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LncLocation: Efficient Subcellular Location Prediction of Long Non-Coding RNA-Based Multi-Source Heterogeneous

Shiyao Feng1,2, Yanchun Liang1,2, Wei Du1

  • 1Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.

International Journal of Molecular Sciences
|October 6, 2020
PubMed
Summary

Predicting the subcellular location of long non-coding RNAs (lncRNAs) is crucial for understanding their function. Our new tool, lncLocation, effectively addresses data imbalance challenges for improved lncRNA localization prediction.

Keywords:
logarithm-distance of Hexamermulti-source featuressubcellullar locationthe binomial distribution-based filtering

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • The subcellular localization of long non-coding RNAs (lncRNAs) is critical for elucidating their biological functions.
  • Limited experimentally verified data and imbalanced distribution across organelles present significant challenges for predicting lncRNA localization.
  • Existing prediction methods struggle with the multi-classification, small-sample imbalance problem inherent in lncRNA subcellular localization prediction.

Purpose of the Study:

  • To develop a robust computational tool, lncLocation, for predicting the subcellular location of lncRNAs.
  • To address the challenges posed by small sample sizes and data imbalance in lncRNA localization prediction.
  • To improve the accuracy and reliability of lncRNA subcellular localization prediction using integrated multi-source features.

Main Methods:

  • Integration of multi-source features to construct a sequence-based computational tool.
  • Utilization of Autoencoder for feature enhancement.
  • Application of binomial distribution-based filtering and recursive feature elimination (RFE) for feature selection.
  • Comprehensive experimentation with feature combinations and machine learning models to select optimal parameters.

Main Results:

  • The developed lncLocation tool achieved an accuracy of 87.78% using 5-fold cross-validation on benchmark data.
  • lncLocation outperforms existing state-of-the-art tools in predicting lncRNA subcellular localization.
  • Significant improvements in classification performance were observed, particularly for underrepresented lncRNA classes.

Conclusions:

  • lncLocation provides an effective solution for predicting lncRNA subcellular localization, overcoming data imbalance issues.
  • The integrated feature approach and optimized machine learning model enhance predictive accuracy.
  • This tool advances the field by offering a more reliable method for understanding lncRNA function through localization prediction.