Deep Feature Selection: Theory and Application to Identify Enhancers and Promoters

@article{Li2016DeepFS,
  title={Deep Feature Selection: Theory and Application to Identify Enhancers and Promoters},
  author={Yifeng Li and Chih-Yu Chen and Wyeth W. Wasserman},
  journal={Journal of computational biology : a journal of computational molecular cell biology},
  year={2016},
  volume={23 5},
  pages={322-36}
}
Sparse linear models approximate target variable(s) by a sparse linear combination of input variables. Since they are simple, fast, and able to select features, they are widely used in classification and regression. Essentially they are shallow feed-forward neural networks that have three limitations: (1) incompatibility to model nonlinearity of features, (2) inability to learn high-level features, and (3) unnatural extensions to select features in a multiclass case. Deep neural networks are… CONTINUE READING
Highly Cited
This paper has 46 citations. REVIEW CITATIONS
Recent Discussions
This paper has been referenced on Twitter 4 times over the past 90 days. VIEW TWEETS

Citations

Publications citing this paper.
Showing 1-10 of 28 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 26 references

Similar Papers

Loading similar papers…