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Recognizing Biomedical Named Entities (BioNEs) such as genes, proteins, cells, drugs, diseases, etc. play a vital role in many Biomedical Text Mining applications. BioNER fall into five approaches: Dictionary-Based, Rule-Based, Machine-Learning-Based, Statistical-Based, and Hybrid-Based. Methods Based Machine Learning approach, are more effective than those of other approaches, and therefore have...
DNA-binding proteins play an important role in various intra- and extra-cellular activities. The key in the protein is DNA-binding region also called DNA-binding domain (DBD). However, it is hard to search the DBDs by means of homology search or hidden Markov models because of a wide variety of the sequences. In this work, we develop a kernel-based machine learning method by combination of multiple...
Machine learning methods are widely used in bioinformatics and computational and systems biology. Here, we review the development of machine learning methods for protein structure prediction, one of the most fundamental problems in structural biology and bioinformatics. Protein structure prediction is such a complex problem that it is often decomposed and attacked at four different levels: 1-D prediction...
In this work, we propose a machine learning method to identify protein-protein interacting partners based on domain level knowledge that can take into account information about the interaction sites. The general approach is to use the profile hidden Markov models of protein domains and the known interactions between domains to train a support vector machine. Proteins are characterized by the vectors...
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