Application of a Time-delay Neural Network to Promoter Annotation in the Drosophila Melanogaster Genome

By Martin G. Reese. Computers and Chemistry, 2001

885 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f6faf249f6cef5248ab791475e6b3c6b5a508610

How researchers see this paper

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

In Molecular Biology

1. Ramzan Umarov
2. Kil To Chong
3. V. V. Solovyev
4. Mhaned Oubounyt
5. Zakaria Louadi
6. Hilal Tayara

Citations in the corpus

• DeePromoter: Robust Promoter Predictor Using Deep Learning (2019, methodology): “1 (Reese, 2001) which utilized initiator element (Inr) and TATA-Box for feature extraction and a time-delay neural network for classification, and Down and Hubbard (2002) which used TATA-Box and utilized a relevance vector machines (RVM) as a classifier.”
• Recognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networks (2016, background): “These programs clearly outperform the NNPP (trained on E.coli K12 sequences) [11] and SIDD [17] programs.”