Automated Training for Algorithms That Learn from Genomic Data

@inproceedings{Cilingir2015AutomatedTF,
  title={Automated Training for Algorithms That Learn from Genomic Data},
  author={Gokcen Cilingir and Shira L. Broschat},
  booktitle={BioMed research international},
  year={2015}
}
Supervised machine learning algorithms are used by life scientists for a variety of objectives. Expert-curated public gene and protein databases are major resources for gathering data to train these algorithms. While these data resources are continuously updated, generally, these updates are not incorporated into published machine learning algorithms which thereby can become outdated soon after their introduction. In this paper, we propose a new model of operation for supervised machine… CONTINUE READING

Similar Papers

Figures and Tables from this paper.

References

Publications referenced by this paper.
SHOWING 1-10 OF 17 REFERENCES

A database of published protein sub-cellular localisation in apicomplexa

B. J. Woodcroft, K.-L. Scanlon, M. Doyle, T. Speed, S. A. Ralph
  • Version 3, 2010.
  • 2010
VIEW 13 EXCERPTS
HIGHLY INFLUENTIAL

EuPathDB: a portal to eukaryotic pathogen databases

  • Nucleic Acids Research
  • 2010
VIEW 7 EXCERPTS
HIGHLY INFLUENTIAL

Improved prediction of signal peptides: signalP 3.0

J. D. Bendtsen, H. Nielsen, G. von Heijne, S. Brunak
  • Journal of Molecular Biology, vol. 340, no. 4, pp. 783–795, 2004.
  • 2004
VIEW 2 EXCERPTS