Bio-Inspired Microsystem for Robust Genetic Assay Recognition

Jaw-Chyng Lue, Wai-Chi Fang
<span title="">2008</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/im7oe5kk5zbrbou26efdztq23i" style="color: black;">Journal of Biomedicine and Biotechnology</a> </i> &nbsp;
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
more &raquo; ... ork (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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2008/259174">doi:10.1155/2008/259174</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/18566679">pmid:18566679</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC2426746/">pmcid:PMC2426746</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2bzfdwlphrfxrhgpyd2jbqhnhm">fatcat:2bzfdwlphrfxrhgpyd2jbqhnhm</a> </span>
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