PURPOSE: A numerical method of designing a multiple quantum filter (MQF) is presented for the optimum detection of myo-inositol (mI), an important brain metabolite, by using in vivo proton nuclear magnetic resonance spectroscopy ((1)HMRS). MATERIALS AND METHODS: Starting from the characterization of the metabolite, the filter design includes the optimization of the sequence parameters such as the two echo times (TEs), the mixing time (TM), and the flip angle and offset frequency of the 3rd 90 degrees pulse which converts multiple quantum coherences (MQCs) back into single quantum coherences (SQCs). The optimized filter was then tested both in phantom and in human brains. RESULTS: The results demonstrate that the proposed MQF can improve the signal-tobackground ratio of the target metabolite by a factor of more than three by effectively suppressing the signal from the background metabolites. CONCLUSION: By incorporating a numerical method into the design of MQFs in 1HMRS the spectral integrity of a target metabolite, in particular, with a complicated spin system can be substantially enhanced.
Brain ; Humans ; Magnetic Resonance Spectroscopy ; Protons ; Spectrum Analysis

We developed an identifier mapping application for bioinformatics research in Java programming language. It is easy to use and provides many usability functionalities that are expected as essentials for a professional application. It supports three widely used mapping services and can convert many ids from one source database into many target databases at once. Id mapping across service providers is possible by remapping the resultant ids. Because it adheres to the NetBeans platform architecture, it can be incorporated into other NetBeans platform applications as an id mapping provider without adaption or modification.
Computational Biology ; Indonesia ; Programming Languages

The Gene Set network viewer (GSnet) visualizes the functional enrichment of a given gene set with a protein interaction network and is implemented as a plug-in for the Cytoscape platform. The functional enrichment of a given gene set is calculated using a hypergeometric test based on the Gene Ontology annotation. The protein interaction network is estimated using public data. Set operations allow a complex protein interaction network to be decomposed into a functionally-enriched module of interest. GSnet provides a new framework for gene set analysis by integrating a priori knowledge of a biological network with functional enrichment analysis.
Gene Ontology ; Microarray Analysis ; Protein Interaction Maps