Protein-protein Interaction Network Data Research Articles

GSLAlign: community detection and local PPI network alignment

High throughput protein–protein interaction (PPI) profiling and computational techniques have resulted in generating a large amount of PPI network data. The study of PPI networks helps in understanding the biological processes of the proteins. The comparative study of the PPI networks helps in identifying the conserved interactions across the species. This article presents a novel local PPI network aligner ‘GSLAlign’ that consists of two stages. It first detects the communities from the PPI networks by applying the GraphSAGE algorithm using gene expression data. In the second stage, the detected communities are aligned using a community aligner that is based on protein sequence similarity. The community detection algorithm produces more separable and biologically accurate communities as compared to previous community detection algorithms. Moreover, the proposed community alignment algorithm achieves 3–8% better results in terms of semantic similarity as compared to previous local aligners. The average connectivity and coverage of the proposed algorithm are also better than the existing aligners. Communicated by Ramaswamy H. Sarma

Implementing link prediction in protein networks via feature fusion models based on graph neural networks

MotivationProtein-protein interactions serve as the cornerstone for various biochemical processes within biological organisms. Existing research methodologies predominantly employ link prediction techniques to analyze these interaction networks. However, traditional approaches often fall short in delivering satisfactory predictive performance when applied to multi-species datasets. Current computational methods largely focus on analyzing the network topology, resulting in a somewhat monolithic feature set. The integration of diverse features in the model could potentially yield superior performance and broader applicability. To this end, we propose an autoencoder model built on graph neural networks, designed to enhance both predictive performance and generalizability by leveraging the integration of gene ontology. ResultsIn this research, we developed AGraphSAGE, a model specifically designed for analyzing protein-protein interaction network data. By seamlessly integrating gene ontology into the graph structure, we employed a dual-channel graph sampling and aggregation network that capitalizes on topological information to process high-dimensional features. Feature fusion is achieved through the implementation of graph attention mechanisms, and we adopted a link prediction framework as the experimental training model. Performance was evaluated on real-world datasets using key metrics, such as Area Under the Curve (AUC). A hyperparameter search space was established, and a Bayesian optimization strategy was applied to iteratively fine-tune the model, assessing the impact of various parameters on predictive efficacy. The experimental results validate that our proposed model is capable of effectively predicting protein-protein interactions across diverse biological species.

Genome-Wide Identification and Analysis Uncovers the Potential Role of JAZ and MYC Families in Potato under Abiotic Stress.

As key regulators of the Jasmonates (JAs) signal transduction pathway, JAZ protein, and MYC transcription factors are imperative for plant response to external environmental changes, growth, and development. In this study, 18 StJAZs and 12 StMYCs were identified in potatoes. Their chromosomal position, phylogenetic development, gene structure, and promoter cis-acting parts of the StJAZ genes were analyzed. In addition, Protein-Protein Interaction (PPI) network analysis of StJAZ and StMYC gene families and yeast two-hybrid assay demonstrated that five StMYCs can interact with 16 StJAZs, which provides new insights into the operation mechanism of StJAZs and StMYCs in JA signal response. Moreover, we explored the expression profiles of StJAZs and StMYCs genes in different tissues and during abiotic stresses by RNA-seq data. Based on the PPI network and transcriptome data, the genes StJAZ11, StJAZ16, and StMYC6 were chosen for further qRT-PCR study under salt or mannitol treatment. Under mannitol-induced drought or salinity treatment, the expression patterns of StMYC6, StJAZ11, and StJAZ16 were different, indicating that the JAZ protein and MYC transcription factor may be engaged in the response of potatoes to abiotic stress, which opened up a new research direction for the genetic improvement of potatoes in response to environmental stress.

SANA: cross-species prediction of Gene Ontology GO annotations via topological network alignment

Topological network alignment aims to align two networks node-wise in order to maximize the observed common connection (edge) topology between them. The topological alignment of two protein–protein interaction (PPI) networks should thus expose protein pairs with similar interaction partners allowing, for example, the prediction of common Gene Ontology (GO) terms. Unfortunately, no network alignment algorithm based on topology alone has been able to achieve this aim, though those that include sequence similarity have seen some success. We argue that this failure of topology alone is due to the sparsity and incompleteness of the PPI network data of almost all species, which provides the network topology with a small signal-to-noise ratio that is effectively swamped when sequence information is added to the mix. Here we show that the weak signal can be detected using multiple stochastic samples of “good” topological network alignments, which allows us to observe regions of the two networks that are robustly aligned across multiple samples. The resulting network alignment frequency (NAF) strongly correlates with GO-based Resnik semantic similarity and enables the first successful cross-species predictions of GO terms based on topology-only network alignments. Our best predictions have an AUPR of about 0.4, which is competitive with state-of-the-art algorithms, even when there is no observable sequence similarity and no known homology relationship. While our results provide only a “proof of concept” on existing network data, we hypothesize that predicting GO terms from topology-only network alignments will become increasingly practical as the volume and quality of PPI network data increase.

Towards future directions in data-integrative supervised prediction of human aging-related genes.

Identification of human genes involved in the aging process is critical due to the incidence of many diseases with age. A state-of-the-art approach for this purpose infers a weighted dynamic aging-specific subnetwork by mapping gene expression (GE) levels at different ages onto the protein-protein interaction network (PPIN). Then, it analyzes this subnetwork in a supervised manner by training a predictive model to learn how network topologies of known aging- versus non-aging-related genes change across ages. Finally, it uses the trained model to predict novel aging-related gene candidates. However, the best current subnetwork resulting from this approach still yields suboptimal prediction accuracy. This could be because it was inferred using outdated GE and PPIN data. Here, we evaluate whether analyzing a weighted dynamic aging-specific subnetwork inferred from newer GE and PPIN data improves prediction accuracy upon analyzing the best current subnetwork inferred from outdated data. Unexpectedly, we find that not to be the case. To understand this, we perform aging-related pathway and Gene Ontology term enrichment analyses. We find that the suboptimal prediction accuracy, regardless of which GE or PPIN data is used, may be caused by the current knowledge about which genes are aging-related being incomplete, or by the current methods for inferring or analyzing an aging-specific subnetwork being unable to capture all of the aging-related knowledge. These findings can potentially guide future directions towards improving supervised prediction of aging-related genes via -omics data integration. All data and code are available at zenodo, DOI: 10.5281/zenodo.6995045. Supplementary data are available at Bioinformatics Advances online.

Integration of RNA-Seq and proteomics data identifies glioblastoma multiforme surfaceome signature

BackgroundGlioblastoma multiforme (GBM) is a highly lethal, stage IV brain tumour with a prevalence of approximately 2 per 10,000 people globally. The cell surface proteins or surfaceome serve as information gateway in many oncogenic signalling pathways and are important in modulating cancer phenotypes. Dysregulation in surfaceome expression and activity have been shown to promote tumorigenesis. The expression of GBM surfaceome is a case in point; OMICS screening in a cell-based system identified that this sub-proteome is largely perturbed in GBM. Additionally, since these cell surface proteins have ‘direct’ access to drugs, they are appealing targets for cancer therapy. However, a comprehensive GBM surfaceome landscape has not been fully defined yet. Thus, this study aimed to define GBM-associated surfaceome genes and identify key cell-surface genes that could potentially be developed as novel GBM biomarkers for therapeutic purposes.MethodsWe integrated the RNA-Seq data from TCGA GBM (n = 166) and GTEx normal brain cortex (n = 408) databases to identify the significantly dysregulated surfaceome in GBM. This was followed by an integrative analysis that combines transcriptomics, proteomics and protein-protein interaction network data to prioritize the high-confidence GBM surfaceome signature.ResultsOf the 2381 significantly dysregulated genes in GBM, 395 genes were classified as surfaceome. Via the integrative analysis, we identified 6 high-confidence GBM molecular signature, HLA-DRA, CD44, SLC1A5, EGFR, ITGB2, PTPRJ, which were significantly upregulated in GBM. The expression of these genes was validated in an independent transcriptomics database, which confirmed their upregulated expression in GBM. Importantly, high expression of CD44, PTPRJ and HLA-DRA is significantly associated with poor disease-free survival. Last, using the Drugbank database, we identified several clinically-approved drugs targeting the GBM molecular signature suggesting potential drug repurposing.ConclusionsIn summary, we identified and highlighted the key GBM surface-enriched repertoires that could be biologically relevant in supporting GBM pathogenesis. These genes could be further interrogated experimentally in future studies that could lead to efficient diagnostic/prognostic markers or potential treatment options for GBM.

Supervised Prediction of Aging-Related Genes From a Context-Specific Protein Interaction Subnetwork.

Human aging is linked to many prevalent diseases. The aging process is highly influenced by genetic factors. Hence, it is important to identify human aging-related genes. We focus on supervised prediction of such genes. Gene expression-based methods for this purpose study genes in isolation from each other. While protein-protein interaction (PPI) network-based methods for this purpose account for interactions between genes' protein products, current PPI network data are context-unspecific, spanning different biological conditions. Instead, here, we focus on an aging-specific subnetwork of the entire PPI network, obtained by integrating aging-specific gene expression data and PPI network data. The potential of such data integration has been recognized but mostly in the context of cancer. So, we are the first to propose a supervised learning framework for predicting aging-related genes from an aging-specific PPI subnetwork. In a systematic and comprehensive evaluation, we find that in many of the evaluation tests: (i) using an aging-specific subnetwork indeed yields more accurate aging-related gene predictions than using the entire network, and (ii) predictive methods from our framework that have not previously been used for supervised prediction of aging-related genes outperform existing prominent methods for the same purpose. These results justify the need for our framework.

Protein-Protein interactions uncover candidate 'core genes' within omnigenic disease networks.

Genome wide association studies (GWAS) of human diseases have generally identified many loci associated with risk with relatively small effect sizes. The omnigenic model attempts to explain this observation by suggesting that diseases can be thought of as networks, where genes with direct involvement in disease-relevant biological pathways are named 'core genes', while peripheral genes influence disease risk via their interactions or regulatory effects on core genes. Here, we demonstrate a method for identifying candidate core genes solely from genes in or near disease-associated SNPs (GWAS hits) in conjunction with protein-protein interaction network data. Applied to 1,381 GWAS studies from 5 ancestries, we identify a total of 1,865 candidate core genes in 343 GWAS studies. Our analysis identifies several well-known disease-related genes that are not identified by GWAS, including BRCA1 in Breast Cancer, Amyloid Precursor Protein (APP) in Alzheimer's Disease, INS in A1C measurement and Type 2 Diabetes, and PCSK9 in LDL cholesterol, amongst others. Notably candidate core genes are preferentially enriched for disease relevance over GWAS hits and are enriched for both Clinvar pathogenic variants and known drug targets-consistent with the predictions of the omnigenic model. We subsequently use parent term annotations provided by the GWAS catalog, to merge related GWAS studies and identify candidate core genes in over-arching disease processes such as cancer-where we identify 109 candidate core genes.

Identification of Novel Cancer-Related Genes with a Prognostic Role Using Gene Expression and Protein-Protein Interaction Network Data

Early cancer diagnosis and prognosis prediction are necessary for cancer patients. Effective identification of cancer-related genes and biomarkers and survival prediction for cancer patients would facilitate personalized treatment of cancer patients. This study aimed to investigate a method for integrating data regarding gene expression and protein-protein interaction networks to identify cancer-related prognostic genes via random walk with restart algorithm and survival analysis. Known cancer-related genes in protein-protein interaction networks were considered seed genes, and the random walk algorithm was used to identify candidate cancer-related genes. Thereafter, using the univariant Cox regression model, gene expression data were screened to identify survival-related genes. Furthermore, candidate genes and survival-related genes were screened to identify cancer-related prognostic genes. Finally, the effectiveness of the method was verified through gene function analysis and survival prediction. The results indicate that the cancer-related genes can be considered prognostic cancer biomarkers and provide a basis for cancer diagnosis.

Finnish Parkinson\u2019s disease study integrating protein-protein interaction network data with exome sequencing analysis

Variants associated with Parkinson’s disease (PD) have generally a small effect size and, therefore, large sample sizes or targeted analyses are required to detect significant associations in a whole exome sequencing (WES) study. Here, we used protein-protein interaction (PPI) information on 36 genes with established or suggested associations with PD to target the analysis of the WES data. We performed an association analysis on WES data from 439 Finnish PD subjects and 855 controls, and included a Finnish population cohort as the replication dataset with 60 PD subjects and 8214 controls. Single variant association (SVA) test in the discovery dataset yielded 11 candidate variants in seven genes, but the associations were not significant in the replication cohort after correction for multiple testing. Polygenic risk score using variants rs2230288 and rs2291312, however, was associated to PD with odds ratio of 2.7 (95% confidence interval 1.4–5.2; p < 2.56e-03). Furthermore, an analysis of the PPI network revealed enriched clusters of biological processes among established and candidate genes, and these functional networks were visualized in the study. We identified novel candidate variants for PD using a gene prioritization based on PPI information, and described why these variants may be involved in the pathogenesis of PD.

Using deep maxout neural networks to improve the accuracy of function prediction from protein interaction networks.

Protein-protein interaction network data provides valuable information that infers direct links between genes and their biological roles. This information brings a fundamental hypothesis for protein function prediction that interacting proteins tend to have similar functions. With the help of recently-developed network embedding feature generation methods and deep maxout neural networks, it is possible to extract functional representations that encode direct links between protein-protein interactions information and protein function. Our novel method, STRING2GO, successfully adopts deep maxout neural networks to learn functional representations simultaneously encoding both protein-protein interactions and functional predictive information. The experimental results show that STRING2GO outperforms other protein-protein interaction network-based prediction methods and one benchmark method adopted in a recent large scale protein function prediction competition.

Methylation of RCAN1.4 mediated by DNMT1 and DNMT3b enhances hepatic stellate cell activation and liver fibrogenesis through Calcineurin/NFAT3 signaling.

Background: Liver fibrosis is characterized by extensive deposition of extracellular matrix (ECM) components in the liver. RCAN1 (regulator of calcineurin 1), an endogenous inhibitor of calcineurin (CaN), is required for ECM synthesis during hypertrophy of various organs. However, the functional role of RCAN1 in liver fibrogenesis has not yet been addressed.Methods: We induced experimental liver fibrosis in mice by intraperitoneal injection of 10 % CCl4 twice a week. To investigate the functional role of RCAN1.4 in the progression of liver fibrosis, we specifically over-expressed RCAN1.4 in mice liver using rAAV8-packaged RCAN1.4 over-expression plasmid. Following the establishment of the fibrotic mouse model, primary hepatic stellate cells were isolated. Subsequently, we evaluated the effect of RCAN1.4 on hepatic fibrogenesis, hepatic stellate cell activation, and cell survival. The biological role and signaling events for RCAN1 were analyzed by protein-protein interaction (PPI) network. Bisulfite sequencing PCR (BSP) was used to predict the methylated CpG islands in the RCAN1.4 gene promoter. We used the chromatin immunoprecipitation (ChIP assay) to investigate DNA methyltransferases which induced decreased expression of RCAN1.4 in liver fibrosis.Results: Two isoforms of RCAN1 protein were expressed in CCl4-induced liver fibrosis mouse model and HSC-T6 cells cultured with transforming growth factor-beta 1 (TGF-β1). RCAN1 isoform 4 (RCAN1.4) was selectively down-regulated in vivo and in vitro. The BSP analysis indicated the presence of two methylated sites in RCAN1.4 promoter and the downregulated RCAN1.4 expression levels could be restored by 5-aza-2'-deoxycytidine (5-azadC) and DNMTs-RNAi transfection in vitro. ChIP assay was used to demonstrate that the decreased RCAN1.4 expression was associated with DNMT1 and DNMT3b. Furthermore, we established a CCl4-induced liver fibrosis mouse model by injecting the recombinant adeno-associated virus-packaged RCAN1.4 (rAAV8-RCAN1.4) over-expression plasmid through the tail vein. Liver- specific-over-expression of RAN1.4 led to liver function recovery and alleviated ECM deposition. The key protein (a member of the NFAT family of proteins) identified on PPI network data was analyzed in vivo and in vitro. Our results demonstrated that RCAN1.4 over-expression alleviates, whereas its knockdown exacerbates, TGF-β1-induced liver fibrosis in vitro in a CaN/NFAT3 signaling-dependent manner.Conclusions: RCAN1.4 could alleviate liver fibrosis through inhibition of CaN/NFAT3 signaling, and the anti-fibrosis function of RCAN1.4 could be blocked by DNA methylation mediated by DNMT1 and DNMT3b. Thus, RCAN1.4 may serve as a potential therapeutic target in the treatment of liver fibrosis.

A Method for Predicting Protein Complexes from Dynamic Weighted Protein-Protein Interaction Networks.

Predicting protein complexes from protein-protein interaction (PPI) network is of great significance to recognize the structure and function of cells. A protein may interact with different proteins under different time or conditions. Existing approaches only utilize static PPI network data that may lose much temporal biological information. First, this article proposed a novel method that combines gene expression data at different time points with traditional static PPI network to construct different dynamic subnetworks. Second, to further filter out the data noise, the semantic similarity based on gene ontology is regarded as the network weight together with the principal component analysis, which is introduced to deal with the weight computing by three traditional methods. Third, after building a dynamic PPI network, a predicting protein complexes algorithm based on "core-attachment" structural feature is applied to detect complexes from each dynamic subnetworks. Finally, it is revealed from the experimental results that our method proposed in this article performs well on detecting protein complexes from dynamic weighted PPI networks.

Systematic module approach identifies altered genes and pathways in four types of ovarian cancer.

The present study aimed to identify altered genes and pathways associated with four histotypes of ovarian cancer, according to the systematic tracking of dysregulated modules of reweighted protein-protein interaction (PPI) networks. Firstly, the PPI network and gene expression data were initially integrated to infer and reweight normal ovarian and four types of ovarian cancer (endometrioid, serous, mucinous and clear cell carcinoma) PPI networks based on Spearman's correlation coefficient. Secondly, modules in the PPI network were mined using a clique-merging algorithm and the differential modules were identified through maximum weight bipartite matching. Finally, the gene compositions in the altered modules were analyzed, and pathway functional enrichment analyses for disrupted module genes were performed. In five conditional-specific networks, universal alterations in gene correlations were revealed, which leads to the differential correlation density among disrupted module pairs. The analyses revealed 28, 133, 139 and 33 altered modules in endometrioid, serous, mucinous and clear cell carcinoma, respectively. Gene composition analyses of the disrupted modules revealed five common genes (mitogen-activated protein kinase 1, phosphoinositide 3-kinase-encoding catalytic 110-KDα, AKT serine/threonine kinase 1, cyclin D1 and tumor protein P53) across the four subtypes of ovarian cancer. In addition, pathway enrichment analysis confirmed one common pathway (pathways in cancer), in the four histotypes. This systematic module approach successfully identified altered genes and pathways in the four types of ovarian cancer. The extensive differences of gene correlations result in dysfunctional modules, and the coordinated disruption of these modules contributes to the development and progression of ovarian cancer.

Profiling of differentially expressed genes in adipose tissues of multiple symmetric lipomatosis

Multiple symmetric lipomatosis (MSL) is a rare disorder characterized by aberrant multiple and symmetric subcutaneous adipose tissue accumulation in the face, neck, shoulders, back, chest and abdomen, severely affecting the quality of life of patients. At present, precise MSL etiology and pathogenesis remain to be elucidated. The present study first utilized a digital gene expression technique with a next-generation sequencing platform to profile differentially expressed genes in three cases of MSL vs. normal control tissue. cDNA libraries from these tissue specimens were constructed and DNA sequenced for identification of differentially expressed genes, which underwent bioinformatic analysis using the Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interaction (PPI) network analyses. As a result, a total of 859 differentially expressed genes were identified, including 308 upregulated genes (C19orf80, Apelin, C21orf33, FAM166B and HSD11B2 were mostly upregulated 6.984-, 4.670-, 4.412-, 3.693- and 3.561-fold, respectively) and 551 downregulated genes [FosB proto-oncogene, AP-1 transcription factor subunit (FOSB), selectin (SEL) E, RAR related orphan receptor (ROR) B, salt inducible kinase (SIK)1 and epidermal growth factor-like protein (EGFL)6 were mostly downregulated −9.845, −8.243, −8.123, −7.702 and −7.664 fold, respectively). The GO functional enrichment analysis demonstrated these differentially expressed genes were predominantly involved in biological processes and cellular components, while the KEGG pathway enrichment analysis demonstrated that ribosome, non-alcoholic fatty liver disease, human T-lymphotropic virus type 1 (HTLV-I) infection and Alzheimer's disease pathways were altered in MSL. The PPI network data demonstrated ubiquitin C (UBC), translocator protein (TSPO), Jun Proto-Oncogene, AP-1 Transcription Factor (JUN) and FOS were among these differentially expressed genes that participated in regulation of adipocyte differentiation, although no previous study has linked them to MSL. In conclusion, the present study profiled differentially expressed genes in MSL and identified gene pathways that may be associated with MSL development and progression.

DIGNiFI: Discovering causative genes for orphan diseases using protein-protein interaction networks

BackgroundAn orphan disease is any disease that affects a small percentage of the population. Orphan diseases are a great burden to patients and society, and most of them are genetic in origin. Unfortunately, our current understanding of the genes responsible for inherited orphan diseases is still quite limited. Developing effective computational algorithms to discover disease-causing genes would help unveil disease mechanisms and may enable better diagnosis and treatment.ResultsWe have developed a novel method, named as DIGNiFI (Disease causIng GeNe FInder), which uses Protein-Protein Interaction (PPI) network-based features to discover and rank candidate disease-causing genes. Specifically, our approach computes topologically similar genes by taking into account both local and global connected paths in PPI networks via Direct Neighbors and Local Random Walks, respectively. Furthermore, since genes with similar phenotypes tend to be functionally related, we have integrated PPI data with gene ontology (GO) annotations and protein complex data to further improve the performance of this approach. Results of 128 orphan diseases with 1184 known disease genes collected from the Orphanet show that our proposed methods outperform existing state-of-the-art methods for discovering candidate disease-causing genes. We also show that further performance improvement can be achieved when enriching the human-curated PPI network data with text-mined interactions from the biomedical literature. Finally, we demonstrate the utility of our approach by applying our method to identifying novel candidate genes for a set of four inherited retinal dystrophies. In this study, we found the top predictions for these retinal dystrophies consistent with literature reports and online databases of other retinal dystrophies.ConclusionsOur method successfully prioritizes orphan-disease-causative genes. This method has great potential to benefit the field of orphan disease research, where resources are scarce and greatly needed.

The Intrinsic Geometric Structure of Protein-Protein Interaction Networks for Protein Interaction Prediction.

Recent developments in high-throughput technologies for measuring protein-protein interaction (PPI) have profoundly advanced our ability to systematically infer protein function and regulation. However, inherently high false positive and false negative rates in measurement have posed great challenges in computational approaches for the prediction of PPI. A good PPI predictor should be 1) resistant to high rate of missing and spurious PPIs, and 2) robust against incompleteness of observed PPI networks. To predict PPI in a network, we developed an intrinsic geometry structure (IGS) for network, which exploits the intrinsic and hidden relationship among proteins in network through a heat diffusion process. In this process, all explicit PPIs participate simultaneously to glue local infinitesimal and noisy experimental interaction data to generate a global macroscopic descriptions about relationships among proteins. The revealed implicit relationship can be interpreted as the probability of two proteins interacting with each other. The revealed relationship is intrinsic and robust against individual, local and explicit protein interactions in the original network. We apply our approach to publicly available PPI network data for the evaluation of the performance of PPI prediction. Experimental results indicate that, under different levels of the missing and spurious PPIs, IGS is able to robustly exploit the intrinsic and hidden relationship for PPI prediction with a higher sensitivity and specificity compared to that of recently proposed methods.

INGA: protein function prediction combining interaction networks, domain assignments and sequence similarity.

Identifying protein functions can be useful for numerous applications in biology. The prediction of gene ontology (GO) functional terms from sequence remains however a challenging task, as shown by the recent CAFA experiments. Here we present INGA, a web server developed to predict protein function from a combination of three orthogonal approaches. Sequence similarity and domain architecture searches are combined with protein-protein interaction network data to derive consensus predictions for GO terms using functional enrichment. The INGA server can be queried both programmatically through RESTful services and through a web interface designed for usability. The latter provides output supporting the GO term predictions with the annotating sequences. INGA is validated on the CAFA-1 data set and was recently shown to perform consistently well in the CAFA-2 blind test. The INGA web server is available from URL: http://protein.bio.unipd.it/inga.

Predicting protein functions by using unbalanced bi-random walk algorithm on protein-protein interaction network and functional interrelationship network.

Accurate annotation of protein functions is still a big challenge for understanding life in the post-genomic era. Recently, some methods have been developed to solve the problem by incorporating functional similarity of GO terms into protein-protein interaction (PPI) network, which are based on the observation that a protein tends to share some common functions with proteins that interact with it in PPI network, and two similar GO terms in functional interrelationship network usually co-annotate some common proteins. However, these methods annotate functions of proteins by considering at the same level neighbors of proteins and GO terms respectively, and few attempts have been made to investigate their difference. Given the topological and structural difference between PPI network and functional interrelationship network, we firstly investigate at which level neighbors of proteins tend to have functional associations and at which level neighbors of GO terms usually co-annotate some common proteins. Then, an unbalanced Bi-random walk (UBiRW) algorithm which iteratively walks different number of steps in the two networks is adopted to find protein-GO term associations according to some known associations. Experiments are carried out on S. cerevisiae data. The results show that our method achieves better prediction performance not only than methods that only use PPI network data, but also than methods that consider at the same level neighbors of proteins and of GO terms.

Identifying Spurious Interactions and Predicting Missing Interactions in the Protein-Protein Interaction Networks via a Generative Network Model

With the rapid development of high-throughput experiment techniques for protein-protein interaction (PPI) detection, a large amount of PPI network data are becoming available. However, the data produced by these techniques have high levels of spurious and missing interactions. This study assigns a new reliably indication for each protein pairs via the new generative network model (RIGNM) where the scale-free property of the PPI network is considered to reliably identify both spurious and missing interactions in the observed high-throughput PPI network. The experimental results show that the RIGNM is more effective and interpretable than the compared methods, which demonstrate that this approach has the potential to better describe the PPI networks and drive new discoveries.