iDHS-EL

iDHS-EL: identifying DNase I hypersensitive sites by fusing three different modes of pseudo nucleotide composition into an ensemble learning framework. MOTIVATION: Regulatory DNA elements are associated with DNase I hypersensitive sites (DHSs). Accordingly, identification of DHSs will provide useful insights for in-depth investigation into the function of noncoding genomic regions. RESULTS: In this study, using the strategy of ensemble learning framework, we proposed a new predictor called iDHS-EL for identifying the location of DHS in human genome. It was formed by fusing three individual Random Forest (RF) classifiers into an ensemble predictor. The three RF operators were respectively based on the three special modes of the general pseudo nucleotide composition (PseKNC): (i) kmer, (ii) reverse complement kmer and (iii) pseudo dinucleotide composition. It has been demonstrated that the new predictor remarkably outperforms the relevant state-of-the-art methods in both accuracy and stability. AVAILABILITY AND IMPLEMENTATION: For the convenience of most experimental scientists, a web server for iDHS-EL is established at http://bioinformatics.hitsz.edu.cn/iDHS-EL, which is the first web-server predictor ever established for identifying DHSs, and by which users can easily get their desired results without the need to go through the mathematical details. We anticipate that IDHS-EL: will become a very useful high throughput tool for genome analysis.


References in zbMATH (referenced in 10 articles )

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  1. Ahmad, Jamal; Hayat, Maqsood: MFSC: multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou’s PseAAC components (2019)
  2. Khan, Yaser Daanial; Jamil, Mehreen; Hussain, Waqar; Rasool, Nouman; Khan, Sher Afzal; Chou, Kuo-Chen: pSSbond-PseAAC: prediction of disulfide bonding sites by integration of PseAAC and statistical moments (2019)
  3. Pan, Yi; Wang, Shiyuan; Zhang, Qi; Lu, Qianzi; Su, Dongqing; Zuo, Yongchun; Yang, Lei: Analysis and prediction of animal toxins by various Chou’s pseudo components and reduced amino acid compositions (2019)
  4. Qing, Yang; Cangzhi, Jia; Taoying, Li: Prediction of aptamer-protein interacting pairs based on sparse autoencoder feature extraction and an ensemble classifier (2019)
  5. Mei, Juan; Fu, Yi; Zhao, Ji: Analysis and prediction of ion channel inhibitors by using feature selection and Chou’s general pseudo amino acid composition (2018)
  6. Sabooh, M. Fazli; Iqbal, Nadeem; Khan, Mukhtaj; Khan, Muslim; Maqbool, H. F.: Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou’s PseKNC (2018)
  7. Sankari, E. Siva; Manimegalai, D.: Predicting membrane protein types by incorporating a novel feature set into Chou’s general PseAAC (2018)
  8. Jiao, Ya-Sen; Du, Pu-Feng: Prediction of Golgi-resident protein types using general form of Chou’s pseudo-amino acid compositions: approaches with minimal redundancy maximal relevance feature selection (2016)
  9. Muthu Krishnan, S.: Classify vertebrate hemoglobin proteins by incorporating the evolutionary information into the general PseAAC with the hybrid approach (2016)
  10. Yang, Lei; Wang, Shiyuan; Zhou, Meng; Chen, Xiaowen; Zuo, Yongchun; Lv, Yingli: Characterization of BioPlex network by topological properties (2016)