HPODNets: deep graph convolutional networks for predicting human protein–phenotype associations

Liu, Lizhi; Mamitsuka, Hiroshi; Zhu, Shanfeng

doi:10.1093/bioinformatics/btab729

Cited by 5 publications

(2 citation statements)

References 54 publications

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“…For instance, PPIs are essential for biological cell activities such as cell proliferation, immune response, signal transduction, DNA transcription, and replication [5]. Therefore, exploring the interactions between protein and protein is the key to study cell biology [6][7][8] and has great significance to the diagnosis and treatment of diseases, as well as the design and development of drugs [9]. At present, there are many methods for the prediction of PPIs, which can be broadly divided into two types: laboratory-based traditional methods and deep learning methods.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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“…Given a single query gene, GeneMANIA finds genes likely to share function with the query gene based on their interactions with it. Recently, HPODNets presents a deep GCN architecture to capture high-order topological information from multiple protein–protein interaction networks [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Integration of Human Protein Sequence and Protein-Protein Interaction Data by Graph Autoencoder to Identify Novel Protein-Abnormal Phenotype Associations

Liu

et al. 2022

Cells

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Understanding gene functions and their associated abnormal phenotypes is crucial in the prevention, diagnosis and treatment against diseases. The Human Phenotype Ontology (HPO) is a standardized vocabulary for describing the phenotype abnormalities associated with human diseases. However, the current HPO annotations are far from completion, and only a small fraction of human protein-coding genes has HPO annotations. Thus, it is necessary to predict protein-phenotype associations using computational methods. Protein sequences can indicate the structure and function of the proteins, and interacting proteins are more likely to have same function. It is promising to integrate these features for predicting HPO annotations of human protein. We developed GraphPheno, a semi-supervised method based on graph autoencoders, which does not require feature engineering to capture deep features from protein sequences, while also taking into account the topological properties in the protein–protein interaction network to predict the relationships between human genes/proteins and abnormal phenotypes. Cross validation and independent dataset tests show that GraphPheno has satisfactory prediction performance. The algorithm is further confirmed on automatic HPO annotation for no-knowledge proteins under the benchmark of the second Critical Assessment of Functional Annotation, 2013–2014 (CAFA2), where GraphPheno surpasses most existing methods. Further bioinformatics analysis shows that predicted certain phenotype-associated genes using GraphPheno share similar biological properties with known ones. In a case study on the phenotype of abnormality of mitochondrial respiratory chain, top prioritized genes are validated by recent papers. We believe that GraphPheno will help to reveal more associations between genes and phenotypes, and contribute to the discovery of drug targets.

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GMAT: A Graph Modeling Method for Group Preference Prediction

Li,

Guo,

Chen

2024

J. Syst. Sci. Syst. Eng.

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HPODNets: deep graph convolutional networks for predicting human protein–phenotype associations

Cited by 5 publications

References 54 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

Integration of Human Protein Sequence and Protein-Protein Interaction Data by Graph Autoencoder to Identify Novel Protein-Abnormal Phenotype Associations

GMAT: A Graph Modeling Method for Group Preference Prediction

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