Bi-random Walk Research Articles

A Framework f or Predicting Drug Target Interaction Pairs Through Heterogeneous Information Fusion

Drugs, also known as medicines cure diseases by interacting with some specific targets such as proteins and nucleic acid. Prediction of such drug-target interaction pairs plays a major role in drug discovery. It helps to identify the side effects caused by various drugs and provide a way to analyze the chances of usage of one drug for various diseases apart from the one disease that is predefined for that drug. However, existing Drug Target Interaction prediction methods are very expensive and time consuming. In this work, we present a new method to predict such interactions with the help of bipartite graph, which represents the known drug target interaction pairs. Information about drug and target are collected from various sources and they are integrated using Kronecker Regularized Least Square approach and Multiple Kernel Learning method, to generate drug and target similarity matrices. By integrating the two similarity matrices and known DTIs a heterogeneous network is constructed and new DTI predictions are done by performing Bi Random walk in it.

Drug repositioning based on individual bi-random walks on a heterogeneous network

BackgroundTraditional drug research and development is high cost, time-consuming and risky. Computationally identifying new indications for existing drugs, referred as drug repositioning, greatly reduces the cost and attracts ever-increasing research interests. Many network-based methods have been proposed for drug repositioning and most of them apply random walk on a heterogeneous network consisted with disease and drug nodes. However, these methods generally adopt the same walk-length for all nodes, and ignore the different contributions of different nodes.ResultsIn this study, we propose a drug repositioning approach based on individual bi-random walks (DR-IBRW) on the heterogeneous network. DR-IBRW firstly quantifies the individual work-length of random walks for each node based on the network topology and knowledge that similar drugs tend to be associated with similar diseases. To account for the inner structural difference of the heterogeneous network, it performs bi-random walks with the quantified walk-lengths, and thus to identify new indications for approved drugs. Empirical study on public datasets shows that DR-IBRW achieves a much better drug repositioning performance than other related competitive methods.ConclusionsUsing individual random walk-lengths for different nodes of heterogeneous network indeed boosts the repositioning performance. DR-IBRW can be easily generalized to prioritize links between nodes of a network.

Prediction of disease-related metabolites using bi-random walks.

Metabolites play a significant role in various complex human disease. The exploration of the relationship between metabolites and diseases can help us to better understand the underlying pathogenesis. Several network-based methods have been used to predict the association between metabolite and disease. However, some methods ignored hierarchical differences in disease network and failed to work in the absence of known metabolite-disease associations. This paper presents a bi-random walks based method for disease-related metabolites prediction, called MDBIRW. First of all, we reconstruct the disease similarity network and metabolite functional similarity network by integrating Gaussian Interaction Profile (GIP) kernel similarity of diseases and GIP kernel similarity of metabolites, respectively. Then, the bi-random walks algorithm is executed on the reconstructed disease similarity network and metabolite functional similarity network to predict potential disease-metabolite associations. At last, MDBIRW achieves reliable performance in leave-one-out cross validation (AUC of 0.910) and 5-fold cross validation (AUC of 0.924). The experimental results show that our method outperforms other existing methods for predicting disease-related metabolites.

A novel algorithm based on bi-random walks to identify disease-related lncRNAs

BackgroundsThere is evidence to suggest that lncRNAs are associated with distinct and diverse biological processes. The dysfunction or mutation of lncRNAs are implicated in a wide range of diseases. An accurate computational model can benefit the diagnosis of diseases and help us to gain a better understanding of the molecular mechanism. Although many related algorithms have been proposed, there is still much room to improve the accuracy of the algorithm.ResultsWe developed a novel algorithm, BiWalkLDA, to predict disease-related lncRNAs in three real datasets, which have 528 lncRNAs, 545 diseases and 1216 interactions in total. To compare performance with other algorithms, the leave-one-out validation test was performed for BiWalkLDA and three other existing algorithms, SIMCLDA, LDAP and LRLSLDA. Additional tests were carefully designed to analyze the parameter effects such as α, β, l and r, which could help user to select the best choice of these parameters in their own application. In a case study of prostate cancer, eight out of the top-ten disease-related lncRNAs reported by BiWalkLDA were previously confirmed in literatures.ConclusionsIn this paper, we develop an algorithm, BiWalkLDA, to predict lncRNA-disease association by using bi-random walks. It constructs a lncRNA-disease network by integrating interaction profile and gene ontology information. Solving cold-start problem by using neighbors’ interaction profile information. Then, bi-random walks was applied to three real biological datasets. Results show that our method outperforms other algorithms in predicting lncRNA-disease association in terms of both accuracy and specificity.Availabilityhttps://github.com/screamer/BiwalkLDA

Isoform function prediction based on bi-random walks on a heterogeneous network.

Alternative splicing contributes to the functional diversity of protein species and the proteoforms translated from alternatively spliced isoforms of a gene actually execute the biological functions. Computationally predicting the functions of genes has been studied for decades. However, how to distinguish the functional annotations of isoforms, whose annotations are essential for understanding developmental abnormalities and cancers, is rarely explored. The main bottleneck is that functional annotations of isoforms are generally unavailable and functional genomic databases universally store the functional annotations at the gene level. We propose IsoFun to accomplish Isoform Function prediction based on bi-random walks on a heterogeneous network. IsoFun firstly constructs an isoform functional association network based on the expression profiles of isoforms derived from multiple RNA-seq datasets. Next, IsoFun uses the available Gene Ontology annotations of genes, gene-gene interactions and the relations between genes and isoforms to construct a heterogeneous network. After this, IsoFun performs a tailored bi-random walk on the heterogeneous network to predict the association between GO terms and isoforms, thus accomplishing the prediction of GO annotations of isoforms. Experimental results show that IsoFun significantly outperforms the state-of-the-art algorithms and improves the area under the receiver-operating curve (AUROC) and the area under the precision-recall curve (AUPRC) by 17% and 44% at the gene-level, respectively. We further validated the performance of IsoFun on the genes ADAM15 and BCL2L1. IsoFun accurately differentiates the functions of respective isoforms of these two genes. The code of IsoFun is available at http://mlda.swu.edu.cn/codes.php? name=IsoFun. Supplementary data are available at Bioinformatics online.

BiRWDDA: A Novel Drug Repositioning Method Based on Multisimilarity Fusion.

The explosive growth of large-scale biological data enables network-based drug repositioning to be an important way of drug discovery, which can reduce the time and cost of drug discovery efficiently. Many existing approaches always construct drug-disease association network only based on some similarity measuring data for drug or disease, which ignore the impacts of different similarity measuring on predicting performance. In this study, we develop a new computational approach named BiRWDDA, which fused multiple similarity measures and bi-random walk to discover potential associations between drugs and diseases. First, multiple drug-drug similarity and disease-disease similarity are measured. Next, the information entropy of similarities measured based on different data are calculated to select proper similarities of drugs and diseases. Subsequently, improved drug-drug similarity and disease-disease similarity can be obtained by fusing similarities selected. Then, a logistic function is adopted to adjust the improved drug similarity and disease similarity. What is more, a heterogeneous network can be conducted by connecting the drug similarity network and the disease similarity network through known drug-disease associations. Finally, a bi-random walk algorithm is implemented on the heterogeneous network to predict potential drug-disease associations. Experimental results demonstrate that BiRWDDA outperforms the other state-of-the-art methods with average AUC of 0.930. Case studies for five selected drugs further verify the favorable prediction performance.

Identifying Interactions Between Kinases and Substrates Based on Protein-Protein Interaction Network.

Protein phosphorylation is a kind of important post-translational modification of protein, which plays a critical role in many biological processes of eukaryote. Identifying kinase-substrate interactions is helpful to understand the mechanism of many diseases. Many computational algorithms for kinase-substrate interactions identification have been proposed. However, most of those methods are mainly focused on utilizing protein local sequence information. In this article, we propose a new computational method to predict kinase-substrate interactions based on protein-protein interaction (PPI) network. Different from existing methods, the PPI network is utilized to measure the similarities of kinase-kinase and substrate-substrate, respectively. Then, the pairwise similarities of kinase-kinase and substrate-substrate are adjusted based on the assumption that the similarities of kinase-kinase and substrate-substrate are more reliable if they are in the same cluster. Finally, the bi-random walk is used to predict potential kinase-substrate interactions. The experimental results show that our method outperforms other state-of-the-art algorithms in performance. Furthermore, the case study demonstrates that it is effective in predicting potential kinase-substrate interactions.

BRWMDA:Predicting Microbe-Disease Associations Based on Similarities and Bi-Random Walk on Disease and Microbe Networks.

Many current studies have evidenced that microbes play important roles in human diseases. Therefore, discovering the associations between microbes and diseases is beneficial to systematically understanding the mechanisms of diseases, diagnosing, and treating complex diseases. It is well known that finding new potential microbe-disease associations via biological experiments is a time-consuming and expensive process. However, the computation methods can provide an opportunity to effectively predict microbe-disease associations. In recent years, efforts toward predicting microbe-disease associations are not in proportional to the importance of microbes to human diseases. In this study, we develop a method (called BRWMDA) to predict new microbe-disease associations based on similarity and improving bi-random walk on the disease and microbe networks. BRWMDA integrates microbe network, disease network, and known microbe-disease associations into a single network. After calculating the Gaussian Interaction Profile (GIP) kernel similarity of microbes based on known microbe-disease associations, the microbe network is obtained by adjusting the similarity with the logistics function. In addition, the disease network is computed by the similarity network fusion (SNF) method with the symptom-based similarity and the GIP kernel similarity based on known microbe-disease associations. Then, these two networks of microbe and disease are connected by known microbe-disease associations. Based on the assumption that similar microbes are normally associated with similar diseases and vice versa, BRWMDA is employed to predict new potential microbe-disease associations via random walk with different steps on microbe and disease networks, which reasonably uses the similarity of microbe network and disease network. The 5-fold cross validation and Leave One Out Cross Validation (LOOCV) are adopted to assess the prediction performance of our BRWMDA algorithm, as well as other competing methods for comparison. 5-fold cross validation experiments show that BRWMDA obtained the maximum AUC value of 0.9087, which is again superior to other methods of 0.9025(NGRHMDA), 0.8797 (LRLSHMDA), 0.8571 (KATZHMDA), 0.7782 (HGBI), and 0.5629 (NBI). In addition, BRWMDA also outperforms other methods in terms of LOOCV, whose AUC value is 0.9397, which is superior to other methods of 0.9111(NGRHMDA), 0.8909 (LRLSHMDA), 0.8644 (KATZHMDA), 0.7866 (HGBI), and 0.5553 (NBI). Case studies also illustrate that BRWMDA is an effective method to predict microbe-disease associations.

Inferring microRNA-disease association by hybrid recommendation algorithm and unbalanced bi-random walk on heterogeneous network

More and more research works have indicated that microRNAs (miRNAs) play indispensable roles in exploring the pathogenesis of diseases. Detecting miRNA-disease associations by experimental techniques in biology is expensive and time-consuming. Hence, it is important to propose reliable and accurate computational methods to exploring potential miRNAs related diseases. In our work, we develop a novel method (BRWHNHA) to uncover potential miRNAs associated with diseases based on hybrid recommendation algorithm and unbalanced bi-random walk. We first integrate the Gaussian interaction profile kernel similarity into the miRNA functional similarity network and the disease semantic similarity network. Then we calculate the transition probability matrix of bipartite network by using hybrid recommendation algorithm. Finally, we adopt unbalanced bi-random walk on the heterogeneous network to infer undiscovered miRNA-disease relationships. We tested BRWHNHA on 22 diseases based on five-fold cross-validation and achieves reliable performance with average AUC of 0.857, which an area under the ROC curve ranging from 0.807 to 0.924. As a result, BRWHNHA significantly improves the performance of inferring potential miRNA-disease association compared with previous methods. Moreover, the case studies on lung neoplasms and prostate neoplasms also illustrate that BRWHNHA is superior to previous prediction methods and is more advantageous in exploring potential miRNAs related diseases. All source codes can be downloaded from https://github.com/myl446/BRWHNHA.

Prediction of drug-target interaction by integrating diverse heterogeneous information source with multiple kernel learning and clustering methods

BackgroundIdentification of potential drug-target interaction pairs is very important for pharmaceutical innovation and drug discovery. Numerous machine learning-based and network-based algorithms have been developed for predicting drug-target interactions. However, large-scale pharmacological, genomic and chemical datum emerged recently provide new opportunity for further heightening the accuracy of drug-target interactions prediction. ResultsIn this work, based on the assumption that similar drugs tend to interact with similar proteins and vice versa, we developed a novel computational method (namely MKLC-BiRW) to predict new drug-target interactions. MKLC-BiRW integrates diverse drug-related and target-related heterogeneous information source by using the multiple kernel learning and clustering methods to generate the drug and target similarity matrices, in which the low similarity elements are set to zero to build the drug and target similarity correction networks. By incorporating these drug and target similarity correction networks with known drug-target interaction bipartite graph, MKLC-BiRW constructs the heterogeneous network on which Bi-random walk algorithm is adopted to infer the potential drug-target interactions. ConclusionsCompared with other existing state-of-the-art methods, MKLC-BiRW achieves the best performance in terms of AUC and AUPR. MKLC-BiRW can effectively predict the potential drug-target interactions.

Laplacian normalization and bi-random walks on heterogeneous networks for predicting lncRNA-disease associations

BackgroundEvidences have increasingly indicated that lncRNAs (long non-coding RNAs) are deeply involved in important biological regulation processes leading to various human complex diseases. Experimental investigations of these disease associated lncRNAs are slow with high costs. Computational methods to infer potential associations between lncRNAs and diseases have become an effective prior-pinpointing approach to the experimental verification.ResultsIn this study, we develop a novel method for the prediction of lncRNA-disease associations using bi-random walks on a network merging the similarities of lncRNAs and diseases. Particularly, this method applies a Laplacian technique to normalize the lncRNA similarity matrix and the disease similarity matrix before the construction of the lncRNA similarity network and disease similarity network. The two networks are then connected via existing lncRNA-disease associations. After that, bi-random walks are applied on the heterogeneous network to predict the potential associations between the lncRNAs and the diseases. Experimental results demonstrate that the performance of our method is highly comparable to or better than the state-of-the-art methods for predicting lncRNA-disease associations. Our analyses on three cancer data sets (breast cancer, lung cancer, and liver cancer) also indicate the usefulness of our method in practical applications.ConclusionsOur proposed method, including the construction of the lncRNA similarity network and disease similarity network and the bi-random walks algorithm on the heterogeneous network, could be used for prediction of potential associations between the lncRNAs and the diseases.

Gene Ontology-based function prediction of long non-coding RNAs using bi-random walk

BackgroundWith the development of sequencing technology, more and more long non-coding RNAs (lncRNAs) have been identified. Some lncRNAs have been confirmed that they play an important role in the process of development through the dosage compensation effect, epigenetic regulation, cell differentiation regulation and other aspects. However, the majority of the lncRNAs have not been functionally characterized. Explore the function of lncRNAs and the regulatory network has become a hot research topic currently.MethodsIn the work, a network-based model named BiRWLGO is developed. The ultimate goal is to predict the probable functions for lncRNAs at large scale. The new model starts with building a global network composed of three networks: lncRNA similarity network, lncRNA-protein association network and protein-protein interaction (PPI) network. After that, it utilizes bi-random walk algorithm to explore the similarities between lncRNAs and proteins. Finally, we can annotate an lncRNA with the Gene Ontology (GO) terms according to its neighboring proteins.ResultsWe compare the performance of BiRWLGO with the state-of-the-art models on a manually annotated lncRNA benchmark with known GO terms. The experimental results assert that BiRWLGO outperforms other methods in terms of both maximum F-measure (Fmax) and coverage.ConclusionsBiRWLGO is a relatively efficient method to predict the functions of lncRNA. When protein interaction data is integrated, the predictive performance of BiRWLGO gains a great improvement.

Inferring microRNA-Environmental Factor Interactions Based on Multiple Biological Information Fusion.

Accumulated studies have shown that environmental factors (EFs) can regulate the expression of microRNA (miRNA) which is closely associated with several diseases. Therefore, identifying miRNA-EF associations can facilitate the study of diseases. Recently, several computational methods have been proposed to explore miRNA-EF interactions. In this paper, a novel computational method, MEI-BRWMLL, is proposed to uncover the relationship between miRNA and EF. The similarities of miRNA-miRNA are calculated by using miRNA sequence, miRNA-EF interaction, and the similarities of EF-EF are calculated based on the anatomical therapeutic chemical information, chemical structure and miRNA-EF interaction. The similarity network fusion is used to fuse the similarity between miRNA and the similarity between EF, respectively. Further, the multiple-label learning and bi-random walk are employed to identify the association between miRNA and EF. The experimental results show that our method outperforms the state-of-the-art algorithms.

A novel approach for predicting microbe-disease associations by bi-random walk on the heterogeneous network.

Since the microbiome has a significant impact on human health and disease, microbe-disease associations can be utilized as a valuable resource for understanding disease pathogenesis and promoting disease diagnosis and prognosis. Accordingly, it is necessary for researchers to achieve a comprehensive and deep understanding of the associations between microbes and diseases. Nevertheless, to date, little work has been achieved in implementing novel human microbe-disease association prediction models. In this paper, we develop a novel computational model to predict potential microbe-disease associations by bi-random walk on the heterogeneous network (BiRWHMDA). The heterogeneous network was constructed by connecting the microbe similarity network and the disease similarity network via known microbe-disease associations. Microbe similarity and disease similarity were calculated by the Gaussian interaction profile kernel similarity measure; moreover, a logistic function was applied to regulate disease similarity. Additionally, leave-one-out cross validation and 5-fold cross validation were implemented to evaluate the predictive performance of our method; both cross validation methods performed well. The leave-one-out cross validation experiment results illustrate that our method outperforms other previously proposed methods. Furthermore, case studies on asthma and inflammatory bowel disease prove the favorable performance of our method. In conclusion, our method can be considered as an effective computational model for predicting novel microbe-disease associations.

BRWLDA: bi-random walks for predicting lncRNA-disease associations.

Increasing efforts have been done to figure out the association between lncRNAs and complex diseases. Many computational models construct various lncRNA similarity networks, disease similarity networks, along with known lncRNA-disease associations to infer novel associations. However, most of them neglect the structural difference between lncRNAs network and diseases network, hierarchical relationships between diseases and pattern of newly discovered associations. In this study, we developed a model that performs Bi-Random Walks to predict novel LncRNA-Disease Associations (BRWLDA in short). This model utilizes multiple heterogeneous data to construct the lncRNA functional similarity network, and Disease Ontology to construct a disease network. It then constructs a directed bi-relational network based on these two networks and available lncRNAs-disease associations. Next, it applies bi-random walks on the network to predict potential associations. BRWLDA achieves reliable and better performance than other comparing methods not only on experiment verified associations, but also on the simulated experiments with masked associations. Case studies further demonstrate the feasibility of BRWLDA in identifying new lncRNA-disease associations.

NewGOA: Predicting New GO Annotations of Proteins by Bi-Random Walks on a Hybrid Graph.

A remaining key challenge of modern biology is annotating the functional roles of proteins. Various computational models have been proposed for this challenge. Most of them assume the annotations of annotated proteins are complete. But in fact, many of them are incomplete. We proposed a method called NewGOA to predict new Gene Ontology (GO) annotations for incompletely annotated proteins and for completely un-annotated ones. NewGOA employs a hybrid graph, composed of two types of nodes (proteins and GO terms), to encode interactions between proteins, hierarchical relationships between terms and available annotations of proteins. To account for structural difference between GO terms subgraph and proteins subgraph, NewGOA applies a bi-random walks algorithm, which executes asynchronous random walks on the hybrid graph, to predict new GO annotations of proteins. Experimental study on archived GO annotations of two model species (H. Sapiens and S. cerevisiae) shows that NewGOA can more accurately and efficiently predict new annotations of proteins than other related methods. Experimental results also indicate the bi-random walks can explore and further exploit the structural difference between GO terms subgraph and proteins subgraph. The supplementary files and codes of NewGOA are available at: http://mlda.swu.edu.cn/codes.php?name=NewGOA.

A novel approach for predicting microRNA-disease associations by unbalanced bi-random walk on heterogeneous network

MicroRNAs (miRNAs) play a critical role by regulating their targets in post-transcriptional level. Identification of potential miRNA-disease associations will aid in deciphering the pathogenesis of human polygenic diseases. Several computational models have been developed to uncover novel miRNA-disease associations based on the predicted target genes. However, due to the insufficient number of experimentally validated miRNA-target interactions as well as the relatively high false-positive and false-negative rates of predicted target genes, it is still challenging for these prediction models to obtain remarkable performances. The purpose of this study is to prioritize miRNA candidates for diseases. We first construct a heterogeneous network, which consists of a disease similarity network, a miRNA functional similarity network and a known miRNA-disease association network. Then, an unbalanced bi-random walk-based algorithm on the heterogeneous network (BRWH) is adopted to discover potential associations by exploiting bipartite subgraphs. Based on 5-fold cross validation, the proposed network-based method achieves AUC values ranging from 0.782 to 0.907 for the 22 human diseases and an average AUC of almost 0.846. The experiments indicated that BRWH can achieve better performances compared with several popular methods. In addition, case studies of some common diseases further demonstrated the superior performance of our proposed method on prioritizing disease-related miRNA candidates.

Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm.

Drug repositioning, which aims to identify new indications for existing drugs, offers a promising alternative to reduce the total time and cost of traditional drug development. Many computational strategies for drug repositioning have been proposed, which are based on similarities among drugs and diseases. Current studies typically use either only drug-related properties (e.g. chemical structures) or only disease-related properties (e.g. phenotypes) to calculate drug or disease similarity, respectively, while not taking into account the influence of known drug-disease association information on the similarity measures. In this article, based on the assumption that similar drugs are normally associated with similar diseases and vice versa, we propose a novel computational method named MBiRW, which utilizes some comprehensive similarity measures and Bi-Random walk (BiRW) algorithm to identify potential novel indications for a given drug. By integrating drug or disease features information with known drug-disease associations, the comprehensive similarity measures are firstly developed to calculate similarity for drugs and diseases. Then drug similarity network and disease similarity network are constructed, and they are incorporated into a heterogeneous network with known drug-disease interactions. Based on the drug-disease heterogeneous network, BiRW algorithm is adopted to predict novel potential drug-disease associations. Computational experiment results from various datasets demonstrate that the proposed approach has reliable prediction performance and outperforms several recent computational drug repositioning approaches. Moreover, case studies of five selected drugs further confirm the superior performance of our method to discover potential indications for drugs practically. http://github.com//bioinfomaticsCSU/MBiRW CONTACT: jxwang@mail.csu.edu.cn Supplementary data are available at Bioinformatics online.

Network-based Phenome-Genome Association Prediction by Bi-Random Walk.

MotivationThe availability of ontologies and systematic documentations of phenotypes and their genetic associations has enabled large-scale network-based global analyses of the association between the complete collection of phenotypes (phenome) and genes. To provide a fundamental understanding of how the network information is relevant to phenotype-gene associations, we analyze the circular bigraphs (CBGs) in OMIM human disease phenotype-gene association network and MGI mouse phentoype-gene association network, and introduce a bi-random walk (BiRW) algorithm to capture the CBG patterns in the networks for unveiling human and mouse phenome-genome association. BiRW performs separate random walk simultaneously on gene interaction network and phenotype similarity network to explore gene paths and phenotype paths in CBGs of different sizes to summarize their associations as predictions.ResultsThe analysis of both OMIM and MGI associations revealed that majority of the phenotype-gene associations are covered by CBG patterns of small path lengths, and there is a clear correlation between the CBG coverage and the predictability of the phenotype-gene associations. In the experiments on recovering known associations in cross-validations on human disease phenotypes and mouse phenotypes, BiRW effectively improved prediction performance over the compared methods. The constructed global human disease phenome-genome association map also revealed interesting new predictions and phenotype-gene modules by disease classes.

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.