Predicting the 3D structure of a protein from its amino acid sequence is an important challenge in bioinformatics. Since directly predicting the 3D structure is hard to achieve, classifying a protein into one of the “folds”, which are pre-defined structural labels in protein databases such as SCOP and CATH, is generally used as an intermediate step to determine the 3D structure. This classification task is called protein fold recognition (PFR), and much research has addressed the problem of either (i) feature extractions from amino acid sequences or (ii) classification methods of the protein folds. In this paper, we propose a new approach for PFR with (i) learning feature representations with unsupervised methods from a large protein database instead of manual feature selection and using external tools. (ii) learning deep neural architectures, recurrent neural networks (RNNs) with long short-term memory (LSTM) units, and re-training the representations instead of fixing the extracted features. On a benchmark dataset, our approach outperforms existing methods that use various physicochemical features.
Various de novo assembly methods based on the concept of k-mer have been proposed. Despite the success of these methods, an alternative approach, referred to as the hybrid approach, has recently been proposed that combines different traditional methods to effectively exploit each of their properties in an integrated manner. However, the results obtained from the traditional methods used in the hybrid approach depend not only on the specific algorithm or heuristics but also on the selection of a user-specific k-mer size. Consequently, the results obtained with the hybrid approach also depend on these factors. Here, we designed a new assembly approach, referred to as the rule-based assembly. This approach follows a similar strategy to the hybrid approach, but employs specific rules learned from certain characteristics of draft contigs to remove any erroneous contigs and then merges them. To construct the most effective rules for this purpose, a learning method based on decision trees, i.e., a complex decision tree, is proposed. Comparative experiments were also conducted to validate the method. The results showed that proposed method could outperformed traditional methods in certain cases.
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