LPI-DeepGBDT: A Multiple-Layer Deep Framework based on Gradient Boosting Decision Trees for lncRNA-Protein Interaction Identification

ZhouZhou, Liqian; Wang, Zhao; Tian, Xiongfei; Peng, Linfeng

doi:10.21203/rs.3.rs-477640/v1

Cited by 2 publications

(3 citation statements)

References 42 publications

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“…Gradient boosting machine (Friedman, 2001 ; ZhouZhou et al, 2021 ) is a function estimation approach based on numerical optimization and obtains wide application (Peng et al, 2021 ). Grownet (Badirli et al, 2020 ) is a gradient boosting framework with shallow neural networks.…”

Section: Methodsmentioning

confidence: 99%

Identifying potential drug-target interactions based on ensemble deep learning

Zhou

Wang

Peng

et al. 2023

Front. Aging Neurosci.

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IntroductionDrug-target interaction prediction is one important step in drug research and development. Experimental methods are time consuming and laborious.MethodsIn this study, we developed a novel DTI prediction method called EnGDD by combining initial feature acquisition, dimensional reduction, and DTI classification based on Gradient boosting neural network, Deep neural network, and Deep Forest.ResultsEnGDD was compared with seven stat-of-the-art DTI prediction methods (BLM-NII, NRLMF, WNNGIP, NEDTP, DTi2Vec, RoFDT, and MolTrans) on the nuclear receptor, GPCR, ion channel, and enzyme datasets under cross validations on drugs, targets, and drug-target pairs, respectively. EnGDD computed the best recall, accuracy, F1-score, AUC, and AUPR under the majority of conditions, demonstrating its powerful DTI identification performance. EnGDD predicted that D00182 and hsa2099, D07871 and hsa1813, DB00599 and hsa2562, D00002 and hsa10935 have a higher interaction probabilities among unknown drug-target pairs and may be potential DTIs on the four datasets, respectively. In particular, D00002 (Nadide) was identified to interact with hsa10935 (Mitochondrial peroxiredoxin3) whose up-regulation might be used to treat neurodegenerative diseases. Finally, EnGDD was used to find possible drug targets for Parkinson's disease and Alzheimer's disease after confirming its DTI identification performance. The results show that D01277, D04641, and D08969 may be applied to the treatment of Parkinson's disease through targeting hsa1813 (dopamine receptor D2) and D02173, D02558, and D03822 may be the clues of treatment for patients with Alzheimer's disease through targeting hsa5743 (prostaglandinendoperoxide synthase 2). The above prediction results need further biomedical validation.DiscussionWe anticipate that our proposed EnGDD model can help discover potential therapeutic clues for various diseases including neurodegenerative diseases.

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Section: Methodsmentioning

confidence: 99%

Identifying potential drug-target interactions based on ensemble deep learning

Zhou

Wang

Peng

et al. 2023

Front. Aging Neurosci.

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“…Given the sequences of protein and lncRNA, predict the potential interaction between lncRNA and protein. LPI-deepGBDT [ 40 ]: A multiple-layer deep structure model based on gradient boosting decision trees. Given the sequences of protein and lncRNA, predict the unobserved LPIs.…”

Section: Resutsmentioning

confidence: 99%

“…LPI-deepGBDT [ 40 ]: A multiple-layer deep structure model based on gradient boosting decision trees. Given the sequences of protein and lncRNA, predict the unobserved LPIs.…”

Section: Resutsmentioning

confidence: 99%

Long-distance dependency combined multi-hop graph neural networks for protein–protein interactions prediction

Zhang

Chen

et al. 2022

BMC Bioinformatics

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Background Protein–protein interactions are widespread in biological systems and play an important role in cell biology. Since traditional laboratory-based methods have some drawbacks, such as time-consuming, money-consuming, etc., a large number of methods based on deep learning have emerged. However, these methods do not take into account the long-distance dependency information between each two amino acids in sequence. In addition, most existing models based on graph neural networks only aggregate the first-order neighbors in protein–protein interaction (PPI) network. Although multi-order neighbor information can be aggregated by increasing the number of layers of neural network, it is easy to cause over-fitting. So, it is necessary to design a network that can capture long distance dependency information between amino acids in the sequence and can directly capture multi-order neighbor information in protein–protein interaction network. Results In this study, we propose a multi-hop neural network (LDMGNN) model combining long distance dependency information to predict the multi-label protein–protein interactions. In the LDMGNN model, we design the protein amino acid sequence encoding (PAASE) module with the multi-head self-attention Transformer block to extract the features of amino acid sequences by calculating the interdependence between every two amino acids. And expand the receptive field in space by constructing a two-hop protein–protein interaction (THPPI) network. We combine PPI network and THPPI network with amino acid sequence features respectively, then input them into two identical GIN blocks at the same time to obtain two embeddings. Next, the two embeddings are fused and input to the classifier for predict multi-label protein–protein interactions. Compared with other state-of-the-art methods, LDMGNN shows the best performance on both the SHS27K and SHS148k datasets. Ablation experiments show that the PAASE module and the construction of THPPI network are feasible and effective. Conclusions In general terms, our proposed LDMGNN model has achieved satisfactory results in the prediction of multi-label protein–protein interactions.

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LPI-DeepGBDT: A Multiple-Layer Deep Framework based on Gradient Boosting Decision Trees for lncRNA-Protein Interaction Identification

Cited by 2 publications

References 42 publications

Identifying potential drug-target interactions based on ensemble deep learning

Identifying potential drug-target interactions based on ensemble deep learning

Long-distance dependency combined multi-hop graph neural networks for protein–protein interactions prediction

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