NNLDA

NNLDA is a deep learning-based computational tool to predict potential associations between long non-coding RNAs (lncRNAs) and diseases. The key points about NNLDA are:

It utilizes a deep neural network architecture to learn the complex relationships between lncRNAs and diseases from a large-scale interaction network.

The tool can handle large datasets efficiently by employing mini-batch stochastic gradient descent, making it scalable for extensive studies.

4. NNLDA is the first algorithm to apply deep neural networks for predicting lncRNA-disease associations, introducing a novel approach to this field.

Topic

Functional, regulatory and non-coding RNA;Machine learning;Pathology

Detail

  • Operation: Pathway or network prediction;Relation extraction

  • Software interface: Command-line interface

  • Language: Python

  • License: Not stated

  • Cost: Free of charge

  • Version name: -

  • Credit: National Natural Science Foundation of China, China Postdoctoral Science Foundation, Natural Science Foundation of Shaanxi Province, and Northwestern Polytechnical University.

  • Input: -

  • Output: -

  • Contact: Xuequn Shang shang@nwpu.edu.cn

  • Collection: -

  • Maturity: -

Publications

  • Deep Learning Enables Accurate Prediction of Interplay Between lncRNA and Disease.
  • Hu J, et al. Deep Learning Enables Accurate Prediction of Interplay Between lncRNA and Disease. Deep Learning Enables Accurate Prediction of Interplay Between lncRNA and Disease. 2019; 10:937. doi: 10.3389/fgene.2019.00937
  • https://doi.org/10.3389/FGENE.2019.00937
  • PMID: 31649723
  • PMC: PMC6795129

Download and documentation

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