Cited by 181Bioinformatics and Genomic NetworksComplex Network Analysis TechniquesGene Regulatory Network Analysis

The DOI matches its Crossref record; 53 of the 55 references it cites were found in a registry or an index.

Geometric De-noising of Protein-Protein Interaction Networks

Open access

Kuchaiev, Oleksii, Rašajski, Marija, Higham, Desmond J., Pržulj, Nataša·2009·PLoS Computational Biology

DOI 10.1371/journal.pcbi.1000454PMC PMC2711306

Open this paper in CoChat — free account Open PDF

Abstract

Understanding complex networks of protein-protein interactions (PPIs) is one of the foremost challenges of the post-genomic era. Due to the recent advances in experimental bio-technology, including yeast-2-hybrid (Y2H), tandem affinity purification (TAP) and other high-throughput methods for protein-protein interaction (PPI) detection, huge amounts of PPI network data are becoming available. Of major concern, however, are the levels of noise and incompleteness. For example, for Y2H screens, it is thought that the false positive rate could be as high as 64%, and the false negative rate may range from 43% to 71%. TAP experiments are believed to have comparable levels of noise.We present a novel technique to assess the confidence levels of interactions in PPI networks obtained from experimental studies. We use it for predicting new interactions and thus for guiding future biological experiments. This technique is the first to utilize currently the best fitting network model for PPI networks, geometric graphs. Our approach achieves specificity of 85% and sensitivity of 90%. We use it to assign confidence scores to physical protein-protein interactions in the human PPI network downloaded from BioGRID. Using our approach, we predict 251 interactions in the human PPI network, a statistically significant fraction of which correspond to protein pairs sharing common GO terms. Moreover, we validate a statistically significant portion of our predicted interactions in the HPRD database and the newer release of BioGRID. The data and Matlab code implementing the methods are freely available from the web site: http://www.kuchaev.com/Denoising.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1371/journal.pcbi.1000454

Cites

53 of 53 references verified0 citation errors · 0 not checkable

Statistics

Figure 1 Probability density functions and . Probability density functions and learned from embedding the largest connected components of the following PPI networks into 5-dimensional Euclidean space: (A) the yeast S. cerevisiae high confidence PPI network [11] (“Yhigh”); (B) the human PPI network from BioGRID (version 2.0.35) [13] (“HumanBG”). The represents the values of the Euclidean distances between pairs of nodes in the embedding; the represents the values of probability density functions and .CC BY · PubMed Central

Figure 2 ROC curves for “Yhigh” PPI network for embedding space dimensions of 2 to 7. ROC curves measuring the accuracy of de-noising procedure when applied to “Yhigh” PPI network using embedding space dimensions of 2 to 7. is 1–specificity and is sensitivity. Numbers in brackets correspond to the numbers of true positives and false positives for a given distance cutoff (TP,FP).CC BY · PubMed Central

Figure 3 Precision versus Recall curves. Precision versus Recall curves for “Yhigh” PPI network for embedding space dimensions of 2 to 7. is recall and is precision.CC BY · PubMed Central

Figure 4 ROC curves for “Yhigh” PPI network with and without iterating embedding and de-noising procedures. The first (blue) ROC curve shows the performance of the de-noising procedure applied to “Yhigh” PPI network using embedding space dimension of 4. The second (red) ROC curve shows the performance after iterating the embedding and de-noising procedures until convergence. is 1–specificity and is sensitivity.CC BY · PubMed Central

Figure 5 ROC curves for recovering deleted edges. ROC curves for the experiments in which 500, 1000, 2000 and 3000 edges from “Yhigh” network were removed at random and then recovered using the de-noising procedure. is 1–specificity and is sensitivity.CC BY · PubMed Central

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
Matrix Computations
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066
Pattern Recognition and Machine Learning
(2006) · doi:10.1007/978-0-387-45528-0 · Cited by 9,824
KEGG for linking genomes to life and the environment
Kanehisa, M. I., Araki, M., Goto, S., Hattori, M., Hirakawa, M., Itoh, M., Katayama, T., Kawashima, S., Okuda, S., Tokimatsu, T., et al. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm882 · Cited by 6,828
On random graphs. I.
Erdős, P., & Rényi, A. (2022) · Publicationes Mathematicae Debrecen · doi:10.5486/pmd.1959.6.3-4.12 · Cited by 5,337
A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae
Uetz, P., Giot, L., Cagney, G., Mansfield, T. A., Judson, R. S., Knight, J. R., Lockshon, D., Narayan, V., Srinivasan, M., Pochart, P., et al. (2000) · Nature · doi:10.1038/35001009 · Cited by 4,706
BioGRID: a general repository for interaction datasets
Stark, C. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkj109 · Cited by 3,960
Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry
Ho, Y., Gruhler, A., Heilbut, A. M., Bader, G. D., Moore, L., Adams, S., Millar, A., Taylor, P. P., Bennett, K. L., Boutilier, K., et al. (2002) · Nature · doi:10.1038/415180a · Cited by 3,573

Show all 55 references

Sorted by citations

Computed structures of core eukaryotic protein complexes
Humphreys, I. R., Pei, J., Baek, M., Krishnakumar, A., Anishchenko, I., Ovchinnikov, S., Zhang, J., Ness, T. J., Banjade, S., Bagde, S. R., et al. (2021) · Science · doi:10.1126/science.abm4805 · Cited by 542
Link prediction techniques, applications, and performance: A survey
Kumar, A., Singh, S. S., Singh, K., & Biswas, B. (2020) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2020.124289 · Cited by 549
Analyzing and interpreting genome data at the network level with ConsensusPathDB
Herwig, R., Hardt, C., Lienhard, M., & Kamburov, A. (2016) · Nature Protocols · doi:10.1038/nprot.2016.117 · Cited by 408
From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks
Cannistraci, C. V., Alanis‐Lobato, G., & Ravasi, T. (2013) · Scientific Reports · doi:10.1038/srep01613 · Cited by 403
Causal Network Inference by Optimal Causation Entropy
Sun, J., Taylor, D., & Bollt, E. M. (2015) · SIAM Journal on Applied Dynamical Systems · doi:10.1137/140956166 · Cited by 219
A novel link prediction algorithm for reconstructing protein–protein interaction networks by topological similarity
Lei, C., & Ruan, J. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts688 · Cited by 196
Machine learning meets complex networks via coalescent embedding in the hyperbolic space
Muscoloni, A., Thomas, J. M., Ciucci, S., Bianconi, G., & Cannistraci, C. V. (2017) · Nature Communications · doi:10.1038/s41467-017-01825-5 · Cited by 188
Network embedding in biomedical data science
Su, C., Tong, J., Zhu, Y., Cui, P., & Wang, F. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby117 · Cited by 176
Protein complex detection using interaction reliability assessment and weighted clustering coefficient
Zaki, N., Efimov, D., & Berengueres, J. O. L. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-163 · Cited by 175
To Embed or Not: Network Embedding as a Paradigm in Computational Biology
Nelson, W., Žitnik, M., Wang, B., Leskovec, J., Goldenberg, A., & Sharan, R. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00381 · Cited by 164
Emergent Complex Network Geometry
Wu, Z., Menichetti, G., Rahmede, C., & Ginestra Bianconi, A. (2015) · Repository KITopen (Karlsruhe Institute of Technology) · doi:10.5445/ir/1000048411 · Cited by 149
Protein–protein interactions: detection, reliability assessment and applications
Peng, X., Wang, J., Peng, W., Wu, F., & Pan, Y. F. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw066 · Cited by 150
Inferring Interaction Networks From Multi-Omics Data
Hawe, J. S., Theis, F. J., & Heinig, M. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00535 · Cited by 148
Fast Inference for the Latent Space Network Model Using a Case-Control Approximate Likelihood
Raftery, A. E., Niu, X. M., Hoff, P. D., & Yeung, K. Y. (2012) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2012.679240 · Cited by 138
Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding
Cannistraci, C. V., Alanis‐Lobato, G., & Ravasi, T. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt208 · Cited by 126
A survey of computational methods for protein complex prediction from protein interaction networksPreprint
Srihari, S. (2013) · Cited by 123
Computational approaches for protein function prediction: A combined strategy from multiple sequence alignment to molecular docking-based virtual screening
Pierri, C. L., Parisi, G., & Porcelli, V. (2010) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2010.04.008 · Cited by 115
A SURVEY OF COMPUTATIONAL METHODS FOR PROTEIN COMPLEX PREDICTION FROM PROTEIN INTERACTION NETWORKS
Srihari, S., & LEONG, H. W. (2012) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s021972001230002x · Cited by 112
Knowledge graphs and their applications in drug discovery
MacLean, F. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1910673 · Cited by 105
SARS‐CoV‐2–host proteome interactions for antiviral drug discovery
Liu, X., Huuskonen, S., Laitinen, T., Redchuk, T. A., Bogacheva, M. S., Salokas, K., Pöhner, I., Öhman, T., Tonduru, A. K., Hassinen, A., et al. (2021) · Molecular Systems Biology · doi:10.15252/msb.202110396 · Cited by 104
Emergent Complex Network Geometry
Wu, Z., Menichetti, G., Rahmede, C., & Bianconi, G. (2015) · Scientific Reports · doi:10.1038/srep10073 · Cited by 98
Evolving graphs: dynamical models, inverse problems and propagation
Grindrod, P., & Higham, D. J. (2009) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.2009.0456 · Cited by 98
Self-citation
Protein Pockets: Inventory, Shape, and Comparison
Coleman, R. G., & Sharp, K. A. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci900397t · Cited by 89
t-LSE: A Novel Robust Geometric Approach for Modeling Protein-Protein Interaction Networks
Zhu, L., You, Z., Huang, D.-S., & Wang, B. (2013) · PLoS ONE · doi:10.1371/journal.pone.0058368 · Cited by 90
Computational identification of protein-protein interactions in model plant proteomes
Ding, Z., & Kihara, D. (2019) · Scientific Reports · doi:10.1038/s41598-019-45072-8 · Cited by 90
Graphlet-based measures are suitable for biological network comparison
Hayes, W. B., Sun, K., & Pržulj, N. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts729 · Cited by 90
Self-citation
GraphCrunch 2: Software tool for network modeling, alignment and clustering
Kuchaiev, O., Stevanović, A., Hayes, W. B., & Pržulj, N. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-24 · Cited by 79
Self-citation
Efficient embedding of complex networks to hyperbolic space via their Laplacian
Alanis‐Lobato, G., Mier, P., & Andrade-Navarro, M. A. (2016) · Scientific Reports · doi:10.1038/srep30108 · Cited by 82
Computational Methods for Predicting Protein‐Protein Interactions Using Various Protein Features
Ding, Z., & Kihara, D. (2018) · Current Protocols in Protein Science · doi:10.1002/cpps.62 · Cited by 77
Protein‐protein interactions: Making sense of networks via graph‐theoretic modeling
Pržulj, N. (2010) · BioEssays · doi:10.1002/bies.201000044 · Cited by 78
Self-citation
Pioneering topological methods for network-based drug–target prediction by exploiting a brain-network self-organization theory
Durán, C., Daminelli, S., Thomas, J. M., Haupt, V. J., Schroeder, M., & Cannistraci, C. V. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx041 · Cited by 69
A Two-Stage Geometric Method for Pruning Unreliable Links in Protein-Protein Networks
Zhu, L., Deng, S.-P., & Huang, D.-S. (2015) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2015.2420754 · Cited by 66
Complex systems and network science: a survey
Yang, K., Li, J., Liu, M., Lei, T., Xu, X., Wu, H., Cao, J., & Qi, G. (2023) · Journal of Systems Engineering and Electronics · doi:10.23919/jsee.2023.000080 · Cited by 64
Combinatorial Complexity and Compositional Drift in Protein Interaction Networks
Deeds, E. J., Krivine, J., Ferêt, J., Vincent Danos, A., & Fontana, W. (2012) · PLoS ONE · doi:10.1371/journal.pone.0032032 · Cited by 60
Machine learning meets network science: dimensionality reduction for fast and efficient embedding of networks in the hyperbolic spacePreprint
Thomas, J. M., Muscoloni, A., Ciucci, S., Bianconi, G., & Cannistraci, C. V. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.06522 · Cited by 60
Assessing and predicting protein interactions by combining manifold embedding with multiple information integration
Lei, Y., You, Z., Ji, Z., Zhu, L., & Huang, D.-S. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-s7-s3 · Cited by 57
Robustness of metabolic networks: A review of existing definitions
Larhlimi, A., Blachon, S., Selbig, J., & Nikoloski, Z. (2011) · Biosystems · doi:10.1016/j.biosystems.2011.06.002 · Cited by 57
Revealing Missing Parts of the Interactome via Link Prediction
Hulovatyy, Y., Solava, R. W., & Milenković, T. (2014) · PLoS ONE · doi:10.1371/journal.pone.0090073 · Cited by 51
Increasing the reliability of protein–protein interaction networks via non-convex semantic embedding
Zhu, L., You, Z., & Huang, D.-S. (2013) · Neurocomputing · doi:10.1016/j.neucom.2013.04.027 · Cited by 53
Predicting Future Scientific Discoveries Based on a Networked Analysis of the Past Literature
Nagarajan, M., Wilkins, A. D., Bachman, B. J., Novikov, I. B., Bao, S., Haas, P. J., Terrón-Díaz, M. E., Bhatia, S., Adikesavan, A. K., Labrie, J. J., et al. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2788609 · Cited by 52
Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network
Xiao, Z., & Deng, Y. (2020) · PLoS ONE · doi:10.1371/journal.pone.0238915 · Cited by 49
Quasi-Continuous Interpolation Scheme for Pathways between Distant Configurations
Wales, D. J., & Carr, J. M. (2012) · Journal of Chemical Theory and Computation · doi:10.1021/ct3004832 · Cited by 49
Detecting Essential Proteins Based on Network Topology, Gene Expression Data, and Gene Ontology Information
Zhang, W., Xu, J., Li, Y., & Zou, X. (2016) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2016.2615931 · Cited by 49
Development and application of a recombination-based library versus library high- throughput yeast two-hybrid (RLL-Y2H) screening system
Yang, F., Lei, Y., Zhou, M., Yao, Q., Han, Y., Wu, X. Y., Zhong, W., Zhu, C., Xu, W., Tao, R. N., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx1173 · Cited by 49
In situ electrochemical evaluation of dsDNA interaction with the anticancer drug danusertib nitrenium radical product using the DNA-electrochemical biosensor
Diculescu, V. C., & Oliveira‐Brett, A. M. (2015) · Bioelectrochemistry · doi:10.1016/j.bioelechem.2015.10.004 · Cited by 47
Detecting overlapping protein complexes based on a generative model with functional and topological properties
Zhang, X.-F., Dai, D., Ou-Yang, L., & Yan, H. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-186 · Cited by 46
IntScore: a web tool for confidence scoring of biological interactions
Kamburov, A., Stelzl, U., & Herwig, R. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks492 · Cited by 45
The Energy Landscape, Folding Pathways and the Kinetics of a Knotted Protein
Prentiss, M. C., Wales, D. J., & Wolynes, P. G. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000835 · Cited by 44
Manifold learning and maximum likelihood estimation for hyperbolic network embedding
Alanis‐Lobato, G., Mier, P., & Andrade-Navarro, M. A. (2016) · Applied Network Science · doi:10.1007/s41109-016-0013-0 · Cited by 45
Identifying Spurious Interactions in the Protein-Protein Interaction Networks Using Local Similarity Preserving Embedding
Zhu, L., Deng, S.-P., You, Z., & Huang, D.-S. (2015) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2015.2407393 · Cited by 42
Statistical Assessment of Crosstalk Enrichment between Gene Groups in Biological Networks
McCormack, T., Frings, O., Alexeyenko, A., & Sonnhammer, E. L. L. (2013) · PLoS ONE · doi:10.1371/journal.pone.0054945 · Cited by 40
ppiPre: predicting protein-protein interactions by combining heterogeneous features
Deng, Y., Gao, L., & Wang, B. (2013) · BMC Systems Biology · doi:10.1186/1752-0509-7-s2-s8 · Cited by 38
The latent geometry of the human protein interaction network
Alanis‐Lobato, G., Mier, P., & Andrade-Navarro, M. A. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty206 · Cited by 41
A Network-based Approach for Predicting Missing Pathway Interactions
Navlakha, S., Gitter, A., & Bar‐Joseph, Z. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002640 · Cited by 35
Network and Pathway Analysis of Toxicogenomics Data
Barel, G., & Herwig, R. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00484 · Cited by 36
Evolution of the Potential Energy Landscape with Static Pulling Force for Two Model Proteins
Wales, D. J., & Head-Gordon, T. L. (2012) · The Journal of Physical Chemistry B · doi:10.1021/jp211806z · Cited by 35
A survey on graph embedding techniques for biomedical data: Methods and applications
Wu, Y., Chen, Y., Yin, Z., Ding, W., & King, I. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101909 · Cited by 33
Computed cancer interactome explains the effects of somatic mutations in cancers
Zhang, J., Pei, J., Durham, J., Bos, T., & Cong, Q. (2022) · Protein Science · doi:10.1002/pro.4479 · Cited by 31
Predicting Essential Proteins by Integrating Network Topology, Subcellular Localization Information, Gene Expression Profile and GO Annotation Data
Zhang, W., Xu, J., & Zou, X. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2916038 · Cited by 30
Mass-Linked Immuno-Selective Assays in Targeted Proteomics
Madian, A. G., Rochelle, N. S., & Regnier, F. E. (2012) · Analytical Chemistry · doi:10.1021/ac302071k · Cited by 29
An integrative approach to modeling biological networks
Memišević, V., Milenković, T., & Pržulj, N. (2010) · Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics · doi:10.1515/jib-2010-120 · Cited by 27
Self-citation
The inherent community structure of hyperbolic networks
Kovács, B., & Palla, G. (2021) · Scientific Reports · doi:10.1038/s41598-021-93921-2 · Cited by 27
Network-based Drug Repurposing: A Critical Review
Selvaraj, N., Swaroop, A. K., Nidamanuri, B. S. S., Kumar, R. R., Natarajan, J., & Selvaraj, J. (2022) · Current Drug Research Reviews · doi:10.2174/2589977514666220214120403 · Cited by 27
Protein Complexes Discovery Based on Protein-Protein Interaction Data via a Regularized Sparse Generative Network Model
Zhang, X., Dai, D., & Li, X. (2012) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2012.20 · Cited by 26
Fusion of expression values and protein interaction information using multi-objective optimization for improving gene clustering
Dutta, P., & Saha, S. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.07.015 · Cited by 25
IN SILICO SCREENING OF PROTEIN–PROTEIN INTERACTIONS WITH ALL-TO-ALL RIGID DOCKING AND CLUSTERING: AN APPLICATION TO PATHWAY ANALYSIS
Matsuzaki, Y., Matsuzaki, Y., Sato, T., & Akiyama, Y. (2009) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720009004461 · Cited by 24
Modelling Self-Organization in Complex Networks Via a Brain-Inspired Network Automata Theory Improves Link Reliability in Protein Interactomes
Cannistraci, C. V. (2018) · Scientific Reports · doi:10.1038/s41598-018-33576-8 · Cited by 26
Denoising Protein–Protein interaction network via variational graph auto-encoder for protein complex detection
Yao, H., Guan, J., & Liu, T. (2020) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720020400107 · Cited by 23
Predicting gene essentiality in Caenorhabditis elegans by feature engineering and machine-learning
Campos, T. D. L., Korhonen, P. K., Sternberg, P. W., Gasser, R. B., & Young, N. D. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.05.008 · Cited by 23
CEGSO: Boosting Essential Proteins Prediction by Integrating Protein Complex, Gene Expression, Gene Ontology, Subcellular Localization and Orthology Information
Zhang, W., Xue, X., Xie, C., Li, Y., Liu, J., Chen, H., & Li, G. (2021) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-021-00426-7 · Cited by 23
Bridging topological and functional information in protein interaction networks by short loops profiling
Chung, S. S., Pandini, A., Annibale, A., Coolen, A. C. C., Thomas, N. S. B., & Fraternali, F. (2015) · Scientific Reports · doi:10.1038/srep08540 · Cited by 21
Completing sparse and disconnected protein-protein network by deep learning
Huang, L., Liao, L., & Wu, C. H. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2112-7 · Cited by 21
Cluster-based assessment of protein-protein interaction confidence
Kamburov, A., Großmann, A., Herwig, R., & Stelzl, U. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-262 · Cited by 22
Exploring Overlapping Functional Units with Various Structure in Protein Interaction Networks
Zhang, X.-F., Dai, D., Ou-Yang, L., & Wu, M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0043092 · Cited by 19
A New Method for Detecting Protein Complexes based on the Three Node Cliques
Zhang, W., & Zou, X. (2014) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2014.2386314 · Cited by 20
Forward Individualized Medicine from Personal Genomes to Interactomes
Zhang, X., Kuivenhoven, J. A., & Groen, A. K. (2015) · Frontiers in Physiology · doi:10.3389/fphys.2015.00364 · Cited by 22
GLIDE: combining local methods and diffusion state embeddings to predict missing interactions in biological networks
Devkota, K., Murphy, J. M., & Cowen, L. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa459 · Cited by 20
Great interactions: How binding incorrect partners can teach us about protein recognition and function
Vamparys, L., Laurent, B., Carbone, A., & Sacquin‐Mora, S. (2016) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25086 · Cited by 18
idenPC-MIIP: identify protein complexes from weighted PPI networks using mutual important interacting partner relation
Wu, Z., Liao, Q., & Liu, B. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa016 · Cited by 18
Identifying Spurious Interactions and Predicting Missing Interactions in the Protein-Protein Interaction Networks via a Generative Network Model
Zhu, Y., Zhang, X., Dai, D., & Wu, M. (2012) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2012.164 · Cited by 17
Using Information Content for Expanding Human Protein Coding Gene Interaction Networks
Fechete, R. (2013) · Journal of Computer Science & Systems Biology · doi:10.4172/jcsb.1000102 · Cited by 18
Global Geometric Affinity for Revealing High Fidelity Protein Interaction Network
Fang, Y., Benjamin, W., Sun, M., & Ramani, K. (2011) · PLoS ONE · doi:10.1371/journal.pone.0019349 · Cited by 18
Mining protein interactomes to improve their reliability and support the advancement of network medicine
Alanis‐Lobato, G. (2015) · Frontiers in Genetics · doi:10.3389/fgene.2015.00296 · Cited by 19
Combined use of feature engineering and machine-learning to predict essential genes in Drosophila melanogaster
Campos, T. D. L., Korhonen, P. K., Hofmann, A., Gasser, R. B., & Young, N. D. (2020) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaa051 · Cited by 18
Accurate multiple network alignment through context-sensitive random walk
Jeong, H., & Yoon, B.-J. (2015) · BMC Systems Biology · doi:10.1186/1752-0509-9-s1-s7 · Cited by 18
Identifying binary protein-protein interactions from affinity purification mass spectrometry data
Zhang, X.-F., Ou-Yang, L., Hu, X. T., & Dai, D. (2015) · BMC Genomics · doi:10.1186/s12864-015-1944-z · Cited by 17
A Bayesian Framework for Combining Protein and Network Topology Information for Predicting Protein-Protein Interactions
Birlutiu, A., d’Alché–Buc, F., & Heskes, T. (2014) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2014.2359441 · Cited by 17
Inference of protein-protein interaction networks from multiple heterogeneous data
Huang, L., Liao, L., & Wu, C. H. (2016) · EURASIP Journal on Bioinformatics and Systems Biology · doi:10.1186/s13637-016-0040-2 · Cited by 17
Prediction of comorbid diseases using weighted geometric embedding of human interactome
Akram, P., & Liao, L. (2019) · BMC Medical Genomics · doi:10.1186/s12920-019-0605-5 · Cited by 16
A scheme for inferring viral-host associations based on codon usage patterns identifies the most affected signaling pathways during COVID-19
Das, J. K., Chakraborty, S., & Roy, S. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103801 · Cited by 19
Hidden partners: Using cross‐docking calculations to predict binding sites for proteins with multiple interactions
Lagarde, N., Carbone, A., & Sacquin‐Mora, S. (2018) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25506 · Cited by 15
The Intrinsic Geometric Structure of Protein-Protein Interaction Networks for Protein Interaction Prediction
Fang, Y., Sun, M., Dai, G., & Ramain, K. (2015) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2015.2456876 · Cited by 15
Advancing from protein interactomes and gene co-expression networks towards multi-omics-based composite networks: approaches for predicting and extracting biological knowledge
Randhawa, V., & Pathania, S. (2020) · Briefings in Functional Genomics · doi:10.1093/bfgp/elaa015 · Cited by 15
Prediction of Protein-Protein Interaction Strength Using Domain Features with Supervised Regression
Kamada, M., Sakuma, Y., Hayashida, M., & Akutsu, T. (2014) · The Scientific World JOURNAL · doi:10.1155/2014/240673 · Cited by 14
Assessment of high-confidence protein–protein interactome in yeast
Karagoz, K., & Arğa, K. Y. (2013) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2013.03.002 · Cited by 13
Optimisation of the coalescent hyperbolic embedding of complex networks
Kovács, B., & Palla, G. (2021) · Scientific Reports · doi:10.1038/s41598-021-87333-5 · Cited by 12
Review of multimer protein–protein interaction complex topology and structure prediction*
Sun, D., Liu, S., & Gong, X. (2020) · Chinese Physics B · doi:10.1088/1674-1056/abb659 · Cited by 11
Model-independent embedding of directed networks into Euclidean and hyperbolic spaces
Kovács, B., & Palla, G. (2023) · Communications Physics · doi:10.1038/s42005-023-01143-x · Cited by 13
An overlapping module identification method in protein-protein interaction networks
Wang, X., Li, L., & Cheng, Y. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-s7-s4 · Cited by 12
Reliability Maximization in Uncertain Graphs
Ke, X., Khan, A., Hasan, M. A., & Rezvansangsari, R. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.2987570 · Cited by 12

Authors: Kuchaiev, O., Rašajski, M., Higham, D. J., & Pržulj, N.
Affiliations: University of California, Irvine (US)
University of Strathclyde (GB)
Corresponding author: Pržulj, Nataša
ORCID: Rašajski, Marija
Higham, Desmond J.
Pržulj, Nataša
Venue: PLoS Computational Biology
Publisher: Public Library of Science (PLoS)
Published: 2009-08-07
Volume: 5
Issue: 8
Pages: e1000454
ISSN: 1553-734X (ISSN-L)
1553-7358
DOI: 10.1371/journal.pcbi.1000454
PMC: PMC2711306
OpenAlex: W2050583899
Cited by: 180
References: 55
Citations by year: 2026: 1 · 2025: 4 · 2024: 4 · 2023: 7 · 2022: 9 · 2021: 11 · 2020: 13 · 2019: 10 · 2018: 9 · 2017: 8 · 2016: 11 · 2015: 18 · 2014: 13 · 2013: 17 · 2012: 24
FWCI: 6.03
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (unspecified)
Locations: PLoS Computational Biology (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
UCL Discovery (University College London) (submittedVersion)
eScholarship (California Digital Library) (submittedVersion, cc-by)
Figshare (submittedVersion, cc-by)
Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) (submittedVersion)
Funders: National Science Foundation (0644424, CAREER)
Engineering and Physical Sciences Research Council (GR/S62383/01, EP/E049370/1)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
Keywords: protein-protein interaction network, geometric graphs, interaction prediction, false positive interactions, BioGRID, Gene Ontology terms, HPRD
MeSH terms: Algorithms, Humans, Models, Biological, Proteins, ROC Curve, Sensitivity and Specificity, Reproducibility of Results, Signal Transduction, Computational Biology, Protein Interaction Mapping, Databases, Protein

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Geometric De-noising of Protein-Protein Interaction Networks

Abstract

Overview

Integrity signals

Figures

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks