This document discusses the development of a scalable neural network platform for predictive metabonomics. It aims to create a "white box" neural network model that allows users full control over the network architecture. Particle swarm optimization will be used to train the network. The implementation uses C++ and OpenNN libraries in Visual Studio. Future work includes applying neural networks to other applications like structure activity relationships and instrument optimization, and creating a graphical user interface.

1. Development of a Scalable Neural Network Platform for Predictive Metabonomics
Marilyn B. Arceo 1,3 and Grady Hanrahan, Ph.D2,3
1Department of Computer Science, California Lutheran University, Thousand Oaks, CA 91360
2Department of Chemistry, California Lutheran University, Thousand Oaks, CA 91360
3Hugh and Hazel Darling Center for Applied Scientific Computing, California Lutheran University, Thousand Oaks, CA 91360
Artificial neural networks (ANNs) are robust computational methodologies
consisting of interconnected processing elements called nodes or neurons that
work together to produce an output function. Inspired by the human central
nervous system, neural networks have been used in various disciplines and fields.
ANNs have been effective computational method that has been used in chemical
applications and in medicine. The goal is to deviate from the black box method of
neural networks that most commercial software uses and create a white box
method that allows the user to modify the mathematical components of the
algorithm. This allows the user to fully have control of the architecture of the
neural network for the given problem at hand. In addition, we would use particle
swarm optimization (PSO) as a training technique for ANN. PSO has become
more effective and accurate in decision making. The members of the swarms
adapt their behavior due to other swarm member behaviors and environment;
thus, creating self-evolving and self-organizing behavior. Using this, we will be
able to make better predictions for each given data set.
INTRODUCTION
Black Box Model vs. White Box Model
The black box model for ANNs have been concealed and their inner workings have
been not available to the public due to commercial software. As a result, white box
models have been developed in order to have the freedom to create a system where
the components and logic of the neural network can be available. This gives the
neural network the ability to solve nonlinear problems instead of being limited to
linear situations.
PSO
The PSO algorithm that will be used for training the ANNs is defined by the
direction and movement of each particle through the search space, by updating its:
velocity: (1)
and position: (2)
METHODS AND THEORY
The implementation of the neural network was through OpenNN C++ library in
Microsoft Visual Studio 2010. The neural network is fully capable of being modified
by the user in order to adjust the neural network architecture for each different set of
metabonomic data. As a result, we were able to run different data sets and develop
overall predictions.
RESULTS & DISCUSSION
RESULTS & DISCUSSION
We would like to thank the Hugh and Hazel Darling Foundation for funding this
project. We would also like to acknowledge the Office of Undergraduate
Research and Creative Scholarship at California Lutheran University. Finally, we
thank Dr. Craig Reinhart for his valuable input.
ACKNOWLEDGEMENTS
ALGORITHMIC FRAMEWORK
1. Dayhoff, J.E., DeLeo, J.M., Artificial Neural Networks Opening the Black
Box, Cancer, 2001, 91, 1615-1635.
2. Hanrahan, G Computational Neural Networks Driving Complex Analytical
Problem Solving, Anal. Chem, 2010, 82, 4307-4313.
3. Lopez, R., OpenNN: Open Neural Networks Library (Version 0.9) [software].,
2012, Retrieved from http://flood.sourceforge.net.
REFERENCES
Fig. 3. The architecture of a white box model for ANNs where PSO will be used to
train the data.
Fig. 4. Pseudo code for ANN framework and logic for the input layer,
hidden layer and output layer of the algorithm.
In the future, we would like to use ANNs for structural characterization and trend
analysis, structure activity relationships, and for instrument optimization. A
graphical user interface for the neural network would help students or instructors
to easily use and create a neural network based off in their needs.
FUTURE WORK
Fig. 1. Vector representation of PSO velocity and position updates in a two-
dimensional space. At each iteration k a particle updates its velocity and
position using Equations (1) and (2), respectively. This process allows all
particles in the swarm to update their and . The solution vector (the position
vector of the swarm of the) gives the optimal set of values for the PSO
parameters s1, s2, and wk .
Fig. 2. Visual representation of the architecture of a black box model for
ANN. The input layer (representing the data), the hidden layer (where the
transformation of our inputs through a function occurs), and the output
layer (representing the predictions computed from hidden layer).
While the network = not trained
for i in each data[i] in dataset
y = Σ wi * datai - T
a = 1 / ( 1 + e –datai) #activation
function
if y != target #doesn’t reach
target value
network = not trained
for each wi
modify weight_connection(wi)
if y = target and reaches end of
data set
network = trained
if y = target #if it reaches the
target value
repeat for data[i+1]
)(111
i
k
i
k
i
kk
i
k vprsvwv 
i
k
i
k
i
k vxx 11  
Fig. 5. Sample prompt from a run for the ANN algorithm that was written in C++
and implemented in Microsoft Visual Studio.