HykGene: a hybrid approach for selecting marker genes for phenotype classification using microarray gene expression data.

MOTIVATION: Recent studies have shown that microarray gene expression data are useful for phenotype classification of many diseases. A major problem in this classification is that the number of features (genes) greatly exceeds the number of instances (tissue samples). It has been shown that selecting a small set of informative genes can lead to improved classification accuracy. Many approaches have been proposed for this gene selection problem. Most of the previous gene ranking methods typically select 50-200 top-ranked genes and these genes are often highly correlated. Our goal is to select a small set of non-redundant marker genes that are most relevant for the classification task. RESULTS: To achieve this goal, we developed a novel hybrid approach that combines gene ranking and clustering analysis. In this approach, we first applied feature filtering algorithms to select a set of top-ranked genes, and then applied hierarchical clustering on these genes to generate a dendrogram. Finally, the dendrogram was analyzed by a sweep-line algorithm and marker genes are selected by collapsing dense clusters. Empirical study using three public datasets shows that our approach is capable of selecting relatively few marker genes while offering the same or better leave-one-out cross-validation accuracy compared with approaches that use top-ranked genes directly for classification. AVAILABILITY: The HykGene software is freely available at http://www.cs.dartmouth.edu/~wyh/software.htm CONTACT: wyh@cs.dartmouth.edu SUPPLEMENTARY INFORMATION: Supplementary material is available from http://www.cs.dartmouth.edu/~wyh/hykgene/supplement/index.htm.

An extensible framework for sharing clinical guidelines and services.

Accurate and descriptive information from clinical studies guides improvements in health care. Clinical guidelines established by authoritative medical organizations provide such information in a standard form for medical professionals' reference. Previous work on electronically sharing clinical guidelines focuses on the idea of building unified clinical terminologies and sharing resources through centralized data repositories. In this paper we propose a novel five-layer framework called the Extensible Clinical Guidelines and Services Sharing Architecture (ECGSSA). This framework provides for clinical guideline sharing among autonomous service providers over a distributed architecture. Requests for exchange of guidelines are disseminated through Web Services through a registry mechanism. Currently we have adopted the Guideline Interchange Format (GLIF) from InterMed as the representation format and use the Open Grid Services Architecture (OGSA) to attain virtual organization of shared guideline and service resources. This approach will allow more flexibility for medical professionals to exchange their practice guidelines in an effort to improve quality of health care. Also, it extends the possibility of solving clinic-related computational problems through collaborative sharing of analytical services. A sample scenario is presented to explain the application of ECGSSA in distributed task assignment and service matching in medical image processing.

BP-neural network based-characterization of electrographic magnetohydrodynamic signals in MR.

Electrocardiographs (ECG) signal collected during magnetic resonance (MR) imaging is affected by signal artifact because magnetic fields produce competing signals, from moving conductors in the large vessels. That is called the magnetohydrodynamic effect, which makes it difficult to recognize ST-T changes from ECG signal collected in a magnetic field (MRI). Resolving that problem is important both for accurate triggering (elimination of false triggers from tall peaked T waves) and for monitoring (identifying if or when patient develops ischemia or myocardial injury). This work describes an algorithm based on neural network that is designed to cancel this artifact for ECG signal acquired during MR imaging.