JSM Bioinformatics, Genomics and Proteomics

Network-Based Protein-Protein Interaction Analysis

Review Article | Open Access

  • 1. School of Electronics and Information Engineering, Tongji University, China
  • 2. The Advanced Research Institute of Intelligent Sensing Network, Tongji University, China
  • 3. The Key Laboratory of Embedded System and Service Computing, Tongji University, China
  • 4. Institute of Health Sciences, Anhui University, China
  • 5. College of Electrical Engineering and Automation, Anhui University, China
+ Show More - Show Less
Corresponding Authors
Bing Wang, Department of Computer Science and Technology, 4800 Caoan Road, Shanghai, China, Tel: 8613817605997

High-throughput experimental technologies in protein interaction continue to alter the study of current system biology, and a large-scale data can be available. Protein-protein interactions on these experimental platforms, however, present numerous production and bioinformatics challenges. Some issues like the functional modules identification, protein complexes prediction, protein function prediction and disease-related gene prioritization have become increasingly problematic in the analysis of protein-protein interaction networks. The development of powerful, efficient prediction methods for the structure and function analysis of protein interaction network is critical for the research community to accelerate research and publications. Currently, Network-based approaches are drawing the most attention in analyzing protein interactions.This review aims to describe the-state-of-art of network-based strategies and applications to infer protein interactions.


Wang B, Shen H, Chen P, Zhang J (2015) Network-Based Protein-Protein Interaction Analysis. J Bioinform, Genomics, Proteomics 1(1): 1002.


•    Protein-protein interaction network
•    Functional modules identification
•    Protein complexes prediction
•    Protein function prediction
•    Disease-related gene prioritization


PPI: Protein-Protein Interaction; PPIN: Protein-Protein Interaction Network


Cells and organs are very complex systems because the interactions and the relations between cells to cells, DNA to RNA and RNA to proteins are very multifaceted and large in volume and length [1]. Of the different types of biological interactions, protein-protein interactions (PPIs) are one of the most significant, interesting and complicated interaction because some protein may work as an individual entity, but usually two or more proteins bind together and form a complex to carry out their biological functions. Biological processes are largely dependent on protein-protein interactions which carry out numerous functions, from DNA replication, cell replication, protein synthesis, and energy production to molecule transport, to various inter- and intracellular signaling. Several experimental methods have been developed to analyze protein-protein interactions, including yeast two-hybrid assay [2-5] protein chips [6], and mass spectrometry of purified protein complexes [7,8], which produce a vast amount of information and make it possible for researchers to study the biological activities systematically.

Currently, many protein interaction databases had been developed, which can support the establishment of interaction networks. With comparison to the analysis technologies investigated PPIs in the interaction partner or interface level [9- 12], protein-protein interaction network (PPIN) based-methods had caught researchers’ attentions for it can analyze the functions of proteins in a system biology level. For example, HParrishH et al., built a PPIN in 2007 for the bacterium Campylobacter jejuni, a food-borne pathogen and a major cause of gastroenteritis worldwide, and identified a number of conserved sub-networks, biological pathways and putative essential genes that may be used to identify potential new antimicrobial drug targets for C. jejuni and related organisms [13].

Recently, some comprehensive reviews provided insights into the analysis and applications of protein-protein interaction networks [14-18]. This up to date review specifically focuses on four aspects: the functional modules identification, protein complexes prediction, protein function prediction and disease-related gene prioritization.

Protein-protein interaction network (PPIN) analysis

Interaction networks can be represented as an interaction graph, where nodes represent proteins and edges represent pair wise interactions (an example can be found in Figure 1). The analysis of the network structure or topological properties of PPIN, such as distribution of node degree (number of incoming and outgoing edges per node), network diameter (average of the shortest distance between pairs of nodes), clustering coefficient (proportion of the potential edges between the neighbors of a node that are effectively observed in the graph), have led to the observation of some apparently recurrent properties of biological networks: power-law degree distribution, small world, high clustering coefficients, and modularity[19-26]. The network whose degree distribution follows a power law also has been called as scale-free network, in which the fraction P(k) of nodes in the network having k connections to other nodes goes for large values of k as P(k) ~ k, where g is a parameter whose value typically ranges from 2 to 3. The character of small-world means that most nodes in PPIN are not neighbors of one another, but they can be reached from every other by a small numbers of hops or steps. Modularity is another characteristic feature of PPIN, where some protein groups are highly connected among them yet with lesser connections between modules.

Important functional modules identification

As one type of biological functional network, it is essential to understand the relationship between the organization of the network and its functions [19,27]. Therefore, clustering algorithms play an important role in the analysis of PIN, and can be used to uncover functional modules and obtain hints about cellular organization [28]. Brohee and Helden had evaluated four algorithms: Markov Clustering (MCL) [29], Restricted Neighborhood Search Clustering (RNSC) [30], Super Paramagnetic Clustering (SPC) [31], and Molecular Complex Detection (MCODE) [32] in 2006 and found that MCL and RNSC outperform SPC and MCODE in robustness where the test was implemented on unweighted graphs [19].

Regularized MCL (R-MCL), an efficient and robust variation of MCL, was proposed by HSatuluri et al., which can improve the accuracy of identifying functional modules by R-MCL’s regularize operation and balance parameter [33]. Shih and Parthasarathy developed a ‘Soft’ R-MCL (SR-MCL) algorithm, a new variation of R-MCL, which can identify overlapped clusters within PPIN [34].

Wang and Qian proposed a novel optimization formulation LCP2 which can identify both dense and sparse modules simultaneous based on protein interaction patterns in given networks through searching for low two-hop conductance sets by Markov random walk on graphs [35]. Moreover, they presented another two algorithms, SLCP2 and GLCP2 , to identify non-overlapping and overlapping functional modules. The authors also proposed a new joint network clustering algorithm, AS Model, which can combine both topology and homology information [36].

Jia et al. proposed a dense module searching (DMS) method to identify candidate sub-networks or genes for complex diseases by integrating the association signal from GWAS datasets into the human PIN [37]. This method extensively searches for sub-networks enriched with low P-value genes in GWAS datasets, and the experiments show the effectiveness of DMS by testing in two GWAS datasets for complex diseases, i.e. breast cancer and pancreatic cancer.

Wang et al., developed a fast algorithm, HC-PIN, based on the local metric of edge clustering value for hierarchical clustering, which can be used both in the un weighted network and in the weighted network [38]. The authors demonstrated that the usage of local metric in the algorithm HC-PIN not only improves its efficiency, but also enhances its robustness to the high rate of false positives in PIN. Meanwhile, HC-PIN can identify significant modules with low density.

Network-based protein complex prediction

A protein complex is a group of proteins that interact with each other at the same time and place, forming a single multi-molecular machine [16,39,40]. In a network-based way, the problem of identifying protein complexes from PPI data can be formulated as that of detecting dense regions containing many connections in PPI networks, or regions with large weights in weighted networks [41].

Nepusz et al., proposed an algorithm, named Cluster ONE, clustering with overlapping neighborhood expansion for detecting potentially overlapping protein complexes from protein-protein interaction data [41]. In Cluster ONE, there is a concept of the cohesiveness score, a measurement can determine how likely a group of proteins form a complex, had been calculated and it uses a greedy growth process to find protein complexes. The authors also found that taking into account network weights, an estimation of the reliability of protein interactions and is included as edge labels in PPIN, can greatly improve the detection of protein complexes, although it is difficult to assess the reliability of the weights.

Zhang et al., constructed ontology augmented networks to predict protein complexes, which can combine the information from protein-protein interaction networks and gene ontology [42]. This method can formulate the topological structure of protein-protein interaction networks and the similarity of gene ontology annotations into a unified distance measure. The experimental results in this work showed that ontology augmented networks can get a higher F1 measure for predicting protein complexes.

Wu et al., presented a novel rough-fuzzy clustering (RFC) method to detect overlapping protein complexes in PPIN [43]. Rather than the graph models employed in previous approaches, this method applied fuzzy relation model by integrating fuzzy sets and rough sets to deal with overlapping complexes, and it determines whether the protein belongs to one or to multiple complexes by calculating the similarity between one protein and each complex. The work compared the RFC with several previous methods and show big performance improvement, i.e., the precision, sensitivity and separation are 32.4%, 42.9% and 81.9% higher than mean of the five methods in four weighted networks, and are 0.5%, 11.2% and 66.1% higher than mean of the six methods in five un weighted networks.

There are many studies focus on protein complexes identification from PPIN. Shen et al. proposed a complex mining algorithm called Multistage Kernel Extension (MKE) algorithm using a two-level kernel strategy based on the centrality-lethality rule [44]. Yang et al. applies a sophisticated natural language processing system, PPI Extractor, to extract PPI data from biomedical literature, and integrated PPI datasets to detect protein complexes [45]. Hanna and Zaki proposed another ranking algorithm, named Pro Rank+, which can figure out important proteins and complexes in the Bio GRID repository, and some of them had been demonstrated by previous studies [46].

Network-based protein function prediction

In the past two decades, the vigorous development in sequencing technologies posed a novel challenge that is how to elucidate protein function from wealth of genomics data generated [47]. A few years ago, Sharan et al., showed in their work that, even for the most well-studied organisms such as yeast, about one-fourth of the proteins remain uncharacterized, and this high percentage does not drop evidently now [figure 2] [48]. Fortunately, protein interaction networks for many species provide a special view to predict the functions of proteins in a computational way.

Wu et al., systematically identified apoptotic/cell cycle related key proteins using a Naïve Bayesian model based a modified apoptotic/cell cycle related PPI networks [49]. Their work not only identified some already known key proteins such as p53, Rb, Myc and Src but also found that the proteasome, Cullin family members, kinases and transcriptional repressors play important roles in regulating apoptosis and the cell cycle. Meanwhile, they found some proteins were enriched in some pathways such as those of cancer, the proteasome, the cell cycle and Wnt signalling, which can provide further new clues towards future anticancer drug discovery.

Davis et al., predicted protein functions from the conservation of topology-function relationships in protein-protein interaction network [50]. They developed a statistical framework that is built upon canonical correlation analysis where the graphlet degrees represented the wiring around proteins in PINs and gene ontology (GO) annotations described the protein functions. Their method can characterize statistically significant topology-function relationships, and uncover the functions that have conserved topology in PINs. Applications to the PINs of yeast and human show that their proposed frameworks had identified seven biological processes and two cellular components GO terms to be topologically orthologous.

Saha et al., proposed a software, named FunPred, to predict protein functions based on network neighborhood properties [51]. There are two approaches in FunPred, one applies a combination of three simple-yet-effective scoring techniques: the neighborhood ratio, the protein path connectivity and the relative functional similarity. Another is a heuristic approach using the edge clustering coefficient to reduce the search space by identifying densely connected neighborhood regions. Wu et al., developed a regularized non-negative matrix factorization (RNMF) algorithm for protein functional properties prediction where attribute features, latent graph, and unlabeled data information in PPI networks had been used [52]. Peng et al., predicted protein functions using an unbalanced Bi-random walk (UBiRW) algorithm on PPI network and functional interrelationship network by considering the topological and structural difference between them [53].

Network-based diseases-related gene prioritization

Elucidating the underlying molecular mechanisms of diseases has become increasingly important in disease prevention, diagnosis, and drug design [54]. PPIN-based analysis approaches have been recently developed and applied to diseases analysis [54- 60]. Candidate gene prioritization is one of important application of network-based knowledge. Studies on the properties of disease genes in protein interaction networks have shown that two genes sharing higher-order topological similarities are likely to interact with each other and may be associated with the same or similar phenotypes [61,62]. Wu et al., established a regression model that measures the correlation between gene closeness and phenotype similarities in the PPI network to prioritize potential candidate genes for inherited diseases on the basis of correlation scores [63]. Dezso et al., applied a modified shortest path between’s to prioritize candidate genes in PPI networks, where a candidate gene has high relevant score to the disease of interest if it laid more on significantly shorter paths connecting nodes of known disease genes than other genes in the network [H59H]. Recently, Luo and Liang proposed a random walk-based algorithm on the reliable heterogeneous network (RWRHN) to prioritize potential candidate genes for inherited diseases, in which a PPI network reconstructed by topological similarity, a phenotype similarity network and known associations between diseases and genes [figure 3][54]


The development of powerful high-throughput experimental technologies has fundamentally changed the study of current system biology [64]. However, huge data produced by these different platforms also presents some serious challenges, such as the high false positive rate in current ‘wet’ experiments and the validation of the analytical results from ‘dry’ methods. Network-based analysis, a kind of computational tools, can adopt graph theory to address the inherent knowledge within the protein interaction data. For example, it can score the importance of proteins using degree information of nodes within PPIN no matter the network is weighted or unweighted. One of biggest advantage of network-based PPI approaches is it can analyze the interactions at a system biology level. Also, it can easily combine other information, such as GO term, protein subcellar location, gene regulation, and so on, into the processing framework, which in turn makes the PPI networks modeling more precise. Especially, network-based analysis will become more and more important for some complex diseases for many of them can be seen as network diseases, that is to say, the root of these diseases is not one or few molecules. In this work, we only focus on the new progresses published on four aspects, i.e., the functional modules identification, protein complexes prediction, protein function prediction and disease-related gene prioritization. It is clear that network-based methods hold incredible promise for protein interaction research in many other applications, and their capabilities in the hands of investigators will undoubtedly accelerate our understanding of the mechanism of cell to perform their functions.


This work was supported by the National Science Foundation of China (Nos. 61300058, 61472282 and 61374181), and Anhui Provincial Natural Science Foundation (No.1508085MF129).


1. Alberts B, Alexander Johnson, Julian Lewis, Martin Raff, Keith Roberts, Peter Walter. Molecular biology of the cell. 4th Edn. Garland Science. New York. 2002.

2. Ito T, Tashiro K, Muta S, Ozawa R, Chiba T, Nishizawa M, et al. Toward a protein-protein interaction map of the budding yeast: A comprehensive system to examine two-hybrid interactions in all possible combinations between the yeast proteins. Proc Natl Acad Sci USA. 2000; 97: 1143-1147.

3. Ito T, Chiba T, Ozawa R, Yoshida M, Hattori M, Sakaki Y. A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad Sci USA. 2001; 98 : 4569-4574.

4. Ito T, Chiba T, Yoshida M. Exploring the protein interactome using comprehensive two-hybrid projects. Trends Biotechnol. 2001; 19: S23-27.

5. Uetz P, Giot L, Cagney G, Mansfield TA, Judson RS, Knight JR, et al. A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature. 2000; 403: 623-627.

6. Kung LA. Proteome chips: a future perspective. Expert Rev Proteomics. 2006; 3: 565-567.

7. Gavin AC, Bosche M, Krause R, Grandi P, Marzioch M, Bauer A, et al. Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature. 2002; 415: 141-147.

8. Ho Y, Gruhler A, Heilbut A, Bader GD, Moore L, Adams SL, et al. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature. 2002; 415: 180-183.

9. Zhu L, You ZH, Huang DS,Wang B. t-LSE: a novel robust geometric approach for modeling protein-protein interaction networks. PLoSOne. 2013; 8: e58368.

10. Wang B, Chen P, Wang P, Zhao G, Zhang X. Radial basis function neural network ensemble for predicting protein-protein interaction sites in heterocomplexes. Protein Pept Lett. 2010; 17: 1111-1116.

11. Wang B, Chen P, Huang DS, Li JJ, Lok TM, Lyu MR. Predicting protein interaction sites from residue spatial sequence profile and evolution rate. FEBS Lett. 2006; 580: 380-384.

12. Wang B, Huang DS, Jiang C. A new strategy for protein interface identification using manifold learning method. IEEETrans Nanobioscience. 2014; 13: 118-23.

13. Parrish JR, Yu J, Liu G, Hines JA, Chan JE, Mangiola BA, et al. A proteomewide protein interaction map for Campylobacter jejuni . Genome Biol. 2007; 8: R130.

14. Ma XT, Chen T, Sun FZ. Integrative approaches for predicting protein function and prioritizing genes for complex phenotypes using protein interaction networks. Briefings in Bioinformatics. 2014; 15: 685-698.

15. Mitra K, Carvunis AR, Ramesh SK, Ideker T. Integrative approaches for finding modular structure in biological networks. Nat Rev Genet. 2013; 14: 719-732.

16. Chen B, Fan W, Liu J, Wu FX. Identifying protein complexes and functional modules--from static PPI networks to dynamic PPI networks. Brief Bioinform. 2014; 15: 177-194.

17. Srihari S, Leong HW. A survey of computational methods for protein complex prediction from protein interaction networks. J Bioinform Comput Biol. 2013; 11: 1230002. 

18. Li X, Wu M, Kwoh CK, Ng SK. Computational approaches for detecting protein complexes from protein interaction networks: a survey. BMC Genomics. 2010; 11: S3.

19. Brohee S, van Helden J. Evaluation of clustering algorithms for protein-protein interaction networks. BMC Bioinformatics. 2006; 7: 488.

20. Raman K, Damaraju N, Joshi GK. The organisational structure of protein networks: revisiting the centrality-lethality hypothesis. Syst Synth Biol. 2014; 8: 73-81.

21. Tew KL, Li XL, Tan SH. Functional centrality: detecting lethality of proteins in protein interaction networks. Genome Inform. 2007; 19: 166-177.

22. Jeong H, Mason SP, Barabasi AL, Oltvai ZN. Lethality and centrality in protein networks. Nature. 2001; 411: 41-42.

23. Han JD, Dupuy D, Bertin N, Cusick ME, Vidal M. Effect of sampling on topology predictions of protein-protein interaction networks. Nat Biotechnol. 2005; 23: 839-844.

24. Goldberg DS, Roth FP. Assessing experimentally derived interactions in a small world. Proc Natl Acad Sci US A. 2003; 100: 4372-4376.

25. Yook SH, Oltvai ZN, Barabasi AL. Functional and topological characterization of protein interaction networks. Proteomics. 2004; 4: 928-942.

26. Ravasz E, Barabasi AL. Hierarchical organization in complex networks. Phys Rev E Stat Nonlin Soft Matter Phys. 2003; 67: 026112.

27. Rhrissorrakrai K, Gunsalus KC. MINE: Module Identification in Networks. BMC Bioinformatics. 2011; 12: 192.

28. Jiang P, Singh M. SPICi: a fast clustering algorithm for large biological networks. Bioinformatics. 2010; 26: 1105-1111.

29. Enright AJ, Van Dongen S, Ouzounis CA. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res. 2002; 30: 1575-1584.

30. King AD, Przulj N, Jurisica I. Protein complex prediction via cost-based clustering. Bioinformatics. 2004; 20: 3013-3020.

31. Bader GD, Hogue CW. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics. 2003;4:2.

32. Blatt M, Wiseman S, Domany E. Superparamagnetic clustering of data. Phys Rev Lett. 1996; 76: 3251-3254.

33. Srihari S, Ning K, Leong HW. Refining Markov Clustering for protein complex prediction by incorporating core-attachment structure. Genome Inform. 2009; 23: 159-168.

34. Shih YK, Parthasarathy S. Identifying functional modules in interaction networks through overlapping Markov clustering. Bioinformatics. 2012; 28: I473-I1479.

35. Wang YJ, Qian XN. Functional module identification in protein interaction networks by interaction patterns. Bioinformatics. 2014; 30: 81-93.

36. Wang YJ, Qian XN. Joint clustering of protein interaction networks through Markov random walk. Bmc Systems Biology. 2014; 8.

37. Jia P, Zheng S, Long J, Zheng W, Zhao Z. dmGWAS: dense module searching for genome-wide association studies in protein-protein interaction networks. Bioinformatics. 2011; 27: 95-102.

38. Wang J, Li M, Chen J, Pan Y. A fast hierarchical clustering algorithm for functional modules discovery in protein interaction networks. IEEE/ACM Trans Comput Biol Bioinform. 2011; 8: 607-620.

39. Terentiev AA, Moldogazieva NT, Shaitan KV. Dynamic proteomics in modeling of the living cell. Protein-protein interactions. Biochemistry (Mosc). 2009; 74: 1586-1607.

40. Spirin V, Mirny LA. Protein complexes and functional modules in molecular networks. Proc Natl Acad Sci U S A. 2003; 100:12123- 12128.

41. Nepusz T, Yu H, Paccanaro A. Detecting overlapping protein complexes in protein-protein interaction networks. Nat Methods. 2012; 9: 471-472.

42. Zhang Y, Lin H, Yang Z, Wang J. Construction of ontology augmented networks for protein complex prediction. PLoSOne. 2013; 8: e62077.

43. Wu H, Gao L, Dong J, Yang X. Detecting overlapping protein complexes by rough-fuzzy clustering in protein-protein interaction networks. PLoS One. 2014; 9: e91856.

44. Shen X, Zhao Y, Li Y, He T, Yang J, Hu X. An efficient protein complex mining algorithm based on Multistage Kernel Extension. BMC Bioinformatics. 2014; 15.

45. Yang Z, Yu F, Lin H, Wang J. Integrating PPI datasets with the PPI data from biomedical literature for protein complex detection. BMC Med Genomics. 2014; 7.

46. Hanna EM, Zaki N. Detecting protein complexes in protein interaction networks using a ranking algorithm with a refined merging procedure. BMC Bioinformatics. 2014; 15.

47. Vazquez A, Flammini A, Maritan A, Vespignani A. Global protein function prediction from protein-protein interaction networks. Nat Biotechnol. 2003; 21: 697-700.

48. Sharan R, Ulitsky I, Shamir R. Network-based prediction of protein function. Mol Syst Biol. 2007; 3: 88.

49. Wu L, Zhou N, Sun R, Chen XD, Feng SC, Zhang B, et al. Network-based identification of key proteins involved in apoptosis and cell cycle regulation. Cell Prolif. 2014; 47: 356-368.

50. Davis D, Yaveroglu ON, Malod-Dognin N, Stojmirovic A, Przulj N. Topology-function conservation in protein-protein interaction networks. Bioinformatics. 2015.

51. Saha S, Chatterjee P, Basu S, Kundu M, Nasipuri M. FunPred-1: protein function prediction from a protein interaction network using neighborhood analysis. Cell Mol Biol Lett. 2014; 19: 675-691.

52. Wu Q, Wang Z, Li C, Ye Y, Li Y, Sun N. Protein functional properties prediction in sparsely-label PPI networks through regularized non-negative matrix factorization. BMC Syst Biol. 2015; 9: S9.

53. Peng W, Wang JX, Chen L, Zhong JC, Zhang Z, Pan Y. Predicting Protein Functions by Using Unbalanced Bi-Random Walk Algorithm on Protein-Protein Interaction Network and Functional Interrelationship Network. Curr Protein Pept Sc. 2014; 15: 529-539.

54. Luo J, Liang S. Prioritization of potential candidate disease genes by topological similarity of protein-protein interaction network and phenotype data. J Biomed Inform. 2015; 53: 229-236.

55. Zhu YC, Deng BY, Zhang LG, Xu P, Du XP, Zhang QG, et al. Protein-protein interaction network analysis of osteoarthritis-related differentially expressed genes. Genet Mol Res. 2014; 13: 9343-9351.

56. Kong B, Yang T, Chen L, Kuang YQ, Gu JW, Xia X, et al. Protein-protein interaction network analysis and gene set enrichment analysis in epilepsy patients with brain cancer. J Clin Neurosci. 2014; 21: 316- 319.

57. Keith BP, Robertson DL, Hentges KE. Locus heterogeneity disease genes encode proteins with high interconnectivity in the human protein interaction network. Front Genet. 2014; 5: 434.

58. Jiang Y, Shu Y, Shi Y, Li LP, Yuan F, Ren H. Identifying gastric cancer related genes using the shortest path algorithm and protein-protein interaction network. Biomed Res Int. 2014; 2014: 371397.

59. Dezso Z, Nikolsky Y, Nikolskaya T, Miller J, Cherba D, Webb C, et al. Identifying disease-specific genes based on their topological significance in protein networks. Bmc Systems Biology. 2009; 3.

60. Wu C, Zhu J, Zhang X. Integrating gene expression and protein-protein interaction network to prioritize cancer-associated genes. BMC Bioinformatics. 2012; 13: 182.

61. Goh KI, Choi IG. Exploring the human diseasome: the human disease network. Brief Funct Genomics. 2012; 11: 533-542.

62. Ideker T, Sharan R. Protein networks in disease. Genome Res. 2008; 18: 644-652.

63. Wu X, Jiang R, Zhang MQ, Li S. Network-based global inference of human disease genes. Mol Syst Biol. 2008; 4: 189.

64. Wang B, Sun WL, Zhang J, Chen P. Current Status of Machine Learning-Based Methods for Identifying Protein-Protein Interaction Sites. Curr Bioinform. 2013; 8: 177-182.

Wang B, Shen H, Chen P, Zhang J (2015) Network-Based Protein-Protein Interaction Analysis. J Bioinform, Genomics, Proteomics 1(1): 1002.

Received : 21 Apr 2015
Accepted : 30 Jun 2015
Published : 02 Jul 2015
Annals of Otolaryngology and Rhinology
ISSN : 2379-948X
Launched : 2014
JSM Schizophrenia
Launched : 2016
Journal of Nausea
Launched : 2020
JSM Internal Medicine
Launched : 2016
JSM Hepatitis
Launched : 2016
JSM Oro Facial Surgeries
ISSN : 2578-3211
Launched : 2016
Journal of Human Nutrition and Food Science
ISSN : 2333-6706
Launched : 2013
JSM Regenerative Medicine and Bioengineering
ISSN : 2379-0490
Launched : 2013
JSM Spine
ISSN : 2578-3181
Launched : 2016
Archives of Palliative Care
ISSN : 2573-1165
Launched : 2016
JSM Nutritional Disorders
ISSN : 2578-3203
Launched : 2017
Annals of Neurodegenerative Disorders
ISSN : 2476-2032
Launched : 2016
Journal of Fever
ISSN : 2641-7782
Launched : 2017
JSM Bone Marrow Research
ISSN : 2578-3351
Launched : 2016
JSM Mathematics and Statistics
ISSN : 2578-3173
Launched : 2014
Journal of Autoimmunity and Research
ISSN : 2573-1173
Launched : 2014
JSM Arthritis
ISSN : 2475-9155
Launched : 2016
JSM Head and Neck Cancer-Cases and Reviews
ISSN : 2573-1610
Launched : 2016
JSM General Surgery Cases and Images
ISSN : 2573-1564
Launched : 2016
JSM Anatomy and Physiology
ISSN : 2573-1262
Launched : 2016
JSM Dental Surgery
ISSN : 2573-1548
Launched : 2016
Annals of Emergency Surgery
ISSN : 2573-1017
Launched : 2016
Annals of Mens Health and Wellness
ISSN : 2641-7707
Launched : 2017
Journal of Preventive Medicine and Health Care
ISSN : 2576-0084
Launched : 2018
Journal of Chronic Diseases and Management
ISSN : 2573-1300
Launched : 2016
Annals of Vaccines and Immunization
ISSN : 2378-9379
Launched : 2014
JSM Heart Surgery Cases and Images
ISSN : 2578-3157
Launched : 2016
Annals of Reproductive Medicine and Treatment
ISSN : 2573-1092
Launched : 2016
JSM Brain Science
ISSN : 2573-1289
Launched : 2016
JSM Biomarkers
ISSN : 2578-3815
Launched : 2014
JSM Biology
ISSN : 2475-9392
Launched : 2016
Archives of Stem Cell and Research
ISSN : 2578-3580
Launched : 2014
Annals of Clinical and Medical Microbiology
ISSN : 2578-3629
Launched : 2014
JSM Pediatric Surgery
ISSN : 2578-3149
Launched : 2017
Journal of Memory Disorder and Rehabilitation
ISSN : 2578-319X
Launched : 2016
JSM Tropical Medicine and Research
ISSN : 2578-3165
Launched : 2016
JSM Head and Face Medicine
ISSN : 2578-3793
Launched : 2016
JSM Cardiothoracic Surgery
ISSN : 2573-1297
Launched : 2016
JSM Bone and Joint Diseases
ISSN : 2578-3351
Launched : 2017
JSM Bioavailability and Bioequivalence
ISSN : 2641-7812
Launched : 2017
JSM Atherosclerosis
ISSN : 2573-1270
Launched : 2016
Journal of Genitourinary Disorders
ISSN : 2641-7790
Launched : 2017
Journal of Fractures and Sprains
ISSN : 2578-3831
Launched : 2016
Journal of Autism and Epilepsy
ISSN : 2641-7774
Launched : 2016
Annals of Marine Biology and Research
ISSN : 2573-105X
Launched : 2014
JSM Health Education & Primary Health Care
ISSN : 2578-3777
Launched : 2016
JSM Communication Disorders
ISSN : 2578-3807
Launched : 2016
Annals of Musculoskeletal Disorders
ISSN : 2578-3599
Launched : 2016
Annals of Virology and Research
ISSN : 2573-1122
Launched : 2014
JSM Renal Medicine
ISSN : 2573-1637
Launched : 2016
Journal of Muscle Health
ISSN : 2578-3823
Launched : 2016
JSM Genetics and Genomics
ISSN : 2334-1823
Launched : 2013
JSM Anxiety and Depression
ISSN : 2475-9139
Launched : 2016
Clinical Journal of Heart Diseases
ISSN : 2641-7766
Launched : 2016
Annals of Medicinal Chemistry and Research
ISSN : 2378-9336
Launched : 2014
JSM Pain and Management
ISSN : 2578-3378
Launched : 2016
JSM Women's Health
ISSN : 2578-3696
Launched : 2016
Clinical Research in HIV or AIDS
ISSN : 2374-0094
Launched : 2013
Journal of Endocrinology, Diabetes and Obesity
ISSN : 2333-6692
Launched : 2013
Journal of Substance Abuse and Alcoholism
ISSN : 2373-9363
Launched : 2013
JSM Neurosurgery and Spine
ISSN : 2373-9479
Launched : 2013
Journal of Liver and Clinical Research
ISSN : 2379-0830
Launched : 2014
Journal of Drug Design and Research
ISSN : 2379-089X
Launched : 2014
JSM Clinical Oncology and Research
ISSN : 2373-938X
Launched : 2013
JSM Chemistry
ISSN : 2334-1831
Launched : 2013
Journal of Trauma and Care
ISSN : 2573-1246
Launched : 2014
JSM Surgical Oncology and Research
ISSN : 2578-3688
Launched : 2016
Annals of Food Processing and Preservation
ISSN : 2573-1033
Launched : 2016
Journal of Radiology and Radiation Therapy
ISSN : 2333-7095
Launched : 2013
JSM Physical Medicine and Rehabilitation
ISSN : 2578-3572
Launched : 2016
Annals of Clinical Pathology
ISSN : 2373-9282
Launched : 2013
Annals of Cardiovascular Diseases
ISSN : 2641-7731
Launched : 2016
Journal of Behavior
ISSN : 2576-0076
Launched : 2016
Annals of Clinical and Experimental Metabolism
ISSN : 2572-2492
Launched : 2016
Clinical Research in Infectious Diseases
ISSN : 2379-0636
Launched : 2013
JSM Microbiology
ISSN : 2333-6455
Launched : 2013
Journal of Urology and Research
ISSN : 2379-951X
Launched : 2014
Journal of Family Medicine and Community Health
ISSN : 2379-0547
Launched : 2013
Annals of Pregnancy and Care
ISSN : 2578-336X
Launched : 2017
JSM Cell and Developmental Biology
ISSN : 2379-061X
Launched : 2013
Annals of Aquaculture and Research
ISSN : 2379-0881
Launched : 2014
Clinical Research in Pulmonology
ISSN : 2333-6625
Launched : 2013
Journal of Immunology and Clinical Research
ISSN : 2333-6714
Launched : 2013
Annals of Forensic Research and Analysis
ISSN : 2378-9476
Launched : 2014
JSM Biochemistry and Molecular Biology
ISSN : 2333-7109
Launched : 2013
Annals of Breast Cancer Research
ISSN : 2641-7685
Launched : 2016
Annals of Gerontology and Geriatric Research
ISSN : 2378-9409
Launched : 2014
Journal of Sleep Medicine and Disorders
ISSN : 2379-0822
Launched : 2014
JSM Burns and Trauma
ISSN : 2475-9406
Launched : 2016
Chemical Engineering and Process Techniques
ISSN : 2333-6633
Launched : 2013
Annals of Clinical Cytology and Pathology
ISSN : 2475-9430
Launched : 2014
JSM Allergy and Asthma
ISSN : 2573-1254
Launched : 2016
Journal of Neurological Disorders and Stroke
ISSN : 2334-2307
Launched : 2013
Annals of Sports Medicine and Research
ISSN : 2379-0571
Launched : 2014
JSM Sexual Medicine
ISSN : 2578-3718
Launched : 2016
Annals of Vascular Medicine and Research
ISSN : 2378-9344
Launched : 2014
JSM Biotechnology and Biomedical Engineering
ISSN : 2333-7117
Launched : 2013
Journal of Hematology and Transfusion
ISSN : 2333-6684
Launched : 2013
JSM Environmental Science and Ecology
ISSN : 2333-7141
Launched : 2013
Journal of Cardiology and Clinical Research
ISSN : 2333-6676
Launched : 2013
JSM Nanotechnology and Nanomedicine
ISSN : 2334-1815
Launched : 2013
Journal of Ear, Nose and Throat Disorders
ISSN : 2475-9473
Launched : 2016
JSM Ophthalmology
ISSN : 2333-6447
Launched : 2013
Journal of Pharmacology and Clinical Toxicology
ISSN : 2333-7079
Launched : 2013
Annals of Psychiatry and Mental Health
ISSN : 2374-0124
Launched : 2013
Medical Journal of Obstetrics and Gynecology
ISSN : 2333-6439
Launched : 2013
Annals of Pediatrics and Child Health
ISSN : 2373-9312
Launched : 2013
JSM Clinical Pharmaceutics
ISSN : 2379-9498
Launched : 2014
JSM Foot and Ankle
ISSN : 2475-9112
Launched : 2016
JSM Alzheimer's Disease and Related Dementia
ISSN : 2378-9565
Launched : 2014
Journal of Addiction Medicine and Therapy
ISSN : 2333-665X
Launched : 2013
Journal of Veterinary Medicine and Research
ISSN : 2378-931X
Launched : 2013
Annals of Public Health and Research
ISSN : 2378-9328
Launched : 2014
Annals of Orthopedics and Rheumatology
ISSN : 2373-9290
Launched : 2013
Journal of Clinical Nephrology and Research
ISSN : 2379-0652
Launched : 2014
Annals of Community Medicine and Practice
ISSN : 2475-9465
Launched : 2014
Annals of Biometrics and Biostatistics
ISSN : 2374-0116
Launched : 2013
JSM Clinical Case Reports
ISSN : 2373-9819
Launched : 2013
Journal of Cancer Biology and Research
ISSN : 2373-9436
Launched : 2013
Journal of Surgery and Transplantation Science
ISSN : 2379-0911
Launched : 2013
Journal of Dermatology and Clinical Research
ISSN : 2373-9371
Launched : 2013
JSM Gastroenterology and Hepatology
ISSN : 2373-9487
Launched : 2013
Annals of Nursing and Practice
ISSN : 2379-9501
Launched : 2014
JSM Dentistry
ISSN : 2333-7133
Launched : 2013
Author Information X