An assessment of neural network and statistical approaches for prediction of E. coli promoter sites.

By P. B. Horton, Minoru Kanehisa. 1992

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Travers Ching
2. Alexandr A Kalinin
3. Brett K. Beaulieu-Jones
4. Daniel S. Himmelstein
5. Amr M. Alexandari
6. Casey S. Greene
7. G. Way
8. Yifan Peng

Citations in the corpus

• Opportunities and obstacles for deep learning in biology and medicine (2017, background): “Simple neural network classifiers were then proposed to differentiate positive and negative binding sites but did not show meaningful improvements over the weight matrix matching methods [161].”
• Identifying Sigma70 Promoters with Novel Pseudo Nucleotide Composition (2019, methodology): “Many previous methods [3], [9], [10] only extracted shortrange or local-range sequence information as features to recognize prokaryotic promoter.”
• DNA binding sites: representation and discovery (2000, methodology): “These methods were able to show improved discrimination on the training data, but when tested on new, independent data did not show significant improvements over the simple weight matrix methods (Horton and Kanehisa, 1992).”