[Catalyst 2017]

training data) to modify the system until the
differences between the expected output
and the actual output values are minimized. 4
Training algorithms allow for neural networks
to be constructed with optimal weights,
which lets the neural network make accurate
predictions when presented with new
data. One such training algorithm is the
backpropagation algorithm. In this design,
the algorithm analyzes the gradient vector
and the error surface in the data until a
minimum is found. 1 The difficult part of the
backpropagation algorithm is determining
the step size. Larger steps can result in faster
runtimes, but can overstep the solution;
comparatively smaller steps can lead to a
much slower runtime, but are more likely to
find a correct solution. 1
While feedforward neural network designs
like MLP are common, there are many
other neural network designs. These
other structures include examples such
as recurrent neural networks, which allow
for connections between neurons in the
same layer, and self-organizing maps, in
which neurons attain weights that retain
characteristics of the input. All of these
network types also have variations within
their specific frameworks. 5 The Hopfield
network and Boltzmann machine neural
network architectures utilize the recurrent
neural network design. 5 While feedforward
neural networks are the most common, each
design is uniquely suited to solve specific
problems.
DISADVANTAGES
One of the main problems with neural
networks is that, for the most part, they have
limited ability to identify causal relationships
explicitly. Developers of neural networks feed
these networks large swathes of data and
allow for the neural networks to determine
independently which input variables are most
important. 10 However, it is difficult for the
network to indicate to the developers which
variables are most important in calculating
the outputs. While some techniques exist
to analyze the relative importance of each
neuron in a neural network, these techniques
still do not present as clear of a causal
relationship between variables as can be
gained in similar data analysis methods such
as a logistic regression. 10
Another problem with neural networks is
the tendency to overfit. Overfitting of data
occurs when a data analysis model such as a
neural network generates good predictions
for the training data but worse ones for
testing data. 10 Overfitting happens because
the model accounts for irregularities and
outliers in the training data that may not be
present across actual data sets. Developers
can mitigate overfitting in neural networks
by penalizing large weights and limiting
the number of neurons in hidden layers. 10
Reducing the number of neurons in hidden
layers reduces overfitting but also limits the
ability of the neural network to model more
complex, nonlinear relationships. 10
APPLICATIONS
Artificial neural networks allow for the
processing of large amounts of data, making
them useful tools in many fields of research.
For example, the field of bioinformatics
relies heavily on neural network pattern
recognition to predict various proteins’
secondary structures. One popular algorithm
used for this purpose is Position Specific
Iterated Basic Local Alignment Search Tool
(PSI-BLAST) Secondary Structure Prediction
(PSIPRED). 6 This algorithm uses a two-stage
structure that consists of two three-layered
Neural networks can
provide more accurate
predictions more efficiently
than other prediction
models.
feedforward neural networks. The first stage
of PSIPRED involves inputting a scoring matrix
generated by using the PSI-BLAST algorithm
on a peptide sequence. PSIPRED then takes
15 positions from the scoring matrix and uses
them to output three values that represent
the probabilities of forming the three protein
secondary structures: helix, coil, and strand. 6
These probabilities are then input into the
second stage neural network along with the
15 positions from the scoring matrix, and the
output of this second stage neural network
includes three values representing more
accurate probabilities of forming helix, coil,
and strand secondary structures. 6
Neural networks are used not only to predict
protein structures, but also to analyze
genes associated with the development and
progression of cancer. More specifically,
researchers and doctors use artificial
neural networks to identify the type of
cancer associated with certain tumors. Such
identification is useful for correct diagnosis
and treatment of each specific cancer. 7 These
artificial neural networks enable researchers
to match genomic characteristics from large
datasets to specific types of cancer and
predict these types of cancer. 7
In bioinformatic scenarios such as the
above two examples, trained artificial
neural networks quickly provide highquality
results for prediction tasks. 6 These
characteristics of neural networks are
important for bioinformatics projects because
bioinformatics generally involves large
quantities of data that need to be interpreted
both effectively and efficiently. 6
The applications of artificial neural networks
are also viable within fields outside the
natural sciences, such as finance. These
networks can be used to predict subtle trends
such as variations in the stock market or
when organizations will face bankruptcy. 8,9
Neural networks can provide more accurate
predictions more efficiently than other
prediction models. 9
CONCLUSION
Over the past decade, artificial neural
networks have become more refined and are
being used in a wide variety of fields. Artificial
neural networks allow researchers to find
patterns in the largest of datasets and utilize
the patterns to predict potential outcomes.
These artificial neural networks provide a new
computational way to learn and understand
diverse assortments of data and allow for
a more accurate and effective grasp of the
world.
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https://papers.ssrn.com/sol3/papers2.cfm?abstract_
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DESIGN BY Jessica Lee
EDITED BY Jacob Mattia
CATALYST | 69