Prosnet: Integrating Homology With Molecular Networks for Protein Function Prediction

Wang, Sheng; Qu, Meng; Peng, Jian

doi:10.1142/9789813207813_0004

Cited by 12 publications

(9 citation statements)

References 32 publications

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“…Li et al (2017b) proposes a prediction of pathogenic human genes using network embedding. Network embedding is very popular method in protein-protein interaction assessment and function prediction (Kulmanov, Khan & Hoehndorf, 2018;Su et al, 2020;Wang, Qu & Peng, 2017b). Shen et al (2017) and Li et al (2017a) applies to miRNA-disease interaction network to associate genes with complex diseases.…”

Section: Biomedical Data Sciencementioning

confidence: 99%

Survey on graph embeddings and their applications to machine learning problems on graphs

Makarov

Kiselev

Nikitinsky

et al. 2021

PeerJ Computer Science

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Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of literature. So-called graph embeddings provide a powerful tool to construct vectorized feature spaces for graphs and their components, such as nodes, edges and subgraphs under preserving inner graph properties. Using the constructed feature spaces, many machine learning problems on graphs can be solved via standard frameworks suitable for vectorized feature representation. Our survey aims to describe the core concepts of graph embeddings and provide several taxonomies for their description. First, we start with the methodological approach and extract three types of graph embedding models based on matrix factorization, random-walks and deep learning approaches. Next, we describe how different types of networks impact the ability of models to incorporate structural and attributed data into a unified embedding. Going further, we perform a thorough evaluation of graph embedding applications to machine learning problems on graphs, among which are node classification, link prediction, clustering, visualization, compression, and a family of the whole graph embedding algorithms suitable for graph classification, similarity and alignment problems. Finally, we overview the existing applications of graph embeddings to computer science domains, formulate open problems and provide experiment results, explaining how different networks properties result in graph embeddings quality in the four classic machine learning problems on graphs, such as node classification, link prediction, clustering and graph visualization. As a result, our survey covers a new rapidly growing field of network feature engineering, presents an in-depth analysis of models based on network types, and overviews a wide range of applications to machine learning problems on graphs.

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Section: Biomedical Data Sciencementioning

confidence: 99%

Survey on graph embeddings and their applications to machine learning problems on graphs

Makarov

Kiselev

Nikitinsky

et al. 2021

PeerJ Computer Science

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show abstract

“…Compared with other biological data, most species have only sequence data [17], [19]. Some methods utilizes sequence data to establish the cross-species function transfer bridges [19], [23]. PSI-BLAST [14] is widely-used to measure the sequence similarity between proteins from amino acids, which can be used to construct a weighted interspecies network of proteins and enables functional knowledge transfer between species.…”

Section: Constructing Inter-species Network For Function Transfer Bementioning

confidence: 99%

“…But for two species with low homology, integrating the annotations does not bring in a significant improvement. Wang et al [23] introduced an approach called ProSNet, which first builds an integrated heterogeneous network to include molecular networks of multiple species and link together homologous proteins based on sequence data. Next, ProSNet samples a large number of heterogeneous paths on the heterogeneous network to find the low-dimensional vector for each node.…”

Section: Introductionmentioning

confidence: 99%

Cross-Species Protein Function Prediction with Asynchronous-Random Walk

Zhao

Wang

Guo

et al. 2021

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Protein function prediction is a fundamental task in the postgenomic era. Available functional annotations of proteins are incomplete and the annotations of two homologous species are complementary to each other. However, how to effectively leverage mutually complementary annotations of different species to further boost the prediction performance is still not well studied. In this paper, we propose a cross-species protein function prediction approach by performing Asynchronous Random Walk on a heterogeneous network (AsyRW). AsyRW firstly constructs a heterogeneous network to integrate multiple functional association networks derived from different biological data, established homology-relationships between proteins from different species, known annotations of proteins and Gene Ontology (GO). To account for the intrinsic structures of intra-and inter-species of proteins and that of GO, AsyRW quantifies the individual walk lengths of each network node using the gravity-like theory and performs asynchronousrandom walk with the individual length to predict associations between proteins and GO terms. Experiments on annotations archived in different years show that individual walk length and asynchronous-random walk can effectively leverage the complementary annotations of different species, AsyRW has a significantly improved performance to other related and competitive methods. The codes of AsyRW are available at: http://mlda.swu.edu.cn/codes.php?name=AsyRW.

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“…Moreover, sequence information are completely ignored. More recently ProSNet was proposed to integrate both sequence homology and molecular network information of 5 species for constructing a large heterogeneous network to improve the performance of AFP [23]. Due to its high complexity in constructing and training a global heterogeneous network, it would be infeasible for ProSNet to incorporate the network of hundreds of species or more at the same time.…”

Section: Related Workmentioning

confidence: 99%

NetGO: Improving Large-scale Protein Function Prediction with Massive Network Information

You

Yao

Huang

et al. 2018

Preprint

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Automated function prediction (AFP) of proteins is of great significance in biology. In essence, AFP is a large-scale multi-label classification over pairs of proteins and GO terms. Existing AFP approaches, however, have their limitations on both sides of proteins and GO terms. Using various sequence information and the robust learning to rank (LTR) framework, we have developed GOLabeler, a state-of-the-art approach of CAFA3, which overcomes the limitation of the GO term side, such as imbalanced GO terms. Unfortunately, for the protein side issue, available abundant protein information, except for sequences, have not been effectively used for large-scale AFP in CAFA. We propose NetGO that is able to improve large-scale AFP with massive network information. The novelties of NetGO have threefold in using network information: 1) the powerful LTR framework of NetGO efficiently and effectively integrates both sequence and network information, which can easily make large-scale AFP; 2) NetGO can use whole and massive network information of all species (>2000) in STRING (other than only high confidence links and/or some specific species); and 3) NetGO can still use network information to annotate a protein by homology transfer even if it is not covered in STRING. Under numerous experimental settings, we examined the performance of NetGO, such as general performance comparison, species-specific prediction, and prediction on difficult proteins, by using training and test data separated by time-delayed settings of CAFA. Experimental results have clearly demonstrated that NetGO outperforms GOLabeler, DeepGO, and other compared baseline methods significantly. In addition, several interesting findings from our experiments on NetGO would be useful for future AFP research. * zhusf@fudan.edu.cn 1

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Prosnet: Integrating Homology With Molecular Networks for Protein Function Prediction

Cited by 12 publications

References 32 publications

Survey on graph embeddings and their applications to machine learning problems on graphs

Survey on graph embeddings and their applications to machine learning problems on graphs

Cross-Species Protein Function Prediction with Asynchronous-Random Walk

NetGO: Improving Large-scale Protein Function Prediction with Massive Network Information

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