Advanced Search
Display options
Filter resources
Text Availability
Article type
Publication date
Species
Language
Sex
Age
Showing 1 to 12 of 48 entries
Sorted by: Best Match Show Resources per page
2P2I HUNTER: a tool for filtering orthosteric protein-protein interaction modulators via a dedicated support vector machine.

Journal of the Royal Society, Interface

Hamon V, Bourgeas R, Ducrot P, Theret I, Xuereb L, Basse MJ, Brunel JM, Combes S, Morelli X, Roche P.
PMID: 24196694
J R Soc Interface. 2013 Nov 06;11(90):20130860. doi: 10.1098/rsif.2013.0860. Print 2014 Jan 06.

Over the last 10 years, protein-protein interactions (PPIs) have shown increasing potential as new therapeutic targets. As a consequence, PPIs are today the most screened target class in high-throughput screening (HTS). The development of broad chemical libraries dedicated to...

Cite
Hamon V, Bourgeas R, Ducrot P, et al. 2P2I HUNTER: a tool for filtering orthosteric protein-protein interaction modulators via a dedicated support vector machine. J R Soc Interface. 2013;11(90):20130860doi: 10.1098/rsif.2013.0860.
Hamon, V., Bourgeas, R., Ducrot, P., Theret, I., Xuereb, L., Basse, M. J., Brunel, J. M., Combes, S., Morelli, X., & Roche, P. (2014). 2P2I HUNTER: a tool for filtering orthosteric protein-protein interaction modulators via a dedicated support vector machine. Journal of the Royal Society, Interface, 11(90), 20130860. https://doi.org/10.1098/rsif.2013.0860
Hamon, Véronique, et al. "2P2I HUNTER: a tool for filtering orthosteric protein-protein interaction modulators via a dedicated support vector machine." Journal of the Royal Society, Interface vol. 11,90 (2014): 20130860. doi: https://doi.org/10.1098/rsif.2013.0860
Hamon V, Bourgeas R, Ducrot P, Theret I, Xuereb L, Basse MJ, Brunel JM, Combes S, Morelli X, Roche P. 2P2I HUNTER: a tool for filtering orthosteric protein-protein interaction modulators via a dedicated support vector machine. J R Soc Interface. 2013 Nov 06;11(90):20130860. doi: 10.1098/rsif.2013.0860. Print 2014 Jan 06. PMID: 24196694; PMCID: PMC3836326.
Copy Download .nbib Format: NLM
BioNet: an R-Package for the functional analysis of biological networks.

Bioinformatics (Oxford, England)

Beisser D, Klau GW, Dandekar T, Müller T, Dittrich MT.
PMID: 20189939
Bioinformatics. 2010 Apr 15;26(8):1129-30. doi: 10.1093/bioinformatics/btq089. Epub 2010 Feb 25.

MOTIVATION: Increasing quantity and quality of data in transcriptomics and interactomics create the need for integrative approaches to network analysis. Here, we present a comprehensive R-package for the analysis of biological networks including an exact and a heuristic approach...

Cite
Beisser D, Klau GW, Dandekar T, et al. BioNet: an R-Package for the functional analysis of biological networks. Bioinformatics. 2010;26(8):1129-30doi: 10.1093/bioinformatics/btq089.
Beisser, D., Klau, G. W., Dandekar, T., Müller, T., & Dittrich, M. T. (2010). BioNet: an R-Package for the functional analysis of biological networks. Bioinformatics (Oxford, England), 26(8), 1129-30. https://doi.org/10.1093/bioinformatics/btq089
Beisser, Daniela, et al. "BioNet: an R-Package for the functional analysis of biological networks." Bioinformatics (Oxford, England) vol. 26,8 (2010): 1129-30. doi: https://doi.org/10.1093/bioinformatics/btq089
Beisser D, Klau GW, Dandekar T, Müller T, Dittrich MT. BioNet: an R-Package for the functional analysis of biological networks. Bioinformatics. 2010 Apr 15;26(8):1129-30. doi: 10.1093/bioinformatics/btq089. Epub 2010 Feb 25. PMID: 20189939.
Copy Download .nbib Format: NLM
HAPPI: an online database of comprehensive human annotated and predicted protein interactions.

BMC genomics

Chen JY, Mamidipalli S, Huan T.
PMID: 19594875
BMC Genomics. 2009 Jul 07;10:S16. doi: 10.1186/1471-2164-10-S1-S16.

BACKGROUND: Human protein-protein interaction (PPIs) data are the foundation for understanding molecular signalling networks and the functional roles of biomolecules. Several human PPI databases have become available; however, comparisons of these datasets have suggested limited data coverage and poor...

Cite
Chen JY, Mamidipalli S, Huan T. HAPPI: an online database of comprehensive human annotated and predicted protein interactions. BMC Genomics. 2009;10:S16doi: 10.1186/1471-2164-10-S1-S16.
Chen, J. Y., Mamidipalli, S., & Huan, T. (2009). HAPPI: an online database of comprehensive human annotated and predicted protein interactions. BMC genomics, 10S16. https://doi.org/10.1186/1471-2164-10-S1-S16
Chen, Jake Yue, et al. "HAPPI: an online database of comprehensive human annotated and predicted protein interactions." BMC genomics vol. 10 (2009): S16. doi: https://doi.org/10.1186/1471-2164-10-S1-S16
Chen JY, Mamidipalli S, Huan T. HAPPI: an online database of comprehensive human annotated and predicted protein interactions. BMC Genomics. 2009 Jul 07;10:S16. doi: 10.1186/1471-2164-10-S1-S16. PMID: 19594875; PMCID: PMC2709259.
Copy Download .nbib Format: NLM
Score_set: a CAPRI benchmark for scoring protein complexes.

Proteins

Lensink MF, Wodak SJ.
PMID: 25179222
Proteins. 2014 Nov;82(11):3163-9. doi: 10.1002/prot.24678. Epub 2014 Sep 11.

Critical Assessment of PRedicted Interactions (CAPRI) has proven to be a catalyst for the development of docking algorithms. An essential step in docking is the scoring of predicted binding modes in order to identify stable complexes. In 2005, CAPRI...

Cite
Lensink MF, Wodak SJ. Score_set: a CAPRI benchmark for scoring protein complexes. Proteins. 2014;82(11):3163-9doi: 10.1002/prot.24678.
Lensink, M. F., & Wodak, S. J. (2014). Score_set: a CAPRI benchmark for scoring protein complexes. Proteins, 82(11), 3163-9. https://doi.org/10.1002/prot.24678
Lensink, Marc F, and Wodak, Shoshana J. "Score_set: a CAPRI benchmark for scoring protein complexes." Proteins vol. 82,11 (2014): 3163-9. doi: https://doi.org/10.1002/prot.24678
Lensink MF, Wodak SJ. Score_set: a CAPRI benchmark for scoring protein complexes. Proteins. 2014 Nov;82(11):3163-9. doi: 10.1002/prot.24678. Epub 2014 Sep 11. PMID: 25179222.
Copy Download .nbib Format: NLM
Detecting complexes from edge-weighted PPI networks via genes expression analysis.

BMC systems biology

Zhang Z, Song J, Tang J, Xu X, Guo F.
PMID: 29745859
BMC Syst Biol. 2018 Apr 24;12:40. doi: 10.1186/s12918-018-0565-y.

BACKGROUND: Identifying complexes from PPI networks has become a key problem to elucidate protein functions and identify signal and biological processes in a cell. Proteins binding as complexes are important roles of life activity. Accurate determination of complexes in...

Cite
Zhang Z, Song J, Tang J, et al. Detecting complexes from edge-weighted PPI networks via genes expression analysis. BMC Syst Biol. 2018;12:40doi: 10.1186/s12918-018-0565-y.
Zhang, Z., Song, J., Tang, J., Xu, X., & Guo, F. (2018). Detecting complexes from edge-weighted PPI networks via genes expression analysis. BMC systems biology, 1240. https://doi.org/10.1186/s12918-018-0565-y
Zhang, Zehua, et al. "Detecting complexes from edge-weighted PPI networks via genes expression analysis." BMC systems biology vol. 12 (2018): 40. doi: https://doi.org/10.1186/s12918-018-0565-y
Zhang Z, Song J, Tang J, Xu X, Guo F. Detecting complexes from edge-weighted PPI networks via genes expression analysis. BMC Syst Biol. 2018 Apr 24;12:40. doi: 10.1186/s12918-018-0565-y. PMID: 29745859; PMCID: PMC5998908.
Copy Download .nbib Format: NLM
Highly Efficient Framework for Predicting Interactions Between Proteins.

IEEE transactions on cybernetics

Zhu-Hong You, MengChu Zhou, Xin Luo, Shuai Li.
PMID: 28113829
IEEE Trans Cybern. 2017 Mar;47(3):731-743. doi: 10.1109/TCYB.2016.2524994. Epub 2016 Mar 30.

Protein-protein interactions (PPIs) play a central role in many biological processes. Although a large amount of human PPI data has been generated by high-throughput experimental techniques, they are very limited compared to the estimated 130 000 protein interactions in...

Cite
Zhu-Hong You, MengChu Zhou, Xin Luo, et al. Highly Efficient Framework for Predicting Interactions Between Proteins. IEEE Trans Cybern. 2016;47(3):731-743doi: 10.1109/TCYB.2016.2524994.
Zhu-Hong You, MengChu Zhou, Xin Luo, & Shuai Li. (2017). Highly Efficient Framework for Predicting Interactions Between Proteins. IEEE transactions on cybernetics, 47(3), 731-743. https://doi.org/10.1109/TCYB.2016.2524994
Zhu-Hong You, et al. "Highly Efficient Framework for Predicting Interactions Between Proteins." IEEE transactions on cybernetics vol. 47,3 (2017): 731-743. doi: https://doi.org/10.1109/TCYB.2016.2524994
Zhu-Hong You, MengChu Zhou, Xin Luo, Shuai Li. Highly Efficient Framework for Predicting Interactions Between Proteins. IEEE Trans Cybern. 2017 Mar;47(3):731-743. doi: 10.1109/TCYB.2016.2524994. Epub 2016 Mar 30. PMID: 28113829.
Copy Download .nbib Format: NLM
A systematic approach to orient the human protein-protein interaction network.

Nature communications

Silverbush D, Sharan R.
PMID: 31289271
Nat Commun. 2019 Jul 09;10(1):3015. doi: 10.1038/s41467-019-10887-6.

The protein-protein interaction (PPI) network of an organism serves as a skeleton for its signaling circuitry, which mediates cellular response to environmental and genetic cues. Understanding this circuitry could improve the prediction of gene function and cellular behavior in...

Cite
Silverbush D, Sharan R. A systematic approach to orient the human protein-protein interaction network. Nat Commun. 2019;10(1):3015doi: 10.1038/s41467-019-10887-6.
Silverbush, D., & Sharan, R. (2019). A systematic approach to orient the human protein-protein interaction network. Nature communications, 10(1), 3015. https://doi.org/10.1038/s41467-019-10887-6
Silverbush, Dana, and Sharan, Roded. "A systematic approach to orient the human protein-protein interaction network." Nature communications vol. 10,1 (2019): 3015. doi: https://doi.org/10.1038/s41467-019-10887-6
Silverbush D, Sharan R. A systematic approach to orient the human protein-protein interaction network. Nat Commun. 2019 Jul 09;10(1):3015. doi: 10.1038/s41467-019-10887-6. PMID: 31289271; PMCID: PMC6617457.
Copy Download .nbib Format: NLM
Weighted edge based clustering to identify protein complexes in protein-protein interaction networks incorporating gene expression profile.

Computational biology and chemistry

Keretsu S, Sarmah R.
PMID: 27771556
Comput Biol Chem. 2016 Dec;65:69-79. doi: 10.1016/j.compbiolchem.2016.10.001. Epub 2016 Oct 08.

Protein complex detection from protein-protein interaction (PPI) network has received a lot of focus in recent years. A number of methods identify protein complexes as dense sub-graphs using network information while several other methods detect protein complexes based on...

Cite
Keretsu S, Sarmah R. Weighted edge based clustering to identify protein complexes in protein-protein interaction networks incorporating gene expression profile. Comput Biol Chem. 2016;65:69-79doi: 10.1016/j.compbiolchem.2016.10.001.
Keretsu, S., & Sarmah, R. (2016). Weighted edge based clustering to identify protein complexes in protein-protein interaction networks incorporating gene expression profile. Computational biology and chemistry, 6569-79. https://doi.org/10.1016/j.compbiolchem.2016.10.001
Keretsu, Seketoulie, and Sarmah, Rosy. "Weighted edge based clustering to identify protein complexes in protein-protein interaction networks incorporating gene expression profile." Computational biology and chemistry vol. 65 (2016): 69-79. doi: https://doi.org/10.1016/j.compbiolchem.2016.10.001
Keretsu S, Sarmah R. Weighted edge based clustering to identify protein complexes in protein-protein interaction networks incorporating gene expression profile. Comput Biol Chem. 2016 Dec;65:69-79. doi: 10.1016/j.compbiolchem.2016.10.001. Epub 2016 Oct 08. PMID: 27771556.
Copy Download .nbib Format: NLM
The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction.

Biomolecules

Li H, Peng J, Leung Y, Leung KS, Wong MH, Lu G, Ballester PJ.
PMID: 29538331
Biomolecules. 2018 Mar 14;8(1). doi: 10.3390/biom8010012.

It has recently been claimed that the outstanding performance of machine-learning scoring functions (SFs) is exclusively due to the presence of training complexes with highly similar proteins to those in the test set. Here, we revisit this question using...

Cite
Li H, Peng J, Leung Y, et al. The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction. Biomolecules. 2018;8(1)doi: 10.3390/biom8010012.
Li, H., Peng, J., Leung, Y., Leung, K. S., Wong, M. H., Lu, G., & Ballester, P. J. (2018). The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction. Biomolecules, 8(1), . https://doi.org/10.3390/biom8010012
Li, Hongjian, et al. "The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction." Biomolecules vol. 8,1 (2018). doi: https://doi.org/10.3390/biom8010012
Li H, Peng J, Leung Y, Leung KS, Wong MH, Lu G, Ballester PJ. The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction. Biomolecules. 2018 Mar 14;8(1). doi: 10.3390/biom8010012. PMID: 29538331; PMCID: PMC5871981.
Copy Download .nbib Format: NLM
PICKLE 2.0: A human protein-protein interaction meta-database employing data integration via genetic information ontology.

PloS one

Gioutlakis A, Klapa MI, Moschonas NK.
PMID: 29023571
PLoS One. 2017 Oct 12;12(10):e0186039. doi: 10.1371/journal.pone.0186039. eCollection 2017.

It has been acknowledged that source databases recording experimentally supported human protein-protein interactions (PPIs) exhibit limited overlap. Thus, the reconstruction of a comprehensive PPI network requires appropriate integration of multiple heterogeneous primary datasets, presenting the PPIs at various genetic...

Cite
Gioutlakis A, Klapa MI, Moschonas NK. PICKLE 2.0: A human protein-protein interaction meta-database employing data integration via genetic information ontology. PLoS One. 2017;12(10):e0186039doi: 10.1371/journal.pone.0186039.
Gioutlakis, A., Klapa, M. I., & Moschonas, N. K. (2017). PICKLE 2.0: A human protein-protein interaction meta-database employing data integration via genetic information ontology. PloS one, 12(10), e0186039. https://doi.org/10.1371/journal.pone.0186039
Gioutlakis, Aris, et al. "PICKLE 2.0: A human protein-protein interaction meta-database employing data integration via genetic information ontology." PloS one vol. 12,10 (2017): e0186039. doi: https://doi.org/10.1371/journal.pone.0186039
Gioutlakis A, Klapa MI, Moschonas NK. PICKLE 2.0: A human protein-protein interaction meta-database employing data integration via genetic information ontology. PLoS One. 2017 Oct 12;12(10):e0186039. doi: 10.1371/journal.pone.0186039. eCollection 2017. PMID: 29023571; PMCID: PMC5638325.
Copy Download .nbib Format: NLM
ITM Probe: analyzing information flow in protein networks.

Bioinformatics (Oxford, England)

Stojmirović A, Yu YK.
PMID: 19561335
Bioinformatics. 2009 Sep 15;25(18):2447-9. doi: 10.1093/bioinformatics/btp398. Epub 2009 Jun 27.

SUMMARY: Founded upon diffusion with damping, ITM Probe is an application for modeling information flow in protein interaction networks without prior restriction to the sub-network of interest. Given a context consisting of desired origins and destinations of information, ITM...

Cite
Stojmirović A, Yu YK. ITM Probe: analyzing information flow in protein networks. Bioinformatics. 2009;25(18):2447-9doi: 10.1093/bioinformatics/btp398.
Stojmirović, A., & Yu, Y. K. (2009). ITM Probe: analyzing information flow in protein networks. Bioinformatics (Oxford, England), 25(18), 2447-9. https://doi.org/10.1093/bioinformatics/btp398
Stojmirović, Aleksandar, and Yu, Yi-Kuo. "ITM Probe: analyzing information flow in protein networks." Bioinformatics (Oxford, England) vol. 25,18 (2009): 2447-9. doi: https://doi.org/10.1093/bioinformatics/btp398
Stojmirović A, Yu YK. ITM Probe: analyzing information flow in protein networks. Bioinformatics. 2009 Sep 15;25(18):2447-9. doi: 10.1093/bioinformatics/btp398. Epub 2009 Jun 27. PMID: 19561335; PMCID: PMC2735661.
Copy Download .nbib Format: NLM
Do protein-protein interaction databases identify moonlighting proteins?.

Molecular bioSystems

Gómez A, Hernández S, Amela I, Piñol J, Cedano J, Querol E.
PMID: 21677976
Mol Biosyst. 2011 Aug;7(8):2379-82. doi: 10.1039/c1mb05180f. Epub 2011 Jun 16.

One of the most striking results of the human (and mammalian) genomes is the low number of protein-coding genes. To-date, the main molecular mechanism to increase the number of different protein isoforms and functions is alternative splicing. However, a...

Cite
Gómez A, Hernández S, Amela I, et al. Do protein-protein interaction databases identify moonlighting proteins?. Mol Biosyst. 2011;7(8):2379-82doi: 10.1039/c1mb05180f.
Gómez, A., Hernández, S., Amela, I., Piñol, J., Cedano, J., & Querol, E. (2011). Do protein-protein interaction databases identify moonlighting proteins?. Molecular bioSystems, 7(8), 2379-82. https://doi.org/10.1039/c1mb05180f
Gómez, Antonio, et al. "Do protein-protein interaction databases identify moonlighting proteins?." Molecular bioSystems vol. 7,8 (2011): 2379-82. doi: https://doi.org/10.1039/c1mb05180f
Gómez A, Hernández S, Amela I, Piñol J, Cedano J, Querol E. Do protein-protein interaction databases identify moonlighting proteins?. Mol Biosyst. 2011 Aug;7(8):2379-82. doi: 10.1039/c1mb05180f. Epub 2011 Jun 16. PMID: 21677976.
Copy Download .nbib Format: NLM
Showing 1 to 12 of 48 entries