Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction

Lv, Guofeng; Hu, Zhiqiang; Bi, Yanguang; Zhang, Shaoting

doi:10.24963/ijcai.2021/506

Cited by 33 publications

(34 citation statements)

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“…In the SHS148k dataset, the number of negative samples is 44,488. At the same time, inspired by [26], in order to evaluate the generalization ability of the LDMGNN model more realistically, we choose three partition schemes to divide the test set, i.e., random, BFS and DFS. Our test set accounts for 20% of the dataset.…”

Section: Datasetsmentioning

confidence: 99%

“…The interactions between protein-protein pairs have at least one label. Moreover, the types of PPIs in the SHS27k and SHS148k datasets are extremely unbalanced [26]. Micro-F1 will emphasize the common labels in the datasets, which is not easy to be affected by small samples or large samples, so that each sample has the same importance [32].…”

Section: Parameter Settings and Evaluation Metricmentioning

confidence: 99%

“…Inspired by the methods of graph signal processing, Colonnese et al [25] considered node features on PPI networks as signals, and developed a Markov model to accomplish PPIs prediction. Lv et al [26] constructed a GNN-PPI model based on graph isomorphism network (GIN) to predict the interactions between protein-protein pairs. It not only considered the amino acid sequence information, but also fully considered the correlation between proteins.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Datasetsmentioning

confidence: 99%

Section: Parameter Settings and Evaluation Metricmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Protein-protein interactions (PPI) are physical contacts of high specificity established between two or more protein molecules; we regard PPI as a sequence classification task and use three datasets with different sizes for evaluation. STRING is built by Lv et al (2021), which contains 15,335 proteins and 593,397 PPIs. We also use SHS27k and SHS148k, which are generated by .…”

Section: Downstream Task Datasetmentioning

confidence: 99%

“…PIPR (Chen et al ( 2019)), DNN-PPI (Li et al (2018)) and DPPI (Hashemifar et al (2018)) are deep learning based methods. GNN-PPI (Lv et al (2021)) is a graph neural network based method for better inter-novel-protein interaction prediction. To evaluate our OntoProtein, we replace the initial protein embedding part of GNN-PPI with ProtBERT and OntoProtein as baselines.…”

Section: Protein-protein Interactionmentioning

confidence: 99%

OntoProtein: Protein Pretraining With Gene Ontology Embedding

Zhang¹,

Bi²,

Liang³

et al. 2022

Preprint

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Self-supervised protein language models have proved their effectiveness in learning the proteins representations. With the increasing computational power, current protein language models pre-trained with millions of diverse sequences can advance the parameter scale from million-level to billion-level and achieve remarkable improvement. However, those prevailing approaches rarely consider incorporating knowledge graphs (KGs), which can provide rich structured knowledge facts for better protein representations. We argue that informative biology knowledge in KGs can enhance protein representation with external knowledge. In this work, we propose OntoProtein, the first general framework that makes use of structure in GO (Gene Ontology) into protein pre-training models. We construct a novel large-scale knowledge graph that consists of GO and its related proteins, and gene annotation texts or protein sequences describe all nodes in the graph. We propose novel contrastive learning with knowledge-aware negative sampling to jointly optimize the knowledge graph and protein embedding during pre-training. Experimental results show that OntoProtein can surpass state-of-the-art methods with pre-trained protein language models in TAPE benchmark and yield better performance compared with baselines in protein-protein interaction and protein function prediction 1 .

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Co-training based prediction of multi-label protein–protein interactions

Tang,

Zhang,

et al. 2024

Computers in Biology and Medicine

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Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction

Cited by 33 publications

References 0 publications

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

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

OntoProtein: Protein Pretraining With Gene Ontology Embedding

Co-training based prediction of multi-label protein–protein interactions

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