Biologically inspired intelligent decision making: a commentary on the use of artificial neural networks in bioinformatics

Abstract

Artificial neural networks (ANNs) are a class of powerful machine learning models for classification and function approximation which have analogs in nature. An ANN learns to map stimuli to responses through repeated evaluation of exemplars of the mapping. This learning approach results in networks which are recognized for their noise tolerance and ability to generalize meaningful responses for novel stimuli. It is these properties of ANNs which make them appealing for applications to bioinformatics problems where interpretation of data may not always be obvious, and where the domain knowledge required for deductive techniques is incomplete or can cause a combinatorial explosion of rules. In this paper, we provide an introduction to artificial neural network theory and review some interesting recent applications to bioinformatics problems.

Keywords: artificial neural networks; bioinformatics; gene identification; gene-gene interaction; genome wide association study; multilayer perceptron; protein structure prediction.

Figure 1. The number of bioinformatics papers in PubMed that reference neural networks, grouped by year.

Figure 2. Breakdown of bioinformatics topics identified across a number of analyzed papers available on PubMed which reference neural networks.

Figure 3. Structure of a typical 3 layer feed forward multilayer perceptron artificial neural network.

Figure 4. Neurons. (A) An artificial neuron from the hidden or output layer of an MLP, and (B) a simplified depiction of a naturally occurring biological neuron.

Figure 5. A sigmoid function. If this sigmoid was used as an activation function, the activation of the neuron would be a value on the x-axis and the corresponding output of the neuron is mapped to the y-axis.

Figure 6. An example of a simulated error surface. The value of a weight (on the x-axis) plotted against the error of the network (the y-axis). The solid red line represents the initial value of a synaptic weight. The dashed red line represents the slope of the error. The green line is a locally minima, a locally optimal weight value. The blue line is the globally optimal value for the weight at which the error contribution is minimized.

Figure 7. Generating a Q3 classification for a specific amino acid (in the dashed box) using the first ANN of PSIPRED.

Figure 8. Improving the accuracy of the Q3 score using a second ANN.

Figure 9. Classifying a nucleotide (in the dashed box) as coding or non-coding using an ANN.

Figure 10. The windowed subsection of the input sequence and the receptive frames for the initiator box. Each receptive field frame is connected to a separate feature detector.