Daniele Raimondi
Cited by
Cited by
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
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
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
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
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), 1-11, 2017
Modeling the COVID-19 outbreaks and the effectiveness of the containment measures adopted across countries
E De Brouwer, D Raimondi, Y Moreau
medRxiv, 2020
Computational identification of prion-like RNA-binding proteins that form liquid phase-separated condensates
G Orlando, D Raimondi, F Tabaro, F Codicč, Y Moreau, WF Vranken
Bioinformatics 35 (22), 4617-4623, 2019
Observation selection bias in contact prediction and its implications for structural bioinformatics
G Orlando, D Raimondi, WF Vranken
Scientific reports 6 (1), 1-8, 2016
Critical assessment of protein intrinsic disorder prediction
M Necci, D Piovesan, SCE Tosatto
Nature methods 18 (5), 472-481, 2021
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
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
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
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
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), 1-11, 2019
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
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), 1-11, 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
Prediction of disordered regions in proteins with recurrent neural networks and protein dynamics
G Orlando, D Raimondi, F Codice, F Tabaro, W Vranken
bioRxiv, 2020
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Articles 1–20