The DOI matches its Crossref record; 92 of the 92 references it cites were found in a registry or an index.
iDNA-Methyl: Identifying DNA methylation sites via pseudo trinucleotide composition
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Citations
Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865
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
Cd-hit: Accelerated for clustering the next-generation sequencing data
Fu, L., Niu, B., Zhu, Z., Wu, S., & Li, W. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts565 · Cited by 11,175
Jaenisch, R., & Bird, A. (2003) · Nature Genetics · doi:10.1038/ng1089 · Cited by 6,325
The Nature of Statistical Learning Theory
Sain, S. R. (1996) · Technometrics · doi:10.1080/00401706.1996.10484565 · Cited by 5,434
An Introduction to Support Vector Machines and Other Kernel‐based Learning Methods
(2001) · Kybernetes · doi:10.1108/k.2001.30.1.103.6 · Cited by 3,308
Epigenetic Reprogramming in Mammalian Development
Reik, W., Dean, W., & Walter, J. (2001) · Science · doi:10.1126/science.1063443 · Cited by 3,078
Cited by
Liu, B., Liu, F., Wang, X., Chen, J., Fang, L., & Chou, K.-C. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv458 · Cited by 721
Self-citationDeepCpG: accurate prediction of single-cell DNA methylation states using deep learning
Angermueller, C., Lee, H. J., Reik, W., & Stegle, O. (2017) · Genome Biology · doi:10.1186/s13059-017-1189-z · Cited by 536
Machine learning and integrative analysis of biomedical big data
Mirza, B., Wang, W., Wang, J., Choi, H., Chung, N. C., & Ping, P. (2019) · Genes · doi:10.3390/genes10020087 · Cited by 380
Liu, B., Fang, L., Long, R., Lan, X., & Chou, K.-C. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btv604 · Cited by 362
Self-citationLiu, B., Yang, F., Huang, D.-S., & Chou, K.-C. (2018) · Bioinformatics · doi:10.1093/bioinformatics/btx579 · Cited by 331
Self-citation
Metadata
- Authors
- Liu, Z., Xiao, X., Qiu, W.-R., & Chou, K.-C.
- Venue
- Analytical Biochemistry
- Publisher
- Elsevier BV
- Published
- 2015-04
- Volume
- 474
- Pages
- 69-77
- ISSN
- 0003-2697 (ISSN-L)
- OpenAlex
- W1971057981
- Cited by
- 252
- References
- 92
- FWCI
- 19.94
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: iDNA-Methyl: Identifying DNA methylation sites via pseudo trinucleotide composition. https://app.cochat.ai/papers/10.1016/j.ab.2014.12.009
- The paper
- Liu, Z., Xiao, X., Qiu, W.-R., & Chou, K.-C. (2015). iDNA-Methyl: Identifying DNA methylation sites via pseudo trinucleotide composition. Analytical Biochemistry. https://doi.org/10.1016/j.ab.2014.12.009
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
