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For: Romero-Molina S, Ruiz-Blanco YB, Harms M, Münch J, Sanchez-Garcia E. PPI-Detect: A support vector machine model for sequence-based prediction of protein-protein interactions. J Comput Chem 2019;40:1233-42. [PMID: 30768790 DOI: 10.1002/jcc.25780] [Cited by in Crossref: 33] [Cited by in F6Publishing: 34] [Article Influence: 11.0] [Reference Citation Analysis]
Number Citing Articles
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5 Romero-Molina S, Ruiz-Blanco YB, Mieres-Perez J, Harms M, Münch J, Ehrmann M, Sanchez-Garcia E. PPI-Affinity: A Web Tool for the Prediction and Optimization of Protein-Peptide and Protein-Protein Binding Affinity. J Proteome Res 2022. [PMID: 35654412 DOI: 10.1021/acs.jproteome.2c00020] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
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8 Avila-barrientos LP, Cofas-vargas LF, Agüero-chapin G, Hernández-garcía E, Ruiz-carmona S, Valdez-cruz NA, Trujillo-roldán M, Weber J, Ruiz-blanco YB, Barril X, García-hernández E. Computational Design of Inhibitors Targeting the Catalytic β Subunit of Escherichia coli FOF1-ATP Synthase. Antibiotics 2022;11:557. [DOI: 10.3390/antibiotics11050557] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
9 Zhou Y, Xie S, Yang Y, Jiang L, Liu S, Li W, Abagna HB, Ning L, Huang J. SSH2.0: A Better Tool for Predicting the Hydrophobic Interaction Risk of Monoclonal Antibody. Front Genet 2022;13:842127. [PMID: 35368659 DOI: 10.3389/fgene.2022.842127] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Delaunay M, Ha-Duong T. Computational Tools and Strategies to Develop Peptide-Based Inhibitors of Protein-Protein Interactions. Methods Mol Biol 2022;2405:205-30. [PMID: 35298816 DOI: 10.1007/978-1-0716-1855-4_11] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 Pan J, You Z, Li L, Huang W, Guo J, Yu C, Wang L, Zhao Z. DWPPI: A Deep Learning Approach for Predicting Protein–Protein Interactions in Plants Based on Multi-Source Information With a Large-Scale Biological Network. Front Bioeng Biotechnol 2022;10:807522. [DOI: 10.3389/fbioe.2022.807522] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
12 Nasim Z, Susila H, Jin S, Youn G, Ahn JH. Polymerase II-Associated Factor 1 Complex-Regulated FLOWERING LOCUS C-Clade Genes Repress Flowering in Response to Chilling. Front Plant Sci 2022;13:817356. [PMID: 35222476 DOI: 10.3389/fpls.2022.817356] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
13 Modee R, Laghuvarapu S, Priyakumar UD. Benchmark study on deep neural network potentials for small organic molecules. J Comput Chem 2021. [PMID: 34870332 DOI: 10.1002/jcc.26790] [Reference Citation Analysis]
14 Qiu J, Chen K, Zhong C, Zhu S, Ma X. Network-based protein-protein interaction prediction method maps perturbations of cancer interactome. PLoS Genet 2021;17:e1009869. [PMID: 34727106 DOI: 10.1371/journal.pgen.1009869] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
15 Mei S. A framework combines supervised learning and dense subgraphs discovery to predict protein complexes. Front Comput Sci 2022;16. [DOI: 10.1007/s11704-021-0476-8] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
16 Perpetuo L, Klein J, Ferreira R, Guedes S, Amado F, Leite-Moreira A, Silva AMS, Thongboonkerd V, Vitorino R. How can artificial intelligence be used for peptidomics? Expert Rev Proteomics 2021. [PMID: 34343059 DOI: 10.1080/14789450.2021.1962303] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
17 Biggar KK, Charih F, Liu H, Ruiz-Blanco YB, Stalker L, Chopra A, Connolly J, Adhikary H, Frensemier K, Hoekstra M, Galka M, Fang Q, Wynder C, Stanford WL, Green JR, Li SS. Proteome-wide Prediction of Lysine Methylation Leads to Identification of H2BK43 Methylation and Outlines the Potential Methyllysine Proteome. Cell Rep 2020;32:107896. [PMID: 32668242 DOI: 10.1016/j.celrep.2020.107896] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 9.0] [Reference Citation Analysis]
18 Pei F, Shi Q, Zhang H, Bahar I. Predicting Protein-Protein Interactions Using Symmetric Logistic Matrix Factorization. J Chem Inf Model 2021;61:1670-82. [PMID: 33831302 DOI: 10.1021/acs.jcim.1c00173] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
19 Lazim R, Suh D, Lee JW, Vu TNL, Yoon S, Choi S. Structural Characterization of Receptor-Receptor Interactions in the Allosteric Modulation of G Protein-Coupled Receptor (GPCR) Dimers. Int J Mol Sci 2021;22:3241. [PMID: 33810175 DOI: 10.3390/ijms22063241] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
20 Tsukiyama S, Hasan MM, Fujii S, Kurata H. LSTM-PHV: Prediction of human-virus protein-protein interactions by LSTM with word2vec.. [DOI: 10.1101/2021.02.26.432975] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
21 Su XR, You ZH, Hu L, Huang YA, Wang Y, Yi HC. An Efficient Computational Model for Large-Scale Prediction of Protein-Protein Interactions Based on Accurate and Scalable Graph Embedding. Front Genet 2021;12:635451. [PMID: 33719344 DOI: 10.3389/fgene.2021.635451] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
22 Su X, You Z, Chen Z, Yi H, Guo Z. Protein-Protein Interaction Prediction by Integrating Sequence Information and Heterogeneous Network Representation. Intelligent Computing Theories and Application 2021. [DOI: 10.1007/978-3-030-84532-2_55] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
23 Meng F, Liang Z, Zhao K, Luo C. Drug design targeting active posttranslational modification protein isoforms. Med Res Rev 2021;41:1701-50. [PMID: 33355944 DOI: 10.1002/med.21774] [Cited by in Crossref: 12] [Cited by in F6Publishing: 13] [Article Influence: 6.0] [Reference Citation Analysis]
24 Wu L, Han L, Li Q, Wang G, Zhang H, Li L. Using Interactome Big Data to Crack Genetic Mysteries and Enhance Future Crop Breeding. Mol Plant 2021;14:77-94. [PMID: 33340690 DOI: 10.1016/j.molp.2020.12.012] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 6.0] [Reference Citation Analysis]
25 Ruiz-Blanco YB, Ávila-Barrientos LP, Hernández-García E, Antunes A, Agüero-Chapin G, García-Hernández E. Engineering protein fragments via evolutionary and protein-protein interaction algorithms: de novo design of peptide inhibitors for FO F1 -ATP synthase. FEBS Lett 2021;595:183-94. [PMID: 33151544 DOI: 10.1002/1873-3468.13988] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
26 Harms M, Gilg A, Ständker L, Beer AJ, Mayer B, Rasche V, Gruber CW, Münch J. Microtiter plate-based antibody-competition assay to determine binding affinities and plasma/blood stability of CXCR4 ligands. Sci Rep 2020;10:16036. [PMID: 32994431 DOI: 10.1038/s41598-020-73012-4] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 4.0] [Reference Citation Analysis]
27 Han Y, Cheng L, Sun W. Analysis of Protein-Protein Interaction Networks through Computational Approaches. Protein Pept Lett 2020;27:265-78. [PMID: 31692419 DOI: 10.2174/0929866526666191105142034] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
28 Khatun MS, Shoombuatong W, Hasan MM, Kurata H. Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction. Curr Genomics 2020;21:454-63. [PMID: 33093807 DOI: 10.2174/1389202921999200625103936] [Cited by in Crossref: 16] [Cited by in F6Publishing: 17] [Article Influence: 8.0] [Reference Citation Analysis]
29 Harms M, Gilg A, Ständker L, Beer AJ, Mayer B, Rasche V, Gruber CW, Münch J. Microtiter plate-based antibody-competition assay to determine binding affinities and plasma/blood stability of CXCR4 ligands.. [DOI: 10.1101/2020.07.25.221085] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
30 Romero-Molina S, Ruiz-Blanco YB, Green JR, Sanchez-Garcia E. ProtDCal-Suite: A web server for the numerical codification and functional analysis of proteins. Protein Sci 2019;28:1734-43. [PMID: 31271472 DOI: 10.1002/pro.3673] [Cited by in Crossref: 7] [Cited by in F6Publishing: 10] [Article Influence: 3.5] [Reference Citation Analysis]
31 Chou K. Gordon Life Science Institute and Its Impacts on Computational Biology and Drug Development. NS 2020;12:125-61. [DOI: 10.4236/ns.2020.123013] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
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33 Laghuvarapu S, Pathak Y, Priyakumar UD. BAND NN: A Deep Learning Framework for Energy Prediction and Geometry Optimization of Organic Small Molecules. J Comput Chem 2019;41:790-9. [DOI: 10.1002/jcc.26128] [Cited by in Crossref: 17] [Cited by in F6Publishing: 17] [Article Influence: 5.7] [Reference Citation Analysis]
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