• Corpus ID: 14533821

Back-Propagation Learning on RibosomalBinding sites in DNA sequences usingpreprocessed

@inproceedings{Pratt2007BackPropagationLO,
  title={Back-Propagation Learning on RibosomalBinding sites in DNA sequences usingpreprocessed},
  author={Y. Pratt and Leena Laur{\'e}n and TracyDept and Michiel and NoordewierDept},
  year={2007}
}
Several studies have explored how neural networks can be used to nd genes within regions of previously uncharacterized deoxyribonucleic acid (DNA). This paper describes the creation of a neural network training set for determining which part of a DNA strand codes for an important genetic feature called a Ribosomal Binding Site, or RBS. Based on previous research on detecting other genetic features, this data set contains preprocessed features that reeect biologically meaningful patterns in the… 
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References

SHOWING 1-9 OF 9 REFERENCES

Using background knowledge to improve inductive learning of DNA sequences

  • H. HirshM. Noordewier
  • Biology
    Proceedings of the Tenth Conference on Artificial Intelligence for Applications
  • 1994
The use of background knowledge of molecular biology to re-express data into a form more appropriate for learning is described, showing dramatic improvements in classification accuracy for two very different classes of DNA sequences using traditional "off-the-sheIf" decision-tree and neural-network inductive-learning methods.

Use of the 'Perceptron' algorithm to distinguish translational initiation sites in E. coli.

We have used a "Perceptron" algorithm to find a weighting function which distinguishes E. coli translational initiation sites from all other sites in a library of over 78,000 nucleotides of mRNA…

Interpretation of Artificial Neural Networks: Mapping Knowledge-Based Neural Networks into Rules

Empirical tests show that the rules the method extracts from trained neural networks: closely reproduce the accuracy of the network from which they came, are superior to the rules derived by a learning system that directly refines symbolic rules, and are expert-comprehensible.

Computing in molecular biology: mapping and interpreting biological information

Computational challenges for computer scientists in the task of determining the structure of molecules and their components are examined. Problems in sequence analysis, information storage and…

Discriminability-Based Transfer between Neural Networks

A new algorithm, called Discriminability-Based Transfer (DBT), is presented, which uses an information measure to estimate the utility of hyperplanes defined by source weights in the target network, and rescales transferred weight magnitudes accordingly.

Handbook of neural computing applications

Geoorey Cowley. Family matters

  • Geoorey Cowley. Family matters
  • 1993

Computer Systems That Learn

Symbolic and neural learning algorithms: An experimental comparison

Experimental results suggest that backpropagation can work significantly better on data sets containing numerical data, and occasionally outperforms the other two systems when given relatively small amounts of training data.