RPI-SE: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information

Yi, Hai-Cheng; You, Zhu-Hong; Wang, Mei-Neng; Guo, Zhen-Hao; Wang, Yanbin; Zhou, Ji-Ren

doi:10.1186/s12859-020-3406-0

Cited by 39 publications

(20 citation statements)

References 43 publications

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“…In other research areas, stacking has been applied to date prediction, protein-protein interaction prediction, credit scoring, cancer detection, etc. ( Wang et al, 2011 ; Wang Y. et al, 2019 ; Sun and Trevor, 2018 ; Yi et al, 2020 ). However, the application of stacking in GS has rarely been reported.…”

Section: Introductionmentioning

confidence: 99%

A Stacking Ensemble Learning Framework for Genomic Prediction

Liang

Chang

et al. 2021

Front. Genet.

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Machine learning (ML) is perhaps the most useful tool for the interpretation of large genomic datasets. However, the performance of a single machine learning method in genomic selection (GS) is currently unsatisfactory. To improve the genomic predictions, we constructed a stacking ensemble learning framework (SELF), integrating three machine learning methods, to predict genomic estimated breeding values (GEBVs). The present study evaluated the prediction ability of SELF by analyzing three real datasets, with different genetic architecture; comparing the prediction accuracy of SELF, base learners, genomic best linear unbiased prediction (GBLUP) and BayesB. For each trait, SELF performed better than base learners, which included support vector regression (SVR), kernel ridge regression (KRR) and elastic net (ENET). The prediction accuracy of SELF was, on average, 7.70% higher than GBLUP in three datasets. Except for the milk fat percentage (MFP) traits, of the German Holstein dairy cattle dataset, SELF was more robust than BayesB in all remaining traits. Therefore, we believed that SEFL has the potential to be promoted to estimate GEBVs in other animals and plants.

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Section: Introductionmentioning

confidence: 99%

A Stacking Ensemble Learning Framework for Genomic Prediction

Liang

Chang

et al. 2021

Front. Genet.

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“…In order to comprehensively evaluate the performance of our model, we follow the widely used evaluation indicators and strategies [ 54 , 55 ]. The tenfold cross-validation was applied to evaluate the performance of DDIPred.…”

Section: Materials and Methodologymentioning

confidence: 99%

In silico drug repositioning using deep learning and comprehensive similarity measures

You

Wang

et al. 2021

BMC Bioinformatics

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Background Drug repositioning, meanings finding new uses for existing drugs, which can accelerate the processing of new drugs research and development. Various computational methods have been presented to predict novel drug–disease associations for drug repositioning based on similarity measures among drugs and diseases. However, there are some known associations between drugs and diseases that previous studies not utilized. Methods In this work, we develop a deep gated recurrent units model to predict potential drug–disease interactions using comprehensive similarity measures and Gaussian interaction profile kernel. More specifically, the similarity measure is used to exploit discriminative feature for drugs based on their chemical fingerprints. Meanwhile, the Gaussian interactions profile kernel is employed to obtain efficient feature of diseases based on known disease-disease associations. Then, a deep gated recurrent units model is developed to predict potential drug–disease interactions. Results The performance of the proposed model is evaluated on two benchmark datasets under tenfold cross-validation. And to further verify the predictive ability, case studies for predicting new potential indications of drugs were carried out. Conclusion The experimental results proved the proposed model is a useful tool for predicting new indications for drugs or new treatments for diseases, and can accelerate drug repositioning and related drug research and discovery.

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“…By doing this, the stacked ensemble classifier can achieve the purpose of minimizing the error rate of the prediction model. At present, this method has been applied to predict ncRNA-protein interactions [57], Bacterial Type IV Secreted Effectors [58], anticancer drug response [59], MicroRNA automatic classification [60] and etc. In this paper, a stacked ensemble classifier which including two stages of learning is used to predict DBPs.…”

Section: Stacked Ensemble Classifiermentioning

confidence: 99%

StackPDB: Predicting DNA-binding proteins based on XGB-RFE feature optimization and stacked ensemble classifier

Zhang

Liu

Wang

et al. 2021

Applied Soft Computing

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DNA binding proteins (DBPs) not only play an important role in all aspects of genetic activities such as DNA replication, recombination, repair, and modification but also are used as key components of antibiotics, steroids, and anticancer drugs in the field of drug discovery. Identifying DBPs becomes one of the most challenging problems in the domain of proteomics research.Considering the high-priced and inefficient of the experimental method, constructing a detailed DBPs prediction model becomes an urgent problem for researchers. In this paper, we propose a stacked ensemble classifier based method for predicting DBPs called StackPDB. Firstly, pseudo amino acid composition (PseAAC), pseudo position-specific scoring matrix (PsePSSM), position-specific scoring matrix-transition probability composition (PSSM-TPC), evolutionary distance transformation (EDT), and residue probing transformation (RPT) are applied to extract protein sequence features. Secondly, extreme gradient boosting-recursive feature elimination (XGB-RFE) is employed to gain an excellent feature subset. Finally, the best features are applied to the stacked ensemble classifier composed of XGBoost, LightGBM, and SVM to construct StackPDB. After applying leave-one-out cross-validation (LOOCV), StackPDB obtains high ACC and MCC on PDB1075，93.44% and 0.8687, respectively. Besides, the ACC of the independent test datasets PDB186 and PDB180 are 84.41% and 90.00%, respectively. The MCC of the independent test datasets PDB186 and PDB180 are 0.6882 and 0.7997, respectively. The results on the training dataset and the independent test dataset show that StackPDB has a great predictive  Corresponding author.

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RPI-SE: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information

Cited by 39 publications

References 43 publications

A Stacking Ensemble Learning Framework for Genomic Prediction

A Stacking Ensemble Learning Framework for Genomic Prediction

In silico drug repositioning using deep learning and comprehensive similarity measures

StackPDB: Predicting DNA-binding proteins based on XGB-RFE feature optimization and stacked ensemble classifier

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