![]() Where the international application contains a disclosure of a nucleotide and/or amino acid sequence, it must be accompanied by sequence listing that is compliant with WIPO ST.26 Standard. The following information relates to PCT applications filed on or after 1 July 2022 If however the sequence listing is only filed in response to a Rule 13ter request by the ISA then the sequence listing does not form part of the description (unless the subject of an Article 34 amendment) and will not be part of the pamphlet ( Rule 13ter.1(f)).Įxaminers should note that the expressions "nucleotide sequence" and "amino acid sequence" are defined to mean an unbranched sequence of ten or more contiguous nucleotides and an unbranched sequence of four or more contiguous amino acids, respectively branched sequences are specifically excluded ( Annex C). Where the sequence listing was originally filed as part of the description or where it was filed in response to an Article 14 objection (certain defects in the specification) by the Receiving Office, the sequence listing forms part of the description ( Rule 5.2) and hence forms part of the pamphlet. We provide a review of learning algorithms for RNNs and discuss future trends in this area.The listing may be filed as part of the specification or it can be furnished separately at a later date. 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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