Publications

2021

  • Barata, C., Rodrigues, A. M., Canhao, H., Vinga, S., & Carvalho, A. M. (2021). Predicting Biologic Therapy Outcome of Patients With Spondyloarthritis: Joint Models for Longitudinal and Survival Analysis. Jmir Medical Informatics, 9(7). doi: 10.2196/26823
  • Borderes, M., Gasc, C., Prestat, E., Ferrarini, M. G., Vinga, S., Boucinha, L., & Sagot, M. F. (2021). A comprehensive evaluation of binning methods to recover human gut microbial species from a non-redundant reference gene catalog. Nar Genomics and Bioinformatics, 3(1). doi: 10.1093/nargab/lqab009
  • Castro, M. J., Mendes, J. J., Vinga, S., & de Andrade, D. C. (2021). Differential diagnosis of developmental defects of enamel: a review. Annals of Medicine, 53, S37-S38. doi:10.1080/07853890.2021.1897305
  • Constantino, C. S., Carvalho, A. M., & Vinga, S. (2021). Coupling sparse Cox models with clustering of longitudinal transcriptomics data for trauma prognosis. Biodata Mining, 14(1). doi: 10.1186/s13040-021-00257-8
  • Cruz, R. C., Costa, P. R., Vinga, S., Krippahl, L., & Lopes, M. B. (2021). A Review of Recent Machine Learning Advances for Forecasting Harmful Algal Blooms and Shellfish Contamination. Journal of Marine Science and Engineering, 9(3). doi: 10.3390/jmse9030283
  • Gomes, S. C., Vinga, S., & Henriques, R. (2021). Spatiotemporal Correlation Feature Spaces to Support Anomaly Detection in Water Distribution Networks. Water, 13(18). doi: 10.3390/w13182551
  • Lopes, M. B., Martins, E. P., Vinga, S., & Costa, B. M. (2021). The Role of Network Science in Glioblastoma. Cancers, 13(5). doi: 10.3390/cancers13051045
  • Neto, J. P., Alho, I., Costa, L., Casimiro, S., Valerio, D., & Vinga, S. (2021). Dynamic modeling of bone remodeling, osteolytic metastasis and PK/PD therapy: introducing variable order derivatives as a simplification technique. Journal of Mathematical Biology, 83(4). doi: 10.1007/s00285-021-01666-3
  • Serras, J. L., Vinga, S., & Carvalho, A. M. (2021). Outlier Detection for Multivariate Time Series Using Dynamic Bayesian Networks. Applied Sciences-Basel, 11(4). doi: 10.3390/app11041955
  • Vinga, S. (2021). Structured sparsity regularization for analyzing high-dimensional omics data. Briefings in Bioinformatics, 22(1), 77-87. doi:10.1093/bib/bbaa122

2020

  • Andrade, R., Doostmohammadi, M., Santos, J. L., Sagot, M. F., Mira, N. P., & Vinga, S. (2020). MOMO – multi-objective metabolic mixed integer optimization: application to yeast strain engineering. Bmc Bioinformatics, 21(1). doi:10.1186/s12859-020-3377-1
  • Ferreira, P. F., Carvalho, A. M., & Vinga, S. (2020). Variational Inference in Probabilistic Single-cell RNA-seq Models. Computational Intelligence Methods for Bioinformatics and Biostatistics, Cibb 2018, 11925, 11-18. doi:10.1007/978-3-030-34585-3_2
  • Lopes, M. B., & Vinga, S. (2020). Tracking intratumoral heterogeneity in glioblastoma via regularized classification of single-cell RNA-Seq data. Bmc Bioinformatics, 21(1). doi: 10.1186/s12859-020-3390-4
  • Miranda, R., Vinga, S., & Valerio, D. (2020). Studying Bone Remodelling and Tumour Growth for Therapy Predictive Control. Mathematics, 8(5). doi: 10.3390/math8050679
  • Peixoto, C., Lopes, M. B., Martins, M., Costa, L., & Vinga, S. (2020). TCox: Correlation-Based Regularization Applied to Colorectal Cancer Survival Data. Biomedicines, 8(11). doi: 10.3390/biomedicines8110488
  • Villa-Brito, J., Lopes, M. B., Carvalho, A. M., & Vinga, S. (2020). Unravelling Breast and Prostate Common Gene Signatures by Bayesian Network Learning. Computational Intelligence Methods for Bioinformatics and Biostatistics, Cibb 2018, 11925, 285-292. doi:10.1007/978-3-030-34585-3_25

2019

  • Lopes, M. B., Casimiro, S., & Vinga, S. (2019). Twiner: correlation-based regularization for identifying common cancer gene signatures. Bmc Bioinformatics, 20. doi: 10.1186/s12859-019-2937-8
  • Lopes, M. B., Verissimo, A., Carrasquinha, E., & Vinga, S. (2019). On the Role of Hub and Orphan Genes in the Diagnosis of Breast Invasive Carcinoma. Machine Learning, Optimization, and Data Science, 11943, 631-642. doi:10.1007/978-3-030-37599-7_52
  • Loureiro, H., Carrasquinha, E., Alho, I., Ferreira, A. R., Costa, L., Carvalho, A. M., & Vinga, S. (2019). Modelling cancer outcomes of bone metastatic patients: combining survival data with N-Telopeptide of type I collagen (NTX) dynamics through joint models. Bmc Medical Informatics and Decision Making, 19. doi: 10.1186/s12911-018-0728-1
  • Peyroteo, M. M. A., Belinha, J., Vinga, S., Dinis, L. M. J. S., & Jorge, R. M. N. (2019). Mechanical bone remodelling: Comparative study of distinct numerical approaches. Engineering Analysis with Boundary Elements, 100, 125-139. doi:10.1016/j.enganabound.2018.01.011
  • Rama, K., Canhao, H., Carvalho, A. M., & Vinga, S. (2019). AliClu – Temporal sequence alignment for clustering longitudinal clinical data. Bmc Medical Informatics and Decision Making, 19(1). doi: 10.1186/s12911-019-1013-7
  • Segaert, P., Lopes, M. B., Casimiro, S., Vinga, S., & Rousseeuw, P. J. (2019). Robust identification of target genes and outliers in triple-negative breast cancer data. Statistical Methods in Medical Research, 28(10-11), 3042-3056. doi:10.1177/0962280218794722
  • Valerio, D., Neto, J., & Vinga, S. (2019). variable order 3D models of bone remodelling. Bulletin of the Polish Academy of Sciences-Technical Sciences, 67(3), 501-508. doi:10.24425/bpasts.2019.129649
  • Vinga, S. (2019). Bridging computer science and bioengineering for the multiscale modeling of biological systems. Annals of Medicine, 51, S28-S29. doi:10.1080/07853890.2018.1560067
  • Zielezinski, A., Girgis, H. Z., Bernard, G., Leimeister, C. A., Tang, K. J., Dencker, T., . . . Karlowski, W. M. (2019). Benchmarking of alignment-free sequence comparison methods. Genome Biology, 20. doi: 10.1186/s13059-019-1755-7

2018

  • Lopes, M. B., Verissimo, A., Carrasquinha, E., Casimiro, S., Beerenwinkel, N., & Vinga, S. (2018). Ensemble outlier detection and gene selection in triple-negative breast cancer data. BMC Bioinformatics, 19. doi: 10.1186/s12859-018-2149-7
  • Carrasquinha, E., Verissimo, A., Lopes, M. B., & Vinga, S. (2018). Identification of influential observations in high-dimensional cancer survival data through the rank product test. Biodata Mining, 11. doi: 10.1186/s13040-018-0162-z
  • Costa, R.S. & Vinga S. (2018) Assessing Escherichia coli metabolism models and simulation approaches in phenotype predictions: validation against experimental data. Biotechnology Progress.
  • Segaert, P., Lopes, M. B., Casimiro, S., Vinga, S., & Rousseeuw, P. J. (2018). Robust identification of target genes and outliers in triple-negative breast cancer data. Stat Methods Med Res, 962280218794722. doi: 10.1177/0962280218794722
  • Miranda, R., Vinga S., & Valério, D. (2018) Studying Bone Remodelling and Tumour Growth for Therapy Predictive Control. Computers and Mathematics with Applications.
  • Neto, J. P., Coelho, R. M., Valério, D., Vinga, S., Sierociuk, D., Malesza, W., Macias, M., & Dzielinski, A. (2018). Simplifying biochemical tumorous bone remodeling models through variable order derivatives. Computers & Mathematics with Applications, 75(9), 3147-3157. doi: 10.1016/j.camwa.2018.01.037
  • Freitas, P. G., Elias, T. C., Pinto, I. A., Costa, L. T., Carvalho, P. V. S. D. D., Omote, D. D. Q.,,…,Silveira, N. J. F. (2018). Computational Approach to the Discovery of Phytochemical Molecules with Therapeutic Potential Targets to the PKCZ protein. Letters in Drug Design & Discovery, 15(5), 488-499. doi: 10.2174/1570180814666170810120150
  • Dudnik, A., Almeida, A. F., Andrade, R., Avila, B., Banados, P., Barbay, D., . . . Forster, J. (2018). BacHBerry: BACterial Hosts for production of Bioactive phenolics from bERRY fruits. Phytochemistry Reviews, 17(2), 291-326. doi: 10.1007/s11101-017-9532-2
  • Carvalho, S. M., Kloosterman, T. G., Manzoor, I., Caldas, J., Vinga, S., Martinussen, J., Saraiva, L. M. Kuipers, O. P., & Neves, A. P. (2018). Interplay Between Capsule Expression and Uracil Metabolism in Streptococcus pneumoniae D39. Frontiers in Microbiology, 9. doi: 10.3389/fmicb.2018.00321

2017

  • Zielezinski, A., Vinga, S., Almeida, J., & Karlowski, W. M. (2017). Alignment-free sequence comparison: benefits, applications, and tools. Genome Biology, 18. doi: 10.1186/s13059-017-1319-7
  • Tomas, E., Vinga, S., & Carvalho, A. M. (2017). Unsupervised learning of pharmacokinetic responses. Computational Statistics, 32(2), 409-428. doi: 10.1007/s00180-016-0707-x
  • Neto, J. P., Coelho, R. M., Valerio, D., Vinga, S., Sierociuk, D., Malesza, W., Macias, M., & Dzielinski, A. (2017). Variable Order Differential Models of Bone Remodelling. Ifac Papersonline, 50(1), 8066-8071. doi: 10.1016/j.ifacol.2017.08.1233
  • Hartmann, A., Vila-Santa, A., Kallscheuer, N., Vogt, M., Julien-Laferriere, A., Sagot, M. F., . . . Vinga, S. (2017). OptPipe – a pipeline for optimizing metabolic engineering targets. BMC Systems Biology, 11. doi: 10.1186/s12918-017-0515-0

2016

  • Veríssimo A., Oliveira A. L., Sagot, M.-F., & Vinga, S. (2016) DegreeCox: a network-based regularization method for survival analysis. BMC Bioinformatics, 17(16), 109-121. DOI: 10.1186/s12859-016-1310-4
  • Hartmann, A., Neves, A. R., Lemos, J. M., & Vinga, S. (2016). Identification and automatic segmentation of multiphasic cell growth using a linear hybrid model. Mathematical Biosciences, 279, 83-89. DOI: 10.1016/j.mbs.2016.06.013
  • Lemos, J. M., Caiado, D. V., Coelho, R., & Vinga, S. (2016). Optimal and receding horizon control of tumor growth in myeloma bone disease. Biomedical Signal Processing and Control, 24, 128-134. DOI: 10.1016/j.bspc.2015.10.004
  • Julien-Laferriere, A., Bulteau, L., Parrot, D., Marchetti-Spaccamela, A., Stougie, L., Vinga, S., Mary, A. & Sagot, M. F. (2016). A Combinatorial Algorithm for Microbial Consortia Synthetic Design. Scientific Reports, 6, 12. DOI: 10.1038/srep29182
  • Coelho RM, Lemos JM, Alho I, Valério D, Ferreira AR, Costa L, Vinga S (2016) Dynamic modeling of bone metastasis, microenvironment and therapy Integrating parathyroid hormone (PTH) effect, anti-resorptive and anti-cancer therapy. Journal of Theoretical Biology. Volume 391, 21 February 2016, Pages 1–12. DOI:10.1016/j.jtbi.2015.11.024
  • Fernandes, A., & Vinga, S. (2016). Improving Protein Expression Prediction Using Extra Features and Ensemble Averaging. PLoS One, 11(3), 15. DOI: 10.1371/journal.pone.0150369
  • Costa, R. S., & Vinga, S. (2016). Control analysis of the impact of allosteric regulation mechanism in a Escherichia coli kinetic model: Application to serine production. Biochemical Engineering Journal, 110, 59-70. DOI: 10.1016/j.bej.2016.01.013
  • Costa, R. S., Hartmann, A., & Vinga, S. (2016). Kinetic modeling of cell metabolism for microbial production. Journal of Biotechnology, 219, 126-141. DOI: 10.1016/j.jbiotec.2015.12.023

2015

  • Paixão, L., Caldas, J., Kloosterman, T. G., Kuipers, O. P., Vinga, S., & Neves, A. R. (2015). Transcriptional and metabolic effects of glucose on Streptococcus pneumoniae sugar metabolism. Frontiers in Microbiology, 6. DOI: 10.3389/fmicb.2015.01041
  • Hartmann, A., Lemos, J.M., Costa, R.S., Xavier, J. and Vinga, S. (2015) Identification of Switched ARX Models via Convex Optimization and Expectation Maximization. Journal of Process Control. Volume 28, April 2015, Pages 9-16. DOI: 10.1016/j.jprocont.2015.02.003
  • Paixão, L., Oliveira, J., Veríssimo, A., Vinga, S., Lourenço, E.C., Ventura, M.R., Kjos, M., Veening, J-W., Fernandes, V.E., Andrew, P.W., Yesilkaya, H. and Neves, A.R. (2015) Host glycan sugar-specific pathways in Streptococcus pneumoniae: galactose as a key sugar in colonisation and infection. PLoS One. 2015; 10(3): e0121042. Published online 2015 Mar 31. DOI:  10.1371/journal.pone.0121042
  • Hartmann, A., Lemos, J.M., Vinga, S. (2015) Modeling multiple experiments using regularized optimization: a case study on bacterial glucose utilization dynamics. Computers in Biology and Medicine, 63, 301-309. DOI:10.1016/j.compbiomed.2014.08.027.

2014

2013