![]() ![]() The second line has 2 red vertical line segments and a purple circle. The first line has one vertical line segment and a purple circle. Each line begins with the gray primer and then is joined by a red line segment with a vertical line attached that ends with a colored circle. At the tip of the arrow are 9 parallel lines, each increasing in length. At the point where the arrow from the key joins the arrow from the primer, the arrow is labeled primer extension and chain termination. The symbols for the key are ddTTP is red circle, ddCTP is blue circle ddATP is green circle and ddGTP is purple circle. ![]() The arrow is joined by an arrow from a key that read dNTPs. An arrow points from the primer to a series of horizontal lines that are increasing in length. ![]() Above the green line is a horizontal gray line with 9 small vertical lines extending from it the left side of the gray line is labeled 5 prime and the right side is labeled 3 prime and the line is titled primer. The left side of the line is labeled 3 prime, the right side of the line is labeled 5 prime, and the line is titled template. At the top of the diagram is a horizontal green line with 17 small vertical lines extending from the horizontal line. We provide a review of learning algorithms for RNNs and discuss future trends in this area.A diagram with images showing the Sanger DNA sequencing method. In this article, we review RNN architectures and we discuss the challenges involved in training RNNs for sequence processing. They are biologically more plausible and computationally more powerful than other modelling approaches, such as Hidden Markov Models (HMMs), which have non-continuous internal states, feedforward neural networks and Support Vector Machines (SVMs), which do not have internal states at all. RNNs can be trained from examples to map input sequences to output sequences and in principle they can implement any kind of sequential behaviour. Comparing to feedforward neural networks, RNNs are well-known for their power to memorise time dependencies and model nonlinear systems. A recurrent neural network (RNN) is an artificial neural network in which self-loop and backward connections between nodes are allowed (Lin & Lee 1996 Schalkoff, 1997). Therefore, an intelligent system with memorising capability is crucial for effective sequence processing and modelling. Processing both these sequences mainly consists of applying the current known patterns to produce or predict the future ones, while a major difficulty is that the range of data dependencies is usually unknown. In general, a temporal sequence consists of nominal symbols from a particular alphabet, while a time-series sequence deals with continuous, real-valued elements (Antunes & Oliverira, 2001). If the content of a sequence will be varying through different time steps, the sequence is called temporal or time-series. Examples of symbolic data patterns occur in modelling natural (human) language, while the prediction of water level of River Thames is an example of processing non-symbolic data. Sequence processing involves several tasks such as clustering, classification, prediction, and transduction of sequential data which can be symbolic, non-symbolic or mixed. ![]()
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