ICGA-PSO-ELM approach for accurate multiclass cancer classification resulting in reduced gene sets in which genes encoding secreted proteins are highly represented.
- DOI
- 10.1109/tcbb.2010.13
- Published
- 2011 Mar-Apr
- Container
- IEEE/ACM transactions on computational biology and bioinformatics
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/tcbb.2010.13,
title = {ICGA-PSO-ELM approach for accurate multiclass cancer classification resulting in reduced gene sets in which genes encoding secreted proteins are highly represented.},
author = {Saraswathi S and Sundaram S and Sundararajan N and Zimmermann M and Nilsen-Hamilton M},
year = {2011},
journal = {IEEE/ACM transactions on computational biology and bioinformatics},
doi = {10.1109/tcbb.2010.13},
url = {https://doi.org/10.1109/tcbb.2010.13}
}RIS
TY - JOUR TI - ICGA-PSO-ELM approach for accurate multiclass cancer classification resulting in reduced gene sets in which genes encoding secreted proteins are highly represented. AU - Saraswathi S AU - Sundaram S AU - Sundararajan N AU - Zimmermann M AU - Nilsen-Hamilton M PY - 2011 JO - IEEE/ACM transactions on computational biology and bioinformatics DO - 10.1109/tcbb.2010.13 UR - https://doi.org/10.1109/tcbb.2010.13 ER -
APA
S, S., S, S., N, S., M, Z., & M, N. (2011). ICGA-PSO-ELM approach for accurate multiclass cancer classification resulting in reduced gene sets in which genes encoding secreted proteins are highly represented.. IEEE/ACM transactions on computational biology and bioinformatics. https://doi.org/10.1109/tcbb.2010.13
Source records
- pubmed · retrieved 2026-09-25T01:44:01.717Z