Daniele Raimondi
Daniele Raimondi
ESAT-STADIUS - KU Leuven, Leuven, Belgium
Verified email at - Homepage
Cited by
Cited by
Critical assessment of protein intrinsic disorder prediction
M Necci, D Piovesan, SCE Tosatto
Nature methods 18 (5), 472-481, 2021
Improved contact predictions using the recognition of protein like contact patterns
MJ Skwark, D Raimondi, M Michel, A Elofsson
PLoS computational biology 10 (11), e1003889, 2014
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, 2017
COVID-19 in people with multiple sclerosis: a global data sharing initiative
LM Peeters, T Parciak, C Walton, L Geys, Y Moreau, E De Brouwer, ...
Multiple Sclerosis Journal 26 (10), 1157-1162, 2020
Modeling the COVID-19 outbreaks and the effectiveness of the containment measures adopted across countries
E De Brouwer, D Raimondi, Y Moreau
MedRxiv, 2020.04. 02.20046375, 2020
Understanding mutational effects in digenic diseases
A Gazzo, D Raimondi, D Daneels, Y Moreau, G Smits, S Van Dooren, ...
Nucleic acids research 45 (15), e140-e140, 2017
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
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
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
Multilevel biological characterization of exomic variants at the protein level significantly improves the identification of their deleterious effects
D Raimondi, AM Gazzo, M Rooman, T Lenaerts, WF Vranken
Bioinformatics 32 (12), 1797-1804, 2016
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
Early folding events, local interactions, and conservation of protein backbone rigidity
R Pancsa, D Raimondi, E Cilia, WF Vranken
Biophysical journal 110 (3), 572-583, 2016
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
Observation selection bias in contact prediction and its implications for structural bioinformatics
G Orlando, D Raimondi, WF Vranken
Scientific Reports 6 (1), 36679, 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
Current cancer driver variant predictors learn to recognize driver genes instead of functional variants
D Raimondi, A Passemiers, P Fariselli, Y Moreau
BMC biology 19, 1-12, 2021
PyUUL provides an interface between biological structures and deep learning algorithms
G Orlando, D Raimondi, R Duran-Romańa, Y Moreau, J Schymkowitz, ...
Nature communications 13 (1), 961, 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
A novel method for data fusion over entity-relation graphs and its application to protein–protein interaction prediction
D Raimondi, J Simm, A Arany, Y Moreau
Bioinformatics 37 (16), 2275-2281, 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
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