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.
The development of low-cost portable instruments for rapidly analyzing
genetic assays would significantly advance the level of medical services
globally. The polymerase chain reaction (PCR) amplification and the capillary electrophoretic (CE)
techniques are often adopted for genetic analysis. A complex system that can
process full PCR amplification and data analysis tasks usually involves
integration of control, optical, thermal, fluid channel, and data acquisition
systems. For example, a portable system
providing full PCR-CE functions was developed earlier for genetic analysis
[
Most of the research efforts for PCR analysis tools were focusing on the development of the PCR microdevices, the associated thermal systems, the optical systems, and the data analysis software tools. However, to our best knowledge, the data acquisition and analysis system for examining PCR samples or assays is usually a computer equipped with specific PCR analysis software but not a compact hardware solution.
Regarding the goal of building a real compact PCR analysis system that
can rapidly find and analyze the desired genetic patterns, the existing data
acquisition and analysis systems (e.g.,
portable computers and interfaces) are considered relatively large in size and
heavy in weight. In addition, human inspectors cannot recognize the genetic
assay patterns as easily as written characters with explicit meanings. Manual massive PCR data analysis can be very
time consuming. Therefore, people involved in “the human genome project” have
used perceptron-like neural networks for helping to recognize the DNA fragments
with specific functions [
Nonetheless, if the genetic analysis task needs to be conducted on a hazardous or dangerous field (e.g., potentially disease-contagious environment), a compact, autonomous, and even disposable PCR data analysis system would be preferred. Therefore, by taking advantages of the VLSI microfabrication technologies and artificial neural network theories, we proposed a microsystem consisting of a unique optical configuration setup, a differential logarithm sensor-processor array chip, and an ANN SoC processor chip for fast recognizing and analyzing the PCR prepared genetic patterns.
In typical PCR amplification procedure, a dual-labeled (i.e., for sample and reference channels)
assay design is commonly used for identifying differentially expressed genes. This method also reduces the sources of
variability/noise due to aspects of individual spot that affects both specimens
similarly [
The intensity of fluorescence light is usually relatively low. Using higher excitation light intensity can lead to brighter fluorescence patterns. Increasing the integral of detection time can enhance the received fluorescence patterns. However, lower power consumption and faster detection are preferred. Furthermore, some fixed-pattern noises in the input pattern may exist (e.g., fixed pattern noises created by scattered lights, nonuniformity of the responsivity of the detector array). These noises may introduce errors to the measurement of the density of the DNA materials.
In order to fast parallel-process the data and resolve the ambiguity
induced by the noises in the data analysis task, a trained artificial neural
network is considered a solution. The parallel processing capability comes from
the nature of the ANN’s multiple input architecture [
We proposed a hardware microsystem that is suitable for real-time,
on-site, robust genetic fluorescence data analysis (Figure
(a) Hierarchical diagram of the proposed biochip microsystem for genetic assay recognition. (b) Schematics of a three-layered ANN and the preceding differential logarithm stage. The system of dual-labeled gene assay, dual-color beam module, and imaging lens is not shown in this schematic diagram. The thin-film color filters coated on top of the sample and reference channels are represented by the red and green boxes.
(right) Proposed layout of a 15-by-15 unit cell array of the differential logarithm circuitry (2.2 mm by 2.2 mm), (top left) an enlarged view of the layout of a single cell with pseudo thin-film monochromatic filter layers, and (bottom left) the schematic diagram of a single unit cell.
The operational function of each
module is explained below along the optical and electrical signal pathways. The
dual-labeled genetic dots/wells are simultaneously excited by two monocromatic
excitation beams (e.g., 532 nm with a
bandwidth of 10 nm from a green diode laser pointer source for the cyanine Cy3 dye, and 635 nm with a bandwidth
of 10 nm from a red solid-state diode laser source for the cyanine Cy5 dye) according to the receiving
bandwidths of the sample and the reference channels. The assay can be either
front-side or backside illuminated as long as a clear fluorescence image of the
dot/well array is generated. Two fluorescence patterns with different peak
wavelengths are produced (e.g., peak
value at 570 nm from the Cy3 dye and peak value at 670 nm from the Cy5 dye, the
two spectral profiles are highly distinguishable), and imaged onto the
bioimaging chip through an imaging lens. Each unit on the bioimaging chip
contains two sensor channels. One sensor is coated with a thin-film microfilter
for wavelength A (e.g., 580 nm with a
narrow transmission bandwidth of approximately 40 nm of a deposited thin-film filter
[
The ANN stage is responsible for filtering and recognizing the desired assay
cluster patterns. Fixed pattern noises and noises caused by the nonlinear
circuitry are expected to be accommodated after the ANN is trained. Either unsupervised or supervised learning
algorithm can be adopted to train the ANN.
The ANN in the biochip module architecture can be implemented by either
hardware or software. In this work, we provide a hardware implementation (i.e., a weight-reconfigurable winner-take-all
ANN chip suitable for the Kohonen self-organized filter algorithm [
Because the weak fluorescence signals are enhanced by the imaging chip and automatically analyzed by the noise-tolerable neural network module, the entire architecture system is expected to robustly conduct the recognition task.
The proposed bioimaging chip consists of an array
of differential logarithm processor unit and row/column readout circuit. A prototype
layout of an array of
The key logarithmic amplifier circuit is
designed after the works of Chamberlain and Lee [
The optoelectronic logarithmic amplifier
circuit for each channel in this work was fabricated by using the MOSIS AMI 1.5-
Output voltage of a single channel of saturated logarithmic circuit as function of the input optical power (input wavelength: 830 nm). An optical micrograph of the single-channel logarithmic amplifier circuit and its correspondent schematic circuit diagram are shown.
The SoC architecture design of the weight-reconfigurable ANN processor
consists of an input neurons array, a programmable synapse weight matrix, an
array of output neurons, a winner-take-all module, and a membership encoder [
The ANN processor works as a learning accelerator in the learning phase
at a time complexity
This ANN processor can also support the
multiple winner-take-all scenario (e.g.,
more than one classes
that the input assay pattern may belong to, or multiple desired patterns that
the input assay pattern are similar to). After a winning pattern (the most
likelihood) was picked out from the
The ANN chip can learn unsupervised if the selforganization learning procedure is adopted. In this case, the ANN chip can perform on-chip learning in the learning phase. For the supervised learning version (e.g., back-propagation algorithm or its variations), the weight update procedure usually involves complex computations that require further signal processing circuits in order to achieve the on-chip learning purpose. Further real estates on chip are then required to accommodate the circuits.
A prototype ANN SoC chip using a scalable 2-
The optical micrograph of the prototype ANN chip that is wire-bonded to a ceramic package. The silicon chip die size is
A system-on-chip architecture design for the winner-take-all selforganization artificial neural network chip.
An engineering version of the ANN SoC SiP (silicon intellectual property)
has been under development to enable the proposed miniaturized PCR
system-on-chip design using the TSMC 130-nm 1.2-V CMOS technology. The scalable
ANN prototype chip can be converted into a design containing 100 input neurons,
In this section, two pattern-recognition tasks were computer simulated to demonstrate the feasibility of using an ANN for our proposed biochip module architecture. A novel sigmoid-logarithmic function is also integrated within the learning algorithm (i.e., back-propagation algorithm) to demonstrate the capability of recognizing relatively dim patterns. The study in this section will assist our future circuit design and may contribute to the new techniques for medical image processing.
In most of the fluorescence spectroscopy applications, the fluorescence patterns usually have relatively low intensities and are difficult to analyze. We know that high-excitation intensities and long exposure time can lead to stronger fluorescence signals. However, low-energy consumption and fast detection are the design goals for our biochip module architecture. Therefore, if the posterior ANN of our biochip architecture can analyze dim fluorescence patterns better, we can potentially use relatively lower energy and shorter time to conduct the analysis task.
Regarding the neural network learning algorithm, the simplest transfer function that we can use in the algorithm is a linear ramp function (e.g., linear slope between 1 and −1, flat and continuous outside [1, −1]). However, higher recognition capability can be achieved by using nonlinear transfer function in the neural network learning algorithm.
The
nonlinear sigmoid (logistic) transfer function is usually adopted in artificial
neural network models because its derivative can be easily obtained
algebraically. For example, we define
The first derivative of
In addition, a single-layer feedforward
network (SLFN) with any bounded continuous nonconstant activation (transfer) function
or arbitrarily bounded activation (transfer) function with unequal limits at the
infinities can form decision regions with arbitrary shapes [
For the above computational advantage and theoretical reasons, we proposed a novel piecewise sigmoid-logarithmic function that also yields similar mathematical identities and computational benefits:
In this
piecewise function,
To demonstrate the capability of recognizing dim patterns by using a feedforward ANN with sigmoid-logarithmic transfer function, a simple pseudo genetic assay analysis task and an optical character pattern-recognition task were simulated. MATLAB programs were created to train an ANN and examine its performance.
A
100-100-2 (100 inputs, 100 hidden neurons, and 2 output neurons) artificial
feedforward neural network was chosen to perform both recognition tasks. For the
biosignature recognition, 20 patterns/clusters on a microchip genetic assay
were prepared (Figure
(a) Fluorescence image of a sampled microarray of cDNA, Cy3 dye, and Cy5 dye mix (only Cy5 red fluorescence is shown) [
Similarly, in the optical character-recognition
task, a dataset containing 100 different alphanumeric letters 1, 2, 3, and 4
was prepared first (as shown in Figure
The picture of 100 different patterns of alphanumeric letters 1, 2, 3, and 4 used in the optical character recognition experiment.
In both experiments, the digitized biopattern and character datasets were used for both training and testing the artificial neural network. In contrast to the traditional method of preparing independent training and test datasets, the test datasets were assigned to be identical to the training datasets in order to examine the feasibility of the proposed ANN model with the sigmoid-logarithmic function.
For simplicity, the intensities of the high-resolution
pixels of each original fluorescence dot in Figure
The
back-propagation training using sigmoid-logarithmic transfer function and
gradient descent method was conducted to find a convergent weight configuration
(with fixed learning rate
The entire procedure of the BP training algorithm using sigmoid-logarithmic transfer function is described as follows.
Prepare the input patterns for the
feedforward multilayer perceptron (MLP) neural network. Assign the target values for the
associated input patterns. Use the input patterns to train
the multilayer perceptron with sigmoid transfer function until the criterion
value becomes close to one. Now the weight configuration is closer to a
convergence condition for latter training. Use the weight values obtained in
the previous step as the initial weight condition for training the multilayer
perceptron with the logarithmic-sigmoid transfer function. The regular BP algorithm using the gradient
descent method is again adopted. After
the criterion becomes one, the training is finished. Use this trained MLP with
logarithmic-sigmoid transfer function to recognize the test data set. Examine
the recognition accuracy.
The detailed conditions and pseudocodes of the BP algorithm with sigmoid and logarithmic-sigmoid transfer function are provided as the following.
The initial weight values for the first
weight matrix
The
pseudocode for the regular back-propagation algorithm using sigmoid transfer
function is listed in
Algorithm
The pseudocode for
the back-propagation algorithm using the piecewise sigmoid-logarithmic transfer
function is listed in
Algorithm
The
result of recognizing all of the normalized genetic assay datasets by the
trained 100-input-100-hidden-neuron-2-output-neuron network is shown in Table
Biosignature and OCR recognition results (Unit: counts of patterns correctly recognized in one test dataset. OCR: each test dataset contains 100 characters. BIO: biosignature recognition task, each test dataset contains 20 patterns.)
Gray level to original data | Transfer function (learning rate) | |||||
---|---|---|---|---|---|---|
Hybrid sigmoid-logarithmic | ||||||
BIO | OCR | BIO | OCR | BIO | OCR | |
1 (original) | 20 | 64 | 20 | 79 | 20 | 63 |
1/3.16 | 20 | 58 | 6 | 48 | 13 | 65 |
1/10 | 20 | 58 | 1 | 25 | 1 | 37 |
1/31.6 | 20 | 47 | 1 | 25 | 1 | 25 |
1/100 | 19 | 32 | — | — | — | 25 |
1/316 | 17 | 26 | — | — | — | — |
1/1000 | 17 | 25 | — | — | — | — |
1/10000 | 17 | 25 | — | — | — | — |
A new optoelectronic multichip microsystem for real-time field applicable robust dual-label PCR assay analysis was proposed. This microsystem architecture contains a front-end bioimage chip for analog signal conversion and augmentation, and an artificial neural network for the autonomous data analysis purpose. A differential logarithmic bioimage chip is designed and presented. The typical data analysis procedure of taking logarithm of the ratio of the normalized post-PCR sample intensity is conducted effectively in this differential logarithmic bio-image chip. A single channel logarithmic circuit of the differential logarithmic bioimage chip was designed, fabricated, and characterized. The weak fluorescence signals can be amplified by this logarithmic amplifier circuit for easier data analysis. Regarding the ANN subsystem, an unsupervised hardware version: a weight-reconfigurable winner-take-all ANN SoC chip suitable for selforganized Kohonen filter algorithm, and a supervised software version: a computer-simulated ANN using back-propagation algorithm with a novel sigmoid-logarithmic transfer function is presented. The back-propagation neural network learning algorithm using the sigmoid-logarithmic function was successfully simulated. The simulation results show that a trained ANN using this new transfer function can classify low-fluorescence patterns better than using the conventional sigmoid transfer function. This software model might be applicable to other medical image processing tasks. In summary, by integrating the optical setup, the bioimage chip, and the artificial neural network processor with excellent performances and advantages listed previously, we can envision the success of using this compact microsystem to conduct on-site, real-time, noise-tolerable, and high-throughput dual-labeled genetic expression analysis efficiently.
Define
input vector to the first layer that contains input patterns For End
Define
input vector that contains input patterns with −1 bias For the net weighted input is falling in the range of the piecewise sigmoid-logarithmic function contains hidden neuron output and −1 bias propagation error set, and the second back-propagation error set according
to where the net weighted input is falling in the range of the piecewise
sigmoid-logarithmic function End
The authors would like to thank Dr. Armand R. Tanguay, Jr. at University of Southern California for the kind instructions and funding for implementing the logarithmic amplifier circuitry.