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 differential logarithm stage is designed for effectively computing the logarithm of the normalized input fluorescence signals. A posterior VISI 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 winner-take-all (WTA) function was designed, fabricated, and tested. An ANN learning algorithm using a novel sigmoid-logarithmic transfer function based on the error backpropagation (BP) algorithm is proposed for robustly recognizing low intensity patterns. Our results show the trained new ANN can recognize low fluorescence patterns better than the ANN using the conventional sigmoid function.

[1]  Paul J. Werbos,et al.  The Roots of Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting , 1994 .

[2]  Edward C. Uberbacher,et al.  GRAIL: an integrated artificial intelligence system for gene recognition and interpretation , 1992, Proceedings Eighth Conference on Artificial Intelligence for Applications.

[3]  Guang-Bin Huang,et al.  Classification ability of single hidden layer feedforward neural networks , 2000, IEEE Trans. Neural Networks Learn. Syst..

[4]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[5]  Jaw-Chyng Lormen Lue.  Neuron unit arrays and Nature/Nurture adaptation for photonic multichip modules , 2007 .

[6]  Victor M Ugaz,et al.  A pocket-sized convective PCR thermocycler. , 2007, Angewandte Chemie.

[7]  Carver Mead,et al.  Analog VLSI and neural systems , 1989 .

[8]  Jude W. Shavlik,et al.  Machine learning approaches to gene recognition , 1994, IEEE Expert.

[9]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[10]  S.G. Chamberlain,et al.  A novel wide dynamic range silicon photodetector and linear imaging array , 1984, IEEE Journal of Solid-State Circuits.

[11]  A. Zöller,et al.  Temperature-stable bandpass filters deposited with plasma ion-assisted deposition. , 1996, Applied optics.

[12]  I. Sandberg.  General structures for classification , 1994 .

[13]  Wei Zhang,et al.  Obtaining reliable information from minute amounts of RNA using cDNA microarrays , 2002, BMC Genomics.

[14]  Richard Simon,et al.  Questions and answers on design of dual-label microarrays for identifying differentially expressed genes. , 2003, Journal of the National Cancer Institute.

[15]  I-Ming Hsing,et al.  Microfabricated PCR-electrochemical device for simultaneous DNA amplification and detection. , 2003, Lab on a chip.

[16]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[17]  J R Scherer,et al.  Integrated portable genetic analysis microsystem for pathogen/infectious disease detection. , 2004, Analytical chemistry.

[18]  Wai-Chi Fang.  A smart vision system-on-a-chip design based on programmable neural processor integrated with active pixel sensor , 2000, 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353).

[19]  R. Stephenson.  A and V , 1962, The British journal of ophthalmology.

[20]  Oscal T.-C. Chen,et al.  A VLSI neural processor for image data compression using self-organization networks , 1992, IEEE Trans. Neural Networks.