iNuc-PseKNC

iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition. Motivation: Nucleosome positioning participates in many cellular activities and plays significant roles in regulating cellular processes. With the avalanche of genome sequences generated in the post-genomic age, it is highly desired to develop automated methods for rapidly and effectively identifying nucleosome positioning. Although some computational methods were proposed, most of them were species specific and neglected the intrinsic local structural properties that might play important roles in determining the nucleosome positioning on a DNA sequence. Results: Here a predictor called ‘iNuc-PseKNC’ was developed for predicting nucleosome positioning in Homo sapiens, Caenorhabditis elegans and Drosophila melanogaster genomes, respectively. In the new predictor, the samples of DNA sequences were formulated by a novel feature-vector called ‘pseudo k-tuple nucleotide composition’, into which six DNA local structural properties were incorporated. It was observed by the rigorous cross-validation tests on the three stringent benchmark datasets that the overall success rates achieved by iNuc-PseKNC in predicting the nucleosome positioning of the aforementioned three genomes were 86.27%, 86.90% and 79.97%, respectively. Meanwhile, the results obtained by iNuc-PseKNC on various benchmark datasets used by the previous investigators for different genomes also indicated that the current predictor remarkably outperformed its counterparts.


References in zbMATH (referenced in 31 articles )

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  1. Jia, Jianhua; Li, Xiaoyan; Qiu, Wangren; Xiao, Xuan; Chou, Kuo-Chen: iPPI-PseAAC(CGR): identify protein-protein interactions by incorporating chaos game representation into PseAAC (2019)
  2. Qing, Yang; Cangzhi, Jia; Taoying, Li: Prediction of aptamer-protein interacting pairs based on sparse autoencoder feature extraction and an ensemble classifier (2019)
  3. Zhao, Wei; Li, Guang-Ping; Wang, Jun; Zhou, Yuan-Ke; Gao, Yang; Du, Pu-Feng: Predicting protein sub-Golgi locations by combining functional domain enrichment scores with pseudo-amino acid compositions (2019)
  4. Akbar, Shahid; Hayat, Maqsood: iMethyl-STTNC: identification of N(^6)-methyladenosine sites by extending the idea of SAAC into Chou’s PseAAC to formulate RNA sequences (2018)
  5. Arif, Muhammad; Hayat, Maqsood; Jan, Zahoor: IMem-2LSAAC: a two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into Chou’s pseudo amino acid composition (2018)
  6. Contreras-Torres, Ernesto: Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou’s PseAAC (2018)
  7. Jia, Cangzhi; Yang, Qing; Zou, Quan: NucPosPred: predicting species-specific genomic nucleosome positioning via four different modes of general PseKNC (2018)
  8. 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)
  9. Saghapour, Ehsan; Sehhati, Mohammadreza: Prediction of metastasis in advanced colorectal carcinomas using CGH data (2017)
  10. Ali, Farman; Hayat, Maqsood: Machine learning approaches for discrimination of extracellular matrix proteins using hybrid feature space (2016)
  11. Amiri, Saeid; Dinov, Ivo D.: Comparison of genomic data via statistical distribution (2016)
  12. 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)
  13. Jiao, Ya-Sen; Du, Pu-Feng: Predicting Golgi-resident protein types using pseudo amino acid compositions: approaches with positional specific physicochemical properties (2016)
  14. Muthu Krishnan, S.: Classify vertebrate hemoglobin proteins by incorporating the evolutionary information into the general PseAAC with the hybrid approach (2016)
  15. Yang, Lianping; Zhang, Xiangde; Fu, Haoyue; Yang, Chenhui: An estimator for local analysis of genome based on the minimal absent word (2016)
  16. Ali, Farman; Hayat, Maqsood: Classification of membrane protein types using voting feature interval in combination with Chou’s pseudo amino acid composition (2015)
  17. Aram, Reza Zohouri; Charkari, Nasrollah Moghadam: A two-layer classification framework for protein fold recognition (2015)
  18. Bag, Susmita; Ramaiah, Sudha; Anbarasu, Anand: fabp4 is central to eight obesity associated genes: a functional gene network-based polymorphic study (2015)
  19. Ding, Yanrui; Wang, Xueqin; Mou, Zhaolin: Communities in the iron superoxide dismutase amino acid network (2015)
  20. Ju, Zhe; Cao, Jun-Zhe; Gu, Hong: iLM-2L: a two-level predictor for identifying protein lysine methylation sites and their methylation degrees by incorporating K-gap amino acid pairs into Chou’s general PseAAC (2015)

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