DM-RPIs: Predicting ncRNA-protein interactions using stacked ensembling strategy

Cheng, Shiduan; Zhang, Lu; Tan, Jianjun; Gong, Weikang; Li, Chunhua; Zhang, Xiaoyi

doi:10.1016/j.compbiolchem.2019.107088

Cited by 20 publications

(10 citation statements)

References 30 publications

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“…2, the k-mer aptamer frequency implies that the k-mer usage is an essential factor for APIs. This finding is justified by the previous studies [87][88][89][90][91][92] which proved k-mer frequency plays an important role in interaction related to riboswitch, DNA, RNA, ncRNA, lncRNA, etc. This may be due to aptamers in this study were considered as the type of RNA and DNA.…”

Section: Discussionsupporting

confidence: 53%

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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Section: Discussionsupporting

confidence: 53%

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

Emami

Ferdousi

2021

Sci Rep

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“…Most existing studies extracted ncRNA and protein sequence features by using a simple k-mer: 3-mer frequency feature for protein and 4-mer frequency feature for ncRNA [22,24,26,29,33]. For protein, 20 amino acids can be classified into seven groups based on their dipole moments and side-chain volume: 1 =AA, G, V,, 2 =AI, L, F, P,, =AY, M, T, S,, 4 =AH, N, Q, W,, 5 =AR, K,, 6 =AD, E, and 7 ={C} [33].…”

Section: Sequence Codingmentioning

confidence: 99%

“…Besides, Wang et al utilized the deep convolutional neural network (CNN) to learn high-level features from the RNA and protein sequences, further feeding them into an extreme learning machine (ELM) for classification [23]. Furthermore, our group designed DM-RPIs, a classifier integrated SVM, RF, and CNN to classify ncRPIs by learning the discriminative features from 3-mer and 4-mer frequency of proteins and ncRNAs, respectively [24]. In addition, LightGBM, rpiCOOL, RPIFSE, RPI-SAN, and LPI-CNNCP also made ncRNA-protein interaction(ncRPI) predictions based on primary sequence [25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

EDLMFC: An Ensemble Deep Learning Framework with Multi-scale Features Combination for ncRNA-protein Interaction Prediction

Wang

Zhao

Gong

et al. 2021

Preprint

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Background: Non-coding RNA (ncRNA) and protein interactions play essential roles in various physiological and pathological processes. The experimental methods used for predicting ncRNA-protein interactions are time-consuming and labor-intensive. Therefore, there is an increasing demand for computational methods to accurately and efficiently predict ncRNA-protein interactions.Results: In this work, we presented an ensemble deep learning-based method, EDLMFC, to predict ncRNA-protein interactions using the combination of multi-scale features, including primary sequence features, secondary structure sequence features, and tertiary structure features. Conjoint k-mer was used to extract protein/ncRNA sequence features, integrating tertiary structure features, then fed into an ensemble deep learning model, which combined convolutional neural network (CNN) to learn dominating biological information with bi-directional long short-term memory network (BLSTM) to capture long-range dependencies among the features identified by the CNN. Compared with other state-of-the-art methods under 5-fold cross-validation, EDLMFC shows the best performance with accuracy of 94.3% and 90.0% on RPI1807 and NPInter v2.0 datasets, respectively. The results of the independent test demonstrated that EDLMFC can effectively predict potential ncRNA-protein interactions from different organisms. Furtherly, EDLMFC is also shown to predict hub ncRNAs and proteins presented in ncRNA-protein networks of Mus musculus successfully.Conclusions: In general, our proposed method EDLMFC improved the accuracy of ncRNA-protein interaction predictions and anticipated providing some helpful guidance on ncRNA functions research.The source code of EDLMFC and the datasets used in this work are available at https://github.com/JingjingWang-87/EDLMFC.

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“…• each protein sequence is treated as a sentence, made by overlapping words (k-mers) to incorporate some context-order information in the resulting distributed representation; • the word size is 3, which seems to work properly to embed amino acid sequences for biological tasks (S. Cheng et al, 2019, Yi et al (2020); • the sequence vector is defined as the arithmetic mean of all its word vectors.…”

Section: Continuous Distributed Representations For Protein Sequencesmentioning

confidence: 99%

Determining a novel feature-space for SARS-CoV-2 sequence data

Ballesio¹,

Bangash²,

Barradas-Bautista³

et al. 2020

Preprint

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The pandemicity & the ability of the SARS-COV-2 to reinfect a cured subject, among other damaging characteristics of it, took everybody by surprise. A global collaborative scientific effort was direly required to bring learned people from different niches of medicine & data science together. Such a platform was provided by COVID19 Virtual BioHackathon, organized from the 5th to the 11th of April, 2020, to ponder on the related pressing issues varying in their diversity from text mining to genomics. Under the "Machine learning" track, we determined optimal k-mer length for feature extraction, constructed continuous distributed representations for protein sequences to create phylogenetic trees in an alignment-free manner, and clustered predicted MHC class I and II binding affinity to aid in vaccine design. All the related work in available in a Github repository under an MIT license for future research.

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DM-RPIs: Predicting ncRNA-protein interactions using stacked ensembling strategy

Cited by 20 publications

References 30 publications

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

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

EDLMFC: An Ensemble Deep Learning Framework with Multi-scale Features Combination for ncRNA-protein Interaction Prediction

Determining a novel feature-space for SARS-CoV-2 sequence data

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