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 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.
Department of Electrical Engineering - Electrophysics, University of Southern California, Los Angeles, CA 90089, USA.lormen@gmail.com
Jaw-Chyng
JC
Lue
Wai-Chi
WC
Fang
D032701:Biomimetics / Q000295:instrumentation* / Q000379:methods; D004581:Electronics / Q000295:instrumentation*; D004868:Equipment Failure; D019544:Equipment Failure Analysis; D020869:Gene Expression Profiling / Q000295:instrumentation* / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D016133:Polymerase Chain Reaction / Q000295:instrumentation*; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D013050:Spectrometry, Fluorescence / Q000295:instrumentation* / Q000379:methods