MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features

By Peng Jiang, Haonan Wu, Wenkai Wang, Wei Ma, Xiao Sun, Zuhong Lu. Nucleic Acids Res., 2007

542 citations recorded by the source corpus.

To distinguish the real pre-miRNAs from other hairpin sequences with similar stem-loops (pseudo pre-miRNAs), a hybrid feature which consists of local contiguous structure-sequence composition, minimum of free energy (MFE) of the secondary structure and P-value of randomization test is used. Besides, a novel machine-learning algorithm, random forest (RF), is introduced. The results suggest that our method predicts at 98.21% specificity and 95.09% sensitivity. When compared with the previous study, Triplet-SVM-classifier, our RF method was nearly 10% greater in total accuracy. Further analysis indicated that the improvement was due to both the combined features and the RF algorithm. The MiPred web server is available at http://www.bioinf.seu.edu.cn/miRNA/. Given a sequence, MiPred decides whether it is a pre-miRNA-like hairpin sequence or not. If the sequence is a pre-miRNA-like hairpin, the RF classifier will predict whether it is a real pre-miRNA or a pseudo one.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/55117305058b86c6ccf4a8c02308225151c4c2a1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

1. X. Chen
2. David Langenberger
3. Sungroh Yoon
4. Seunghyun Park
5. Amir Hussain
6. Mufti Mahmud
7. M. S. Kaiser

In Molecular Biology

1. Sungroh Yoon
2. Juan Manuel Falcón-Pérez
3. Martin Sturm
4. X. Chen
5. Marco A. Marra
6. Michael Hackenberg
7. Ana María Aransay
8. David Langenberger

Citations in the corpus

• miRanalyzer: a microRNA detection and analysis tool for next-generation sequencing experiments (2009, methodology): “(10) using basically the triplet structure features introduced by Xue et al.”
• deepMiRGene: Deep Neural Network based Precursor microRNA Prediction (2016, methodology): “MiPred (Jiang et al., 2007), microPred (Batuwita & Palade, 2009), triplet-SVM (Xue et al.”
• Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells. (2008, methodology): “MiPred relies on a Random Forest (RF) algorithm to classify miRNAs based on structural properties (Jiang et al. 2007).”
• Random Forests for Genomic Data Analysis (2012, background): “miRNAs like hairpins, and it achieved high specificity and sensitivity [33].”