Found: 31
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Biologically meaningful genome interpretation models to address data underdetermination for the leaf and seed ionome prediction in Arabidopsis thaliana.
- Published in:
- Scientific Reports, 2024, v. 14, n. 1, p. 1, doi. 10.1038/s41598-024-63855-6
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- Article
Genome interpretation in a federated learning context allows the multi-center exome-based risk prediction of Crohn's disease patients.
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- Scientific Reports, 2023, v. 13, n. 1, p. 1, doi. 10.1038/s41598-023-46887-2
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- Article
Large sample size and nonlinear sparse models outline epistatic effects in inflammatory bowel disease.
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- Genome Biology, 2023, v. 24, n. 1, p. 1, doi. 10.1186/s13059-023-03064-y
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- Article
Nonlinear data fusion over Entity–Relation graphs for Drug–Target Interaction prediction.
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- Bioinformatics, 2023, v. 39, n. 6, p. 1, doi. 10.1093/bioinformatics/btad348
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- Article
Fast and accurate inference of gene regulatory networks through robust precision matrix estimation.
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- Bioinformatics, 2022, v. 38, n. 10, p. 2802, doi. 10.1093/bioinformatics/btac178
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- Article
From genotype to phenotype in Arabidopsis thaliana: in-silico genome interpretation predicts 288 phenotypes from sequencing data.
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- Nucleic Acids Research, 2022, v. 50, n. 3, p. e16, doi. 10.1093/nar/gkab1099
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- Article
PyUUL provides an interface between biological structures and deep learning algorithms.
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- Nature Communications, 2022, v. 13, n. 1, p. 1, doi. 10.1038/s41467-022-28327-3
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- Article
In silico prediction of in vitro protein liquid–liquid phase separation experiments outcomes with multi-head neural attention.
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- Bioinformatics, 2021, v. 37, n. 20, p. 3473, doi. 10.1093/bioinformatics/btab350
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- Article
A novel method for data fusion over entity-relation graphs and its application to protein–protein interaction prediction.
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- Bioinformatics, 2021, v. 37, n. 16, p. 2275, doi. 10.1093/bioinformatics/btab092
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- Article
b2bTools: online predictions for protein biophysical features and their conservation.
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- Nucleic Acids Research, 2021, v. 49, n. W1, p. W52, doi. 10.1093/nar/gkab425
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- Article
Current cancer driver variant predictors learn to recognize driver genes instead of functional variants.
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- BMC Biology, 2021, v. 19, n. 1, p. 1, doi. 10.1186/s12915-020-00930-0
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- Article
COVID-19 in people with multiple sclerosis: A global data sharing initiative.
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- Multiple Sclerosis Journal, 2020, v. 26, n. 10, p. 1157, doi. 10.1177/1352458520941485
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- Article
ShiftCrypt: a web server to understand and biophysically align proteins through their NMR chemical shift values.
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- Nucleic Acids Research, 2020, v. 48, n. W1, p. W36, doi. 10.1093/nar/gkaa391
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- Article
Insight into the protein solubility driving forces with neural attention.
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- PLoS Computational Biology, 2020, v. 16, n. 4, p. 1, doi. 10.1371/journal.pcbi.1007722
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- Article
Accurate prediction of protein beta-aggregation with generalized statistical potentials.
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- Bioinformatics, 2020, v. 36, n. 7, p. 2076, doi. 10.1093/bioinformatics/btz912
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- Article
Computational identification of prion-like RNA-binding proteins that form liquid phase-separated condensates.
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- Bioinformatics, 2019, v. 35, n. 22, p. 4617, doi. 10.1093/bioinformatics/btz274
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- Article
Exploring the limitations of biophysical propensity scales coupled with machine learning for protein sequence analysis.
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- Scientific Reports, 2019, v. 9, n. 1, p. N.PAG, doi. 10.1038/s41598-019-53324-w
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- Article
Author Correction: Exploring the Sequence-based Prediction of Folding Initiation Sites in Proteins.
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- 2019
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- Correction Notice
Auto-encoding NMR chemical shifts from their native vector space to a residue-level biophysical index.
- Published in:
- Nature Communications, 2019, v. 10, n. 1, p. N.PAG, doi. 10.1038/s41467-019-10322-w
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- Publication type:
- Article
Large-scale in-silico statistical mutagenesis analysis sheds light on the deleteriousness landscape of the human proteome.
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- Scientific Reports, 2018, v. 8, n. 1, p. 1, doi. 10.1038/s41598-018-34959-7
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- Article
Ultra-fast global homology detection with Discrete Cosine Transform and Dynamic Time Warping.
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- Bioinformatics, 2018, v. 34, n. 18, p. 3118, doi. 10.1093/bioinformatics/bty309
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- Article
SVM-dependent pairwise HMM: an application to protein pairwise alignments.
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- Bioinformatics, 2017, v. 33, n. 24, p. 3902, doi. 10.1093/bioinformatics/btx391
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- Article
Understanding mutational effects in digenic diseases.
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- Nucleic Acids Research, 2017, v. 45, n. 15, p. 1, doi. 10.1093/nar/gkx557
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- Publication type:
- Article
Exploring the Sequence-based Prediction of Folding Initiation Sites in Proteins.
- Published in:
- Scientific Reports, 2017, p. N.PAG, doi. 10.1038/s41598-017-08366-3
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- Publication type:
- Article
DEOGEN2: prediction and interactive visualization of single amino acid variant deleteriousness in human proteins.
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- Nucleic Acids Research, 2017, v. 45, n. W1, p. W201, doi. 10.1093/nar/gkx390
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- Publication type:
- Article
Investigating the Molecular Mechanisms Behind Uncharacterized Cysteine Losses from Prediction of Their Oxidation State.
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- Human Mutation, 2017, v. 38, n. 1, p. 86, doi. 10.1002/humu.23129
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- Article
Multilevel biological characterization of exomic variants at the protein level significantly improves the identification of their deleterious effects.
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- Bioinformatics, 2016, v. 32, n. 12, p. 1797, doi. 10.1093/bioinformatics/btw094
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- Article
An Evolutionary View on Disulfide Bond Connectivities Prediction Using Phylogenetic Trees and a Simple Cysteine Mutation Model.
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- PLoS ONE, 2015, v. 10, n. 7, p. 1, doi. 10.1371/journal.pone.0131792
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- Article
Clustering-based model of cysteine co-evolution improves disulfide bond connectivity prediction and reduces homologous sequence requirements.
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- Bioinformatics, 2015, v. 31, n. 8, p. 1219, doi. 10.1093/bioinformatics/btu794
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- Publication type:
- Article
Clustering-based model of cysteine co-evolution improves disulfide bond connectivity prediction and reduces homologous sequence requirements.
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- Bioinformatics, 2015, v. 31, n. 7, p. 1219, doi. 10.1093/bioinformatics/btu794
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- Publication type:
- Article
Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns.
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- PLoS Computational Biology, 2014, v. 10, n. 11, p. 1, doi. 10.1371/journal.pcbi.1003889
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- Article