Proteins

Drug repurposing for COVID-19 using machine learning and mechanistic models of signal transduction circuits related to SARS-CoV-2 infection.

Loucera C, Esteban-Medina M, Rian K, Falco MM, Dopazo J, Peña-Chilet M. Drug repurposing for COVID-19 using machine learning and mechanistic models of signal transduction circuits related to SARS-CoV-2 infection. Signal Transduct Target Ther. 2020;5(1):290. doi:10.1038/s41392-020-00417-y.

Community Assessment of the Predictability of Cancer Protein and Phosphoprotein Levels from Genomics and Transcriptomics.

Yang M, Petralia F, Li Z, et al. Community Assessment of the Predictability of Cancer Protein and Phosphoprotein Levels from Genomics and Transcriptomics. Cell Syst. 2020;11(2):186-195.e9. doi:10.1016/j.cels.2020.06.013.

Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models.

Esteban-Medina M, Peña-Chilet M, Loucera C, Dopazo J. Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models. BMC Bioinformatics. 2019;20(1):370. doi:10.1186/s12859-019-2969-0.

Use of estimated evolutionary strength at the codon level improves the prediction of disease-related protein mutations in humans.

Capriotti E, Arbiza L, Casadio R, Dopazo J, Dopazo H, Marti-Renom MA. Use of estimated evolutionary strength at the codon level improves the prediction of disease-related protein mutations in humans. Hum Mutat. 2008;29(1):198-204. doi:10.1002/humu.20628.

Joint annotation of coding and non-coding single nucleotide polymorphisms and mutations in the SNPeffect and PupaSuite databases.

Reumers J, Conde L, Medina I, et al. Joint annotation of coding and non-coding single nucleotide polymorphisms and mutations in the SNPeffect and PupaSuite databases. Nucleic Acids Res. 2008;36(Database issue):D825-9. doi:10.1093/nar/gkm979.

Functional profiling of microarray experiments using text-mining derived bioentities.

Minguez P, Al-Shahrour F, Montaner D, Dopazo J. Functional profiling of microarray experiments using text-mining derived bioentities. Bioinformatics. 2007;23(22):3098-9. doi:10.1093/bioinformatics/btm445.

Functional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes.

Shi W, Bessarabova M, Dosymbekov D, et al. Functional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes. Pharmacogenomics J. 2010;10(4):310-23. doi:10.1038/tpj.2010.35.