ArticleBioData mining2016
Prediction of donor splice sites using random forest with a new sequence encoding approach.
Article in BioData mining, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 53 citations in OpenAlex.
- Development of a Peptide-Based Multiepitope Vaccine from the SARS-CoV-2 Spike Protein for Targeted Immune Response Against COVID-19.Protein and peptide letters · 2025Article
- DRANetSplicer: A Splice Site Prediction Model Based on Deep Residual Attention Networks.Genes · 2024Article
- A hybrid approach of ensemble learning and grey wolf optimizer for DNA splice junction prediction.PloS one · 2024Article
- Apoptin NLS2 homodimerization strategy for improved antibacterial activity and bio-stability.Amino acids · 2023Article
- An automated framework for evaluation of deep learning models for splice site predictions.Scientific reports · 2023Article
- Spliceator: multi-species splice site prediction using convolutional neural networks.BMC bioinformatics · 2021Article
- Improved recognition of splice sites in3 Biotech · 2021Article
- mLoc-mRNA: predicting multiple sub-cellular localization of mRNAs using random forest algorithm coupled with feature selection via elastic net.BMC bioinformatics · 2021Article
- DASSI: differential architecture search for splice identification from DNA sequences.BioData mining · 2021Article
- Splice2Deep: An ensemble of deep convolutional neural networks for improved splice site prediction in genomic DNA.Gene: X · 2020Article
- Article
- A high-performance approach for predicting donor splice sites based on short window size and imbalanced large samples.Biology direct · 2019Article
- funbarRF: DNA barcode-based fungal species prediction using multiclass Random Forest supervised learning model.BMC genetics · 2019Article
- Assessing the Performances of Protein Function Prediction Algorithms from the Perspectives of Identification Accuracy and False Discovery Rate.International journal of molecular sciences · 2018Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDetection of splice sites plays a key role for predicting the gene structure and thus development of efficient analytical methods for splice site prediction is vital. This paper presents a novel sequence encoding approach based on the adjacent di-nucleotide dependencies in which the donor splice site motifs are encoded into numeric vectors. The encoded vectors are then used as input in Random Forest (RF), Support Vector Machines (SVM) and Artificial Neural Network (ANN), Bagging, Boosting, Logistic regression, kNN and Naïve Bayes classifiers for prediction of donor splice sites.
resultsThe performance of the proposed approach is evaluated on the donor splice site sequence data of Homo sapiens, collected from Homo Sapiens Splice Sites Dataset (HS3D). The results showed that RF outperformed all the considered classifiers. Besides, RF achieved higher prediction accuracy than the existing methods viz., MEM, MDD, WMM, MM1, NNSplice and SpliceView, while compared using an independent test dataset.
conclusionBased on the proposed approach, we have developed an online prediction server (MaLDoSS) to help the biological community in predicting the donor splice sites. The server is made freely available at http://cabgrid.res.in:8080/maldoss. Due to computational feasibility and high prediction accuracy, the proposed approach is believed to help in predicting the eukaryotic gene structure.
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