Training Knowledge-Based Neural Networks to Recognize Genes in DNA Sequences
Abstract
We describe the application of a hybrid symbolic/connectionist machine learning algorithm to the task of recognizing important genetic sequences. The symbolic portion of the KBANN system utilizes inference rules that provide a roughly-correct method for recognizing a class of DNA sequences known as eukaryotic splice-junctions. We then map this "domain theory" into a neural network and provide training examples. Using the samples, the neural network's learning algorithm adjusts the domain theory so that it properly classifies these DNA sequences. Our procedure constitutes a general method for incorporating preexisting knowledge into artificial neural networks. We present an experiment in molecular genetics that demonstrates the value of doing so.
Cite
Text
Noordewier et al. "Training Knowledge-Based Neural Networks to Recognize Genes in DNA Sequences." Neural Information Processing Systems, 1990.Markdown
[Noordewier et al. "Training Knowledge-Based Neural Networks to Recognize Genes in DNA Sequences." Neural Information Processing Systems, 1990.](https://mlanthology.org/neurips/1990/noordewier1990neurips-training/)BibTeX
@inproceedings{noordewier1990neurips-training,
title = {{Training Knowledge-Based Neural Networks to Recognize Genes in DNA Sequences}},
author = {Noordewier, Michiel O. and Towell, Geoffrey G. and Shavlik, Jude W.},
booktitle = {Neural Information Processing Systems},
year = {1990},
pages = {530-536},
url = {https://mlanthology.org/neurips/1990/noordewier1990neurips-training/}
}