Articles dans des revues avec comité de lecture (28)
  1. 1. Raimondi, D., Orlando, G., Pancsa, R., Khanna, T., & Vranken, W. (2017). Exploring the Sequence-based Prediction of Folding Initiation Sites in Proteins. Scientific reports, 7(1), 8826. doi:10.1038/s41598-017-08366-3
  2. 2. Kagami, L. P., Roca-Martínez, J., Gavaldá-García, J., Ramasamy, P., Feenstra, K. A., & Vranken, W. (2021). Online biophysical predictions for SARS-CoV-2 proteins. BMC Molecular and Cell Biology, 22(1), 23. doi:10.1186/s12860-021-00362-w
  3. 3. Kagami, L. P., Orlando, G., Raimondi, D., Ancien, F., Dixit, B., Gavaldá-García, J., Ramasamy, P., Roca-Martínez, J., Tzavella, K., & Vranken, W. (2021). B2bTools: Online predictions for protein biophysical features and their conservation. Nucleic acids research, 49(W1), W52-W59. doi:10.1093/nar/gkab425
  4. 4. Piovesan, D., Necci, M., Escobedo, N., Monzon, A. M., Hatos, A., Mičetić, I., Quaglia, F., Paladin, L., Ramasamy, P., Dosztányi, Z., Vranken, W., Davey, N. N., Parisi, G., Fuxreiter, M., & Tosatto, S. S. (2021). MobiDB: Intrinsically disordered proteins in 2021. Nucleic acids research, 49(D1), D361-D367. doi:10.1093/nar/gkaa1058
  5. 5. Orlando, G., Raimondi, D., Kagami, L. P., & Vranken, W. (2020). ShiftCrypt: a web server to understand and biophysically align proteins through their NMR chemical shift values. Nucleic acids research, 48(W1), W36-W40. doi:10.1093/nar/gkaa391
  6. 6. Raimondi, D., Orlando, G., Vranken, W., & Moreau, Y. (2019). Exploring the limitations of biophysical propensity scales coupled with machine learning for protein sequence analysis. Scientific reports, 9(1), 16932. doi:10.1038/s41598-019-53324-w
  7. 7. Loos, M. M., Ramakrishnan, R., Vranken, W., Tsirigotaki, A., Tsare, E. P., Zorzini, V., De Geyter, J., Yuan, B., Tsamardinos, I., Klappa, M., Schymkowitz, J. J., Rousseau, F., Karamanou, S., & Economou, A. (2019). Structural basis of the subcellular topology landscape of Escherichia coli. Frontiers in microbiology, 10, 1670, 1-22. doi:10.3389/fmicb.2019.01670
  8. 8. Varadi, M., De Baets, G., Vranken, W., Tompa, P., & Pancsa, R. (2018). AmyPro: A database of proteins with validated amyloidogenic regions. Nucleic acids research, 46(D1), D387-D392. doi:10.1093/nar/gkx950
  9. 9. Piovesan, D., Monzon, A. M., Parisi, G., Schad, E., Sormanni, P., Tompa, P., Vendruscolo, M., Vranken, W., Tosatto, S. S., Tabaro, F., Paladin, L., Necci, M., Mieti, I., Camilloni, C., Davey, N. N., Dosztányi, Z., & Mészáros, B. (2018). MobiDB 3.0: More annotations for intrinsic disorder, conformational diversity and interactions in proteins. Nucleic acids research, 46(D1), D471-D476. doi:10.1093/nar/gkx1071
  10. 10. Brysbaert, G., Lorgouilloux, K., Vranken, W., & Lensink, M. (2018). RINspector: A Cytoscape app for centrality analyses and DynaMine flexibility prediction. Bioinformatics, 34(2), 294-296. doi:10.1093/bioinformatics/btx586
  11. 11. Raimondi, D., Orlando, G., Moreau, Y., & Vranken, W. (2018). Ultra-fast global homology detection with Discrete Cosine Transform and Dynamic Time Warping. Bioinformatics, 34(18), 3118-3125. doi:10.1093/bioinformatics/bty309
  12. 12. Hou, Q., De Geest, P. P., Vranken, W., Heringa, J., & Feenstra, A. K. (2017). Seeing the trees through the forest: Sequencebased homo- and heteromeric protein-protein interaction sites prediction using random forest. Bioinformatics, 33(10), 1479-1487. doi:10.1093/bioinformatics/btx005
    13. << Précédent 1 2 3 Suivant >>