LPI-IBNRA: Long Non-coding RNA-Protein Interaction Prediction Based on Improved Bipartite Network Recommender Algorithm

Xie, Guobo; Wu, Cuiming; Sun, Yeping; Fan, Zhiliang; Liu, Jianghui

doi:10.3389/fgene.2019.00343

Cited by 34 publications

(15 citation statements)

References 69 publications

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“…In our study, we compared LPI-SKF with two popular algorithms, RWR (random walk with restart) and CF (collaborative filtering), and three other methods, including LPIHN (Li et al, 2015 ), LPBNI (Ge et al, 2016 ), and LPI-IBNRA (Xie et al, 2019 ). We built six prediction models based on the same benchmarking dataset.…”

Section: Resultsmentioning

confidence: 99%

LPI-SKF: Predicting lncRNA-Protein Interactions Using Similarity Kernel Fusions

Zhou

Shen

et al. 2020

Front. Genet.

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Long non-coding RNAs (lncRNAs) play an important role in serval biological activities, including transcription, splicing, translation, and some other cellular regulation processes. lncRNAs perform their biological functions by interacting with various proteins. The studies on lncRNA-protein interactions are of great value to the understanding of lncRNA functional mechanisms. In this paper, we proposed a novel model to predict potential lncRNA-protein interactions using the SKF (similarity kernel fusion) and LapRLS (Laplacian regularized least squares) algorithms. We named this method the LPI-SKF. Various similarities of both lncRNAs and proteins were integrated into the LPI-SKF. LPI-SKF can be applied in predicting potential interactions involving novel proteins or lncRNAs. We obtained an AUROC (area under receiver operating curve) of 0.909 in a 5-fold cross-validation, which outperforms other state-of-the-art methods. A total of 19 out of the top 20 ranked interaction predictions were verified by existing data, which implied that the LPI-SKF had great potential in discovering unknown lncRNA-protein interactions accurately. All data and codes of this work can be downloaded from a GitHub repository (https://github.com/zyk2118216069/LPI-SKF).

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Section: Resultsmentioning

confidence: 99%

LPI-SKF: Predicting lncRNA-Protein Interactions Using Similarity Kernel Fusions

Zhou

Shen

et al. 2020

Front. Genet.

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“…The parameters were set based on our several feature selection experiments. Roc curves have been broadly used in machine learning and deep learning approaches for performance evaluation 86,[103][104][105][106] . Therefore, as a popular method for performance evaluation, the ROC curve was utilized in our experiments.…”

Section: Discussionmentioning

confidence: 99%

AptaNet as a deep learning approach for aptamer–protein interaction prediction

Emami

Ferdousi

2021

Sci Rep

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Aptamers are short oligonucleotides (DNA/RNA) or peptide molecules that can selectively bind to their specific targets with high specificity and affinity. As a powerful new class of amino acid ligands, aptamers have high potentials in biosensing, therapeutic, and diagnostic fields. Here, we present AptaNet—a new deep neural network—to predict the aptamer–protein interaction pairs by integrating features derived from both aptamers and the target proteins. Aptamers were encoded by using two different strategies, including k-mer and reverse complement k-mer frequency. Amino acid composition (AAC) and pseudo amino acid composition (PseAAC) were applied to represent target information using 24 physicochemical and conformational properties of the proteins. To handle the imbalance problem in the data, we applied a neighborhood cleaning algorithm. The predictor was constructed based on a deep neural network, and optimal features were selected using the random forest algorithm. As a result, 99.79% accuracy was achieved for the training dataset, and 91.38% accuracy was obtained for the testing dataset. AptaNet achieved high performance on our constructed aptamer-protein benchmark dataset. The results indicate that AptaNet can help identify novel aptamer–protein interacting pairs and build more-efficient insights into the relationship between aptamers and proteins. Our benchmark dataset and the source codes for AptaNet are available in: https://github.com/nedaemami/AptaNet.

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“…Step 3 Inferring novel LPIs by combining the HeteSim algorithm and heterogeneous LPI network. Xie et al (2019) developed a LPI prediction model, LPI-IBNRA. LPI-IBNRA integrated lncRNA-protein interactions, proteinprotein interactions, and similarity matrix for proteins and lncRNAs, and improved bipartite network recommender algorithm.…”

Section: Lpihnmentioning

confidence: 99%

Probing lncRNA–Protein Interactions: Data Repositories, Models, and Algorithms

Peng

Liu

Yang³

et al. 2020

Front. Genet.

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Identifying lncRNA-protein interactions (LPIs) is vital to understanding various key biological processes. Wet experiments found a few LPIs, but experimental methods are costly and time-consuming. Therefore, computational methods are increasingly exploited to capture LPI candidates. We introduced relevant data repositories, focused on two types of LPI prediction models: network-based methods and machine learning-based methods. Machine learning-based methods contain matrix factorization-based techniques and ensemble learning-based techniques. To detect the performance of computational methods, we compared parts of LPI prediction models on Leave-One-Out cross-validation (LOOCV) and fivefold cross-validation. The results show that SFPEL-LPI obtained the best performance of AUC. Although computational models have efficiently unraveled some LPI candidates, there are many limitations involved. We discussed future directions to further boost LPI predictive performance.Keywords: lncRNA-protein interaction, computational method, network-based method, machine learning-based method, data repositories

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LPI-IBNRA: Long Non-coding RNA-Protein Interaction Prediction Based on Improved Bipartite Network Recommender Algorithm

Cited by 34 publications

References 69 publications

LPI-SKF: Predicting lncRNA-Protein Interactions Using Similarity Kernel Fusions

LPI-SKF: Predicting lncRNA-Protein Interactions Using Similarity Kernel Fusions

AptaNet as a deep learning approach for aptamer–protein interaction prediction

Probing lncRNA–Protein Interactions: Data Repositories, Models, and Algorithms

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