Bio-inspired microsystem for robust genetic assay recognition.

A compact integrated system-on-chip (SoC) architecture solution for robust, real-time, and on-site genetic analysis has been proposed. This microsystem solution is noise-tolerable and suitable for analyzing the weak fluorescence patterns from a PCR prepared dual-labeled DNA microchip assay. In the a...

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Publicado en:Journal of Biomedicine & Biotechnology pp. 10p - 11
Autores principales: Lue J, Fang W
Formato: equations & formulas pictorial research tables/charts Journal Article
Publicado: Wiley-Blackwell 2008 Regular issue
Acceso en línea:Ver este registro en EBSCOhost
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      issn: 11107243
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      dt: 2008 Regular issue
      pid: 480
      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        atl: Bio-inspired microsystem for robust genetic assay recognition.
      aug:
        au:
          Lue J
          Fang W
        affil: Department of Electrical Engineering - Electrophysics, University of Southern California, Los Angeles, CA 90089, USA; lormen@gmail.com
      sug:
        subj:
          Biochips
          Genetic Techniques Equipment and Supplies
          Polymerase Chain Reaction Equipment and Supplies
          Signal Processing, Computer Assisted Equipment and Supplies
          Spectrometry, Fluorescence Equipment and Supplies
          Algorithms
          Biotechnology Methods
          Equipment Design
          Equipment Failure
          Evaluation Research
          Funding Source
          Genetic Techniques Methods
          Neural Networks (Computer)
          Spectrometry, Fluorescence Methods
          Human
      ab: A compact integrated system-on-chip (SoC) architecture solution for robust, real-time, and on-site genetic analysis has been proposed. This microsystem solution is noise-tolerable and suitable for analyzing the weak fluorescence patterns from a PCR prepared dual-labeled DNA microchip assay. In the architecture, a preceding VLSI differential logarithm microchip is designed for effectively computing the logarithm of the normalized input fluorescence signals. A posterior VLSI artificial neural network (ANN) processor chip is used for analyzing the processed signals from the differential logarithm stage. A single-channel logarithmic circuit was fabricated and characterized. A prototype ANN chip with unsupervised winner-take-all (WTA) function was designed, fabricated, and tested. An ANN learning algorithm using a novel sigmoid-logarithmic transfer function based on the supervised backpropagation (BP) algorithm is proposed for robustly recognizing low-intensity patterns. Our results show that the trained new ANN can recognize low-fluorescence patterns better than an ANN using the conventional sigmoid function.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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