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HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features.

Rianon ZamanShahana Yasmin ChowdhuryMahmood A RashidAlok SharmaAbdollah DehzangiSwakkhar Shatabda
Published in: BioMed research international (2017)
DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM) as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature.
Keyphrases
  • binding protein
  • circulating tumor
  • machine learning
  • cell free
  • deep learning
  • single molecule
  • stem cells
  • single cell
  • rna seq
  • circulating tumor cells
  • amino acid
  • genetic diversity