The DOI matches its Crossref record; 122 of the 122 references it cites were found in a registry or an index.
iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC
Open accessAbstract
There are many different types of RNA modifications, which are essential for numerous biological processes. Knowledge about the occurrence sites of RNA modifications in its sequence is a key for in-depth understanding of their biological functions and mechanism. Unfortunately, it is both time-consuming and laborious to determine these sites purely by experiments alone. Although some computational methods were developed in this regard, each one could only be used to deal with some type of modification individually. To our knowledge, no method has thus far been developed that can identify the occurrence sites for several different types of RNA modifications with one seamless package or platform. To address such a challenge, a novel platform called "iRNA-PseColl" has been developed. It was formed by incorporating both the individual and collective features of the sequence elements into the general pseudo K-tuple nucleotide composition (PseKNC) of RNA via the chemicophysical properties and density distribution of its constituent nucleotides. Rigorous cross-validations have indicated that the anticipated success rates achieved by the proposed platform are quite high. To maximize the convenience for most experimental biologists, the platform's web-server has been provided at http://lin.uestc.edu.cn/server/iRNA-PseColl along with a step-by-step user guide that will allow users to easily achieve their desired results without the need to go through the mathematical details involved in this paper.
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An introduction to ROC analysis
Fawcett, T. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.010 · Cited by 21,879
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Cristianini, N., & Shawe-Taylor, J. (2000) · doi:10.1017/cbo9780511801389 · Cited by 13,855
The relationship between Precision-Recall and ROC curves
Davis, J., & Goadrich, M. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143874 · Cited by 6,559
Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq
Dominissini, D., Moshitch-Moshkovitz, S., Schwartz, S., Salmon-Divon, M., Ungar, L., Osenberg, S., Cesarkas, K., Jacob-Hirsch, J., Amariglio, N., Kupiec, M., et al. (2012) · Nature · doi:10.1038/nature11112 · Cited by 4,884
Prediction of protein cellular attributes using pseudo‐amino acid composition
Chou, K. (2001) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.1035 · Cited by 1,943
Self-citationSome remarks on protein attribute prediction and pseudo amino acid composition
Chou, K.-C. (2011) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2010.12.024 · Cited by 1,225
Self-citationThe dynamic epitranscriptome: N6-methyladenosine and gene expression control
Meyer, K. D., & Jaffrey, S. R. (2014) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm3785 · Cited by 1,060
Prediction of Protein Structural Classes
Chou, K.-C., & Zhang, C.-T. (1995) · Critical Reviews in Biochemistry and Molecular Biology · doi:10.3109/10409239509083488 · Cited by 1,058
Self-citationThe dynamic N1-methyladenosine methylome in eukaryotic messenger RNA
Dominissini, D., Nachtergaele, S., Moshitch-Moshkovitz, S., Peer, E., Kol, N., Ben-Haim, M. S., Dai, Q., Di Segni, A., Salmon-Divon, M., Clark, W. C., et al. (2016) · Nature · doi:10.1038/nature16998 · Cited by 1,021
MODOMICS: a database of RNA modification pathways—2013 update
Machnicka, M. A., Milanowska, K., Osman Oglou, O., Purta, E., Kurkowska, M., Olchowik, A., Januszewski, W., Kalinowski, S., Dunin-Horkawicz, S., Rother, K. M., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1007 · Cited by 1,013
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Self-citationSeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins
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Metadata
- Authors
- Feng, P., Ding, H., Yang, H., Chen, W., Lin, H., & Chou, K.-C.
- Venue
- Molecular Therapy - Nucleic Acids
- Publisher
- Elsevier BV
- Published
- 2017-06
- Volume
- 7
- Pages
- 155-163
- ISSN
- 2162-2531 (ISSN-L)
- OpenAlex
- W2599457435
- Cited by
- 288
- References
- 122
- FWCI
- 21.84
- Type
- journal article
- Language
- en
- Open access
- gold
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