing and implementing a drug KB based on qualitative pharmacokinetic mechanisms. (+info)
Mining a clinical data warehouse to discover disease-finding associations using co-occurrence statistics.
This paper applies co-occurrence statistics to discover disease-finding associations in a clinical data warehouse. We used two methods, chi2 statistics and the proportion confidence interval (PCI) method, to measure the dependence of pairs of diseases and findings, and then used heuristic cutoff values for association selection. An intrinsic evaluation showed that 94 percent of disease-finding associations obtained by chi2 statistics and 76.8 percent obtained by the PCI method were true associations. The selected associations were used to construct knowledge bases of disease-finding relations (KB-chi2, KB-PCI). An extrinsic evaluation showed that both KB-chi2 and KB-PCI could assist in eliminating clinically non-informative and redundant findings from problem lists generated by our automated problem list summarization system. (+info)