D. Raimondi

发表

Large-scale in-silico statistical mutagenesis analysis sheds light on the deleteriousness landscape of the human proteome

Y. Moreau, M. Rooman, T. Lenaerts, 2018, Scientific Reports.

Current cancer driver variant predictors learn to recognize driver genes instead of functional variants

Y. Moreau, P. Fariselli, D. Raimondi, 2021, BMC Biology.

Prediction of disordered regions in proteins with recurrent Neural Networks and protein dynamics.

W. Vranken, Francesco Tabaro, D. Raimondi, 2022, Journal of molecular biology.

Investigating the Molecular Mechanisms Behind Uncharacterized Cysteine Losses from Prediction of Their Oxidation State

Daniele Raimondi, Joris Messens, Wim F Vranken, 2017, Human mutation.

An interpretable low-complexity machine learning framework for robust exome-based in-silico diagnosis of Crohn’s disease patients

Y. Moreau, P. Fariselli, I. Cleynen, 2020, NAR genomics and bioinformatics.

Understanding mutational effects in digenic diseases

Tom Lenaerts, Yves Moreau, Andrea Gazzo, 2017, Nucleic acids research.

Insight into the protein solubility driving forces with neural attention

Piero Fariselli, Yves Moreau, Daniele Raimondi, 2020, PLoS Comput. Biol..

HPMPdb: A machine learning-ready database of protein molecular phenotypes associated to human missense variants

Y. Moreau, J. Schymkowitz, F. Rousseau, 2022, Current research in structural biology.

Fast and accurate inference of gene regulatory networks through robust precision matrix estimation

Y. Moreau, D. Raimondi, Antoine Passemiers, 2022, Bioinformatics.

Prediction of disordered regions in proteins with recurrent Neural Networks and protein dynamics

W. Vranken, Francesco Tabaro, D. Raimondi, 2020, bioRxiv.

Auto-encoding NMR chemical shifts from their native vector space to a residue-level biophysical index

D. Raimondi, G. Orlando, Wim F. Vranken, 2019, Nature Communications.

First record of Turkish Gecko Hemidactylus turcicus (Linnaeus, 1758) populations for Piedmont, Italy (Sauria Gekkonidae)

D. Raimondi, M. D. Di Nicola, Roberto Ghiglia, 2020 .

DEOGEN2: prediction and interactive visualization of single amino acid variant deleteriousness in human proteins

Tom Lenaerts, Julien Ferté, Andrea Gazzo, 2017, Nucleic Acids Res..

Multilevel biological characterization of exomic variants at the protein level significantly improves the identification of their deleterious effects

Tom Lenaerts, Daniele Raimondi, Wim F. Vranken, 2016, Bioinform..

On-Board Unit Big Data: Short-term Traffic Forecasting in Urban Transportation Networks

Daniele Raimondi, Yann-Aël Le Borgne, Gianluca Bontempi, 2020, 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA).

Ultra-fast global homology detection with Discrete Cosine Transform and Dynamic Time Warping

Yves Moreau, Daniele Raimondi, Gabriele Orlando, 2018, Bioinform..

Large-scale in-silico statistical mutagenesis analysis sheds light on the deleteriousness landscape of the human proteome

Y. Moreau, M. Rooman, T. Lenaerts, 2018, Scientific Reports.

Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns

Marcin J. Skwark, Mirco Michel, Arne Elofsson, 2014, PLoS Comput. Biol..

Editorial: Towards genome interpretation: Computational methods to model the genotype-phenotype relationship

Y. Moreau, P. Fariselli, D. Raimondi, 2022, Frontiers in Bioinformatics.

Critical assessment of protein intrinsic disorder prediction

M. Y. Lobanov, David C. Jones, K. Paliwal, 2021, Nature Methods.

In silico prediction of in vitro protein liquid-liquid phase separation experiments outcomes with multi-head neural attention

Yves Moreau, Frederic Rousseau, Daniele Raimondi, 2021, Bioinform..

Accurate prediction of protein beta-aggregation with generalized statistical potentials

Alexandra Silva, Daniele Raimondi, Gabriele Orlando, 2019, Bioinform..

Early Folding Events, Local Interactions, and Conservation of Protein Backbone Rigidity.

Daniele Raimondi, Wim F Vranken, Elisa Cilia, 2016, Biophysical journal.

Computational identification of prion-like RNA-binding proteins that form liquid phase-separated condensates

Yves Moreau, Daniele Raimondi, Gabriele Orlando, 2019, Bioinform..

A novel method for data fusion over entity-relation graphs and its application to protein-protein interaction prediction

Yves Moreau, Daniele Raimondi, Jaak Simm, 2021, Bioinform..

Exploring the Sequence-based Prediction of Folding Initiation Sites in Proteins

W. Vranken, R. Pancsa, D. Raimondi, 2017, Scientific Reports.

From genotype to phenotype in Arabidopsis thaliana: in-silico genome interpretation predicts 288 phenotypes from sequencing data

Y. Moreau, P. Fariselli, Massimiliano Corso, 2021, Nucleic acids research.

COVID-19 in people with multiple sclerosis: A global data sharing initiative

Yves Moreau, Richard Nicholas, Amber Salter, 2020, Multiple sclerosis.

Exploring the limitations of biophysical propensity scales coupled with machine learning for protein sequence analysis

Yves Moreau, Daniele Raimondi, Gabriele Orlando, 2019, Scientific Reports.

Can herd immunity be achieved without breaking ICUs?

D. Raimondi, Y. Moreau, E. De Brouwer, 2020, medRxiv.

SVM‐dependent pairwise HMM: an application to protein pairwise alignments

Tom Lenaerts, Daniele Raimondi, Wim F Vranken, 2017, Bioinform..

PyUUL provides an interface between biological structures and deep learning algorithms

Y. Moreau, F. Rousseau, Ramon Duran-Romaña, 2022, Nature communications.

Observation selection bias in contact prediction and its implications for structural bioinformatics

G. Orlando, D. Raimondi, W. F. Vranken, 2016, Scientific Reports.

Nonlinear data fusion over Entity–Relation graphs for Drug–Target Interaction prediction

Y. Moreau, P. Fariselli, D. Raimondi, 2023, Bioinformatics.

How good Neural Networks interpretation methods really are? A quantitative benchmark

P. Fariselli, G. Birolo, D. Raimondi, 2023, ArXiv.

Critical assessment of protein intrinsic disorder prediction

Silvio C. E. Tosatto, Marco Necci, Damiano Piovesan, 2020, Nature Methods.

Exploring the Sequence-based Prediction of Folding Initiation Sites in Proteins

Daniele Raimondi, Wim F Vranken, Gabriele Orlando, 2017, Scientific Reports.

Large sample size and nonlinear sparse models outline epistatic effects in inflammatory bowel disease

D. Raimondi, Antoine Passemiers, N. Verplaetse, 2023, Genome Biology.

b2bTools: online predictions for protein biophysical features and their conservation

W. Vranken, D. Raimondi, L. Kagami, 2021, Nucleic Acids Res..

ShiftCrypt: a web server to understand and biophysically align proteins through their NMR chemical shift values

W. Vranken, D. Raimondi, L. Kagami, 2020, Nucleic Acids Res..

Data-driven probabilistic definition of the low energy conformational states of protein residues

D. Raimondi, Joel Roca-Martínez, Jose Gavaldá-García, 2023, bioRxiv.

Genome interpretation in a federated learning context allows the multi-center exome-based risk prediction of Crohn’s disease patients

D. Raimondi, Haleh Chizari, B. Löscher, 2023, Scientific reports.

Clustering-based model of cysteine co-evolution improves disulfide bond connectivity prediction and reduces homologous sequence requirements

Daniele Raimondi, Gabriele Orlando, Wim F. Vranken, 2015, Bioinform..