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- W2153679957 abstract "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 ¿1-vs-1¿ binary classifiers for DNA binding domain prediction. Our result shows that 93.73% accuracy is achieved for multicategory classifier and no less than 90% accuracy for each binary classifier. By comparison, our classifier performs better than other machine learning methods." @default.
- W2153679957 created "2016-06-24" @default.
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- W2153679957 date "2008-11-01" @default.
- W2153679957 modified "2023-10-18" @default.
- W2153679957 title "Detecting DNA-binding domain from sequence and secondary structure Information Using Kernel-based Technique" @default.
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- W2153679957 doi "https://doi.org/10.1109/iske.2008.4730925" @default.
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