An efficient method for protein function annotation based on multilayer protein networks

Zhao, Bihai; Hu, Sai; Li, Xueyong; Zhang, Fan; Tian, Qinglong; Ni, Wen-yin

doi:10.1186/s40246-016-0087-x

Cited by 33 publications

(25 citation statements)

References 33 publications

(39 reference statements)

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“…ER-nuclear membrane was the function for YAR028W (Hitchcock et al, 2003). For YAR003W, our method predicted the GO annotation, RB binding protein (Zhao et al, 2016). The reported annotations are collected from BioGRID and SGD databases (Dwight et al, 2002), whereas the predicted functions from our method are validated from the literature as referenced.…”

Section: Pathway Enrichment Analysis and Functional Annotationmentioning

confidence: 99%

Predicting Protein Functions Based on Differential Co-expression and Neighborhood Analysis

Wekesa

Luan

Meng

2021

Journal of Computational Biology

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Proteins are polypeptides essential in biological processes. Protein physical interactions are complemented by other types of functional relationship data including genetic interactions, knowledge about co-expression, and evolutionary pathways. Existing algorithms integrate protein interaction and gene expression data to retrieve context-specific subnetworks composed of genes/proteins with known and unknown functions. However, most protein function prediction algorithms fail to exploit diverse intrinsic information in feature and label spaces. We develop a novel integrative method based on differential Co-expression analysis and Neighbor-voting algorithm for Protein Function Prediction, namely CNPFP. The method integrates heterogeneous data and exploits intrinsic and latent linkages via global iterative approach and genomic features. CNPFP performs three tasks: clustering, differential co-expression analysis, and predicts protein functions. Our aim is to identify yeast cell cycle-specific proteins linked to differentially expressed proteins in the protein-protein interaction network. To capture intrinsic information, CNPFP selects the most relevant feature subset based on global iterative neighbor-voting algorithm. We identify eight condition-specific modules. The most relevant subnetwork has 87 genes highly enriched with cyclin-dependent kinases, a protein kinase relevant for cell cycle regulation. We present comprehensive annotations for 3538 Saccharomyces cerevisiae proteins. Our method achieves an AUROC of 0.9862, accuracy of 0.9710, and F-score of 0.9691. From the results, we can summarize that exploiting intrinsic nature of protein relationships improves the quality of function prediction. Thus, the proposed method is useful in functional genomics studies.

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Section: Pathway Enrichment Analysis and Functional Annotationmentioning

confidence: 99%

Predicting Protein Functions Based on Differential Co-expression and Neighborhood Analysis

Wekesa

Luan

Meng

2021

Journal of Computational Biology

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“…As much, incorporating quality-controlled protein complexes and analysing functional associations are both essential for accurate function annotation. We therefore proposed to construct the protein correlation network Co-Complex, where the functional correlation between two proteins is measured using the Equation (4) [35].…”

Section: Co-complex Networkmentioning

confidence: 99%

NPF: Network propagation for protein function prediction

Zhao

Zhi-hong

Jiang³

et al. 2020

Preprint

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Background: The accurate annotation of protein functions is of great significance in elucidating the phenomena of life, treating disease and developing new medicines. Various methods have been developed to facilitate the prediction of these functions by combining protein interaction networks (PINs) with multi-omics data. However, it is still challenging to make full use of multiple biological to improve the performance of functions annotation.Results: We presented NPF (Network Propagation for Functions prediction), an integrative protein function predicting framework assisted by network propagation and functional module detection, for discovering interacting partners with similar functions to target proteins. NPF leverages knowledge of the protein interaction network architecture and multi-omics data, such as domain annotation and protein complex information, to augment protein-protein functional similarity in a propagation manner. We have verified the great potential of NPF for accurately inferring protein functions. According to the comprehensive evaluation of NPF, it delivered a better performance than other competing methods in terms of leave-one-out cross-validation and ten-fold cross validation.Conclusions: We demonstrated that network propagation, together with multi-omics data, can both discover more partners with similar function, and is unconstricted by the “small-world” feature of protein interaction networks. We conclude that the performance of function prediction depends greatly on whether we can extract and exploit proper functional information of similarity from protein correlations.

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Section: Co-complex Networkmentioning

confidence: 99%

NPF：Network propagation for protein function prediction

Zhao

Jiang

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Background: The accurate annotation of protein functions is of great significance in elucidating the phenomena of life, disease treatment and new drug development. Various methods have been developed to facilitate the prediction of functions by combining protein interaction networks (PINs) with multi-omics data. However, how to make full use of multiple biological data to improve the performance of functions annotation is still a dilemma. Results We presented NPF (Network Propagation for Functions prediction), an integrative protein function predicting framework assisted by network propagation and functional module detection, for discovering interacting partners with similar functions to target proteins. NPF leverages knowledge of the protein interaction network architecture and multi-omics data, such as domain annotation and protein complex information, to augment protein-protein functional similarity in a propagation manner. We have verified the great potential of NPF for accurately inferring protein functions. Comprehensive evaluation of NPF indicates that NPF archived higher performance than competing methods in terms of leave-one-out cross-validation and ten-fold cross validation. Conclusions: We demonstrated that network propagation combined with multi-omics data can not only discover more partners with similar function, but also effectively free from the constraints of the "small-world" feature of protein interaction networks. We conclude that the performance of function prediction depends greatly on whether we can extract and exploit proper functional similarity information from protein correlations.

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An efficient method for protein function annotation based on multilayer protein networks

Cited by 33 publications

References 33 publications

Predicting Protein Functions Based on Differential Co-expression and Neighborhood Analysis

Predicting Protein Functions Based on Differential Co-expression and Neighborhood Analysis

NPF: Network propagation for protein function prediction

NPF：Network propagation for protein function prediction

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