基于VLSI生物芯片的鲁棒生物信息学识别

Jaw-Chyng L. Lue, W. Fang
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引用次数: 0

摘要

提出了一种实时、现场、鲁棒的生物信息学模式识别和分析的微系统架构。该系统兼容芯片上的DNA分析手段,如聚合酶链反应(PCR)扩增。提出了一种基于误差反向传播(EBP)算法的新型s型对数传递函数的人工神经网络学习算法。结果表明,与传统的s型神经网络相比,训练后的新神经网络能更好地识别低荧光模式。为计算荧光信号相对强度的对数,设计了一种差分对数成像芯片。设计、制作了单轨对数电路和原型人工神经网络芯片,并对其进行了表征
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust Bioinformatics Recognition with VLSI Biochip Microsystem
A microsystem architecture for real-time, on-site, robust bioinformatic patterns recognition and analysis has been proposed. This system is compatible with on-chip DNA analysis means such as polymerase chain reaction (PCR) amplification. A corresponding novel artificial neural network (ANN) learning algorithm using new sigmoid-logarithmic transfer function based on error backpropagation (EBP) algorithm is invented. Our results show the trained new ANN can recognize low fluorescence patterns better than the conventional sigmoidal ANN does. A differential logarithmic imaging chip is designed for calculating logarithm of relative intensities of fluorescence signals. The single-rail logarithmic circuit and a prototype ANN chip are designed, fabricated and characterized
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