| Critical assessment of protein intrinsic disorder prediction M Necci, D Piovesan, SCE Tosatto Nature methods 18 (5), 472-481, 2021 | 222 | 2021 |
| DEOGEN2: prediction and interactive visualization of single amino acid variant deleteriousness in human proteins D Raimondi, I Tanyalcin, J Ferté, A Gazzo, G Orlando, T Lenaerts, ... Nucleic acids research 45 (W1), W201-W206, 2017 | 140 | 2017 |
| Structure-based machine-guided mapping of amyloid sequence space reveals uncharted sequence clusters with higher solubilities N Louros, G Orlando, M De Vleeschouwer, F Rousseau, J Schymkowitz Nature communications 11 (1), 3314, 2020 | 60 | 2020 |
| Computational identification of prion-like RNA-binding proteins that form liquid phase-separated condensates G Orlando, D Raimondi, F Tabaro, F Codice, Y Moreau, WF Vranken Bioinformatics 35 (22), 4617-4623, 2019 | 49 | 2019 |
| Exploring the sequence-based prediction of folding initiation sites in proteins D Raimondi, G Orlando, R Pancsa, T Khan, WF Vranken Scientific reports 7 (1), 8826, 2017 | 48 | 2017 |
| Prediction of disordered regions in proteins with recurrent neural networks and protein dynamics G Orlando, D Raimondi, F Codice, F Tabaro, W Vranken Journal of Molecular Biology 434 (12), 167579, 2022 | 37 | 2022 |
| Role and therapeutic potential of liquid–liquid phase separation in amyotrophic lateral sclerosis D Pakravan, G Orlando, V Bercier, L Van Den Bosch Journal of molecular cell biology 13 (1), 15-28, 2021 | 34 | 2021 |
| Insight into the protein solubility driving forces with neural attention D Raimondi, G Orlando, P Fariselli, Y Moreau PLoS computational biology 16 (4), e1007722, 2020 | 30 | 2020 |
| Accurate prediction of protein beta-aggregation with generalized statistical potentials G Orlando, A Silva, S Macedo-Ribeiro, D Raimondi, W Vranken Bioinformatics 36 (7), 2076-2081, 2020 | 24 | 2020 |
| Observation selection bias in contact prediction and its implications for structural bioinformatics G Orlando, D Raimondi, WF Vranken Scientific Reports 6 (1), 36679, 2016 | 22 | 2016 |
| Exploring the limitations of biophysical propensity scales coupled with machine learning for protein sequence analysis D Raimondi, G Orlando, WF Vranken, Y Moreau Scientific reports 9 (1), 16932, 2019 | 21 | 2019 |
| PyUUL provides an interface between biological structures and deep learning algorithms G Orlando, D Raimondi, R Duran-Romaña, Y Moreau, J Schymkowitz, ... Nature communications 13 (1), 961, 2022 | 18 | 2022 |
| In silico prediction of in vitro protein liquid–liquid phase separation experiments outcomes with multi-head neural attention D Raimondi, G Orlando, E Michiels, D Pakravan, A Bratek-Skicki, ... Bioinformatics 37 (20), 3473-3479, 2021 | 16 | 2021 |
| Ultra-fast global homology detection with discrete cosine transform and dynamic time warping D Raimondi, G Orlando, Y Moreau, WF Vranken Bioinformatics 34 (18), 3118-3125, 2018 | 14 | 2018 |
| SVM-dependent pairwise HMM: an application to protein pairwise alignments G Orlando, D Raimondi, T Khan, T Lenaerts, WF Vranken Bioinformatics 33 (24), 3902-3908, 2017 | 12 | 2017 |
| Clustering-based model of cysteine co-evolution improves disulfide bond connectivity prediction and reduces homologous sequence requirements D Raimondi, G Orlando, WF Vranken Bioinformatics 31 (8), 1219-1225, 2015 | 12 | 2015 |
| Large-scale in-silico statistical mutagenesis analysis sheds light on the deleteriousness landscape of the human proteome D Raimondi, G Orlando, F Tabaro, T Lenaerts, M Rooman, Y Moreau, ... Scientific reports 8 (1), 16980, 2018 | 11 | 2018 |
| b2bTools: online predictions for protein biophysical features and their conservation LP Kagami, G Orlando, D Raimondi, F Ancien, B Dixit, J Gavaldá-García, ... Nucleic acids research 49 (W1), W52-W59, 2021 | 10 | 2021 |
| An evolutionary view on disulfide bond connectivities prediction using phylogenetic trees and a simple cysteine mutation model D Raimondi, G Orlando, WF Vranken PloS one 10 (7), e0131792, 2015 | 9 | 2015 |
| Auto-encoding NMR chemical shifts from their native vector space to a residue-level biophysical index G Orlando, D Raimondi, W F. Vranken Nature communications 10 (1), 2511, 2019 | 7 | 2019 |
Daniele RaimondiESAT-STADIUS - KU Leuven, Leuven, Belgium在 kuleuven.be 的电子邮件经过验证
