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IPMiner: hidden ncRNA-protein interaction sequential pattern mining with stacked autoencoder for accurate

Xiaoyong Pan1,2, Yong-Xian Fan3, Junchi Yan4

  • 1Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Dongchuan Road, Shanghai, China.

BMC Genomics
|August 11, 2016
PubMed
Summary

We developed IPMiner, a deep learning tool to predict interactions between non-coding RNAs (ncRNAs) and RNA binding proteins (RBPs) from sequence data. IPMiner accurately identifies these crucial biological interactions, outperforming existing methods.

Keywords:
Deep learningStacked ensembingncRNAncRNA-protein

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Non-coding RNAs (ncRNAs) regulate gene expression post-transcriptionally.
  • ncRNA function relies on interactions with RNA binding proteins (RBPs).
  • Identifying specific ncRNA-RBP interactions is challenging due to complex patterns.

Purpose of the Study:

  • To develop a computational method for predicting ncRNA-protein interactions from sequence data.
  • To improve prediction accuracy by leveraging deep learning and ensemble methods.
  • To identify hidden sequential patterns in RNA and protein sequences relevant to interactions.

Main Methods:

  • Proposed IPMiner (Interaction Pattern Miner), a deep learning approach.
  • Utilized stacked autoencoders to learn hidden features from sequence composition.
  • Employed random forest models and stacked ensembling for enhanced prediction.
  • Integrated deep neural networks with stacked ensembling for feature abstraction.

Main Results:

  • IPMiner achieved high performance on a lncRNA-protein interaction dataset (Accuracy: 0.891, MCC: 0.784).
  • Demonstrated superior performance compared to state-of-the-art methods on various RNA-protein datasets, with >20% improvement.
  • Successfully applied IPMiner for large-scale ncRNA-protein network prediction.

Conclusions:

  • IPMiner effectively learns high-level features for accurate RNA-protein interaction detection.
  • The method shows strong discriminant ability using sequence composition features.
  • IPMiner offers a promising tool for understanding ncRNA-protein interactions and networks.