Bio-Inspired Microsystem for Robust Genetic Assay Recognition
Identifieur interne : 000B20 ( Main/Exploration ); précédent : 000B19; suivant : 000B21Bio-Inspired Microsystem for Robust Genetic Assay Recognition
Auteurs : Jaw-Chyng Lue [États-Unis] ; Wai-Chi Fang [République populaire de Chine]Source :
- Journal of Biomedicine and Biotechnology [ 1110-7243 ] ; 2008.
Abstract
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.
Url:
DOI: 10.1155/2008/259174
PubMed: 18566679
PubMed Central: 2426746
Affiliations:
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