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MVSLLnc: LncRNA subcellular localization prediction based on multi-source features and two-stage voting strategy.

Sheng Wang1, Zu-Guo Yu1, Guo-Sheng Han1

  • 1National Center for Applied Mathematics in Hunan, Xiangtan University, Hunan 411105, China; Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Hunan 411105, China.

Methods (San Diego, Calif.)
|January 21, 2025
PubMed
Summary

This study introduces MVSLLnc, a novel computational model for predicting long non-coding RNA subcellular localization. The method efficiently integrates multi-source features using a two-stage voting strategy, offering a simpler alternative to experimental techniques.

Keywords:
Chaos Game RepresentationLong non-coding RNAPhysicochemical propertySubcellular localization predictionTwo-stage voting strategy

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Area of Science:

  • Bioinformatics
  • Molecular Biology
  • Computational Biology

Background:

  • Understanding long non-coding RNA (lncRNA) function requires accurate prediction of their subcellular localization.
  • Traditional experimental methods for lncRNA localization are time-consuming.
  • Existing computational methods may require substantial computing resources.

Purpose of the Study:

  • To develop a simple, efficient, and easy-to-implement computational model for predicting lncRNA subcellular localization.
  • To improve upon existing methods by utilizing multi-source features and a novel voting strategy.

Main Methods:

  • Proposed the MVSLLnc model, integrating k-mer frequency, Chaos Game Representation (CGR) coordinates, and physicochemical properties (PhyChe).
  • Employed a two-stage voting strategy combining Random Forest (RF), Support Vector Machine (SVM), and XGBoost classifiers.
  • Validated the model on benchmark and independent test datasets.

Main Results:

  • Achieved high accuracy on benchmark datasets (0.829, 0.793, 0.968).
  • Demonstrated competitive performance on independent test sets (0.642, 0.737, 0.518).
  • Ablation studies confirmed the effectiveness of the two-stage voting strategy and multi-source feature integration.

Conclusions:

  • The MVSLLnc model provides an efficient and robust approach for predicting lncRNA subcellular localization.
  • The proposed method effectively leverages diverse features and multiple classifiers for improved prediction accuracy.
  • MVSLLnc offers a valuable tool for lncRNA research, complementing experimental approaches.