BOOK CHAPTERS

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1. Pessanha, S.;  Silva, J.; Lima, R.R.; Lopes, M.B. (2025). Raman spectroscopic studies on mineralized tissues and skin appendages. In: Singh V.K., Applied Raman Spectroscopy, Concepts, Instrumentation, Chemometrics, and Life Science Applications, Chapter 10, Elsevier, DOI:10.1016/B978-0-443-21834-7.00010-4

2. Lopes, M.B.; Vinga, S. (2021). Learning Biomedical Networks: Toward Data-Informed Clinical Decision and Therapy. In: Pham T.D., Yan H., Ashraf M.W., Sjoberg F. (eds) Advances in Artificial Intelligence, Computation, and Data Science. Computational Biology. Springer, Cham, DOI:10.1007/978-3-030-69951-2_3

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ARTICLES IN INTERNATIONAL PEER-REVIEWED JOURNALS

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1. Coletti, R.; Cerdeira, J.O.; Raydan, M.; Lopes, M.B. (2025).  An unsupervised tool for biomarker discovery and cancer subtyping applied to glioblastoma. BioData Mining 18: 85, DOI:10.1186/s13040-025-00500-6

2. Mendonça, M.L.; Coletti, R.; Martins, E.P.; Gonçalves, C.S.; Costa, B.M.; Vinga, S.; Lopes, M.B. (2025). Updating TCGA glioma classification through integration of molecular data following the latest WHO guidelines. Scientific Data, 12: 935, DOI:10.1038/s41597-025-05117-2

3. Pessanha, S.; Fortes, A.; Lopes, M.B.; Buzanich, A.G.; Ortega-Feliu, I.;  Respaldiza, M.A.; Tubio, B.G.; Makarova, A.; Smirnov, D.; Kumar, S.; Mata, A.; Silveira, J. (2025). Multi-technique computational assessment of fluoride uptake in enamel using PIGE, NEXAFS, and Raman spectroscopy. Journal of Materials Chemistry B, 13: 6366, DOI:10.1039/D5TB00213C

4. Coletti, R.; Carrilho, J.F.; Martins, E.P.; Gonçalves, C.S.; Costa, B.M.; Lopes, M.B. (2025). A novel tool for multi-omics network integration and visualization: A study of glioma heterogeneity. Computers in Biology and Medicine, 188:109811, DOI:10.1016/j.compbiomed.2025.109811

5. Lopes, M.B.; Coletti, R.; Duranton, F.; Glorieux, G.; Jaimes Campos, M.A.; Klein, J.; Ley, M.; Perco, P.; Sampri, A.; Tur-Sinai, A. (2025). The omics-driven machine learning path to cost-effective precision medicine in chronic kidney disease. Proteomics, 0: e202400108, DOI:10.1002/pmic.202400108

6. Kastendiek, N.; Coletti, R.; Gross, T.; Lopes M.B. (2024). Exploring glioma heterogeneity through omics networks: from gene network discovery to causal insights and patient stratification. BioData Mining, 17: 56, DOI:10.1186/s13040-024-00411-y

7. Musib, L.; Coletti, R.; Lopes, M.B.; Mouriño, H.; Carrasquinha, E. (2024). Priority-Elastic net for binary outcome prediction based on multi-omics data. BioData Mining, 17:45, DOI:10.1186/s13040-024-00401-0

8. Coletti, R.; Leiria de Mendonça, M.; Vinga, S.; Lopes, M.B. (2024). Inferring diagnostic and prognostic gene expression signatures across WHO glioma classifications: A network-based approach. Bioinformatics and Biology Insights, 18, DOI:10.1177/11779322241271535

9. Porreca, A.; Ibrahimi, E.; Maturo, F.; Marcos Zambrano, L.J.; Meto, M.; Lopes, M.B. (2024). Robust prediction of colorectal cancer via gut microbiome 16S rRNA sequencing data. Journal of Medical Microbiology, 73: 001903, DOI:10.1099/jmm.0.001903

10. Vieira, F.G.; Bispo, R.; Lopes, M.B. (2024). Integration of multi-omics data for the classification of glioma types and identification of novel biomarkers. Bioinformatics and Biology Insights, 18, DOI:10.1177/ 11779322241249563

11. Martins, S.; Coletti, R.; Lopes, M.B. (2023). Disclosing transcriptomics network-based signatures of glioma heterogeneity using sparse methods. BioData Mining, 16: 26, 2023. DOI:10.1186/s13040-023-00341-1

12. Marcos-Zambrano, L.J.; López-Molina, V.M.; Bakir-Gungor, B.; Frohme, M.; Karaduzovic-Hadziabdic, K.; Klammsteiner, T.; Ibrahimi, E.; Lahti, L.; Turukalo, T.L.; Dhamo, X.; Simeon, A.; Nechyporenko, A.; Pio, G.; Piotr Przymus, P.; Sampri, A.; Trajkovik, V.; Lacruz-Pleguezuelos, B.; Aasmets, O.; Ricardo Araujo, R.; Anagnostopoulos, I.; Aydemir, O.; Berland, M.; Calle, M.J.; Ceci, M.; Duman, H.; Güdogdu, A.; Havulinna, A.S.; Bra, K.H.N.K.; Kalluci, E.; Karav, S.; Lode, D.; Lopes M.B. et al. (2023). A toolbox of machine learning software of support microbiome analysis. Frontiers in Microbiology, 14: 1250806, DOI:10.3389/fmicb.2023.1250806

13. Ibrahimi, E.; Lopes, M.B.; Dhamo, X.; Simeon, A.; Shigdel, R.; Hron, K.; Stres, B.; D'Elia, D.; Berland, M.; Marcos-Zambrano, L.J. (2023). Overview of data preprocessing for machine learning applications in human microbiome research. Frontiers inMicrobiology, 14: 1250909, DOI:10.3389/fmicb.2023.1250909

14. D'Elia, D.; Truu, J.; Lahti, L.; Berland, M.; Papoutsoglou, G.; Ceci, M.; Zomer, A.; Lopes, M.B.; Ibrahimi, E. et al. (2023). Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action. Frontiers in Microbiology, 14: 1257002, DOI:10.3389/fmicb.2023.1257002

15. Papoutsoglou, G.; Tarazona, S.; Lopes, M.B.; Klammsteiner, T.; Ibrahimi, E.; Eckenberger, J.; Novielli, P. et al. (2023). Machine learning approaches in microbiome research: challenges and best practices. Frontiers in Microbiology,14:1261889, DOI:10.3389/fmicb.2023.1261889

16. Peixoto, C.; Lopes, M.B., Martins, M., Casimiro, S.; Sobral, D. et al. (2023). Identification of biomarkers predictive of metastasis development in early-stage colorectal cancer using network-based regularization. BMC Bioinformatics, 24(1): 17, DOI:10.1186/s12859-022-05104-z

17. Carrilho, J.; Lopes, M.B (2022). Classification and biomarker selection in lower-grade glioma using robust sparse logistic regression applied to RNA-seq data. Brazilian Journal of Biometrics, 40: 371:381, DOI:10.28951/bjb.v40i4.634

18. Cruz, R.C.; Costa, P.R.; Krippahl, L.; Lopes, M.B (2022). Forecasting biotoxin contamination in mussels across production areas of the Portuguese coast with Artificial Neural Networks. Knowledge-Based Systems, 257: 109895, DOI:10.1016/j.knosys.2022.109895

19. Patrício A.; Lopes, M.B.; Costa, P.R.; Costa, R.S.; Henriques, R.; Vinga, S. (2022). Time-Lagged Correlation Analysis of Shellfish Toxicity Reveals Predictive Links to Adjacent Areas, Species, and Environmental Conditions. Toxins, 14(10): 679, DOI:10.3390/toxins14100679

20. Jensch, A.; Lopes, M.B.; Vinga, S.; Radde N. (2022). ROSIE: RObust Sparse ensemble for outlIEr detection and gene selection in cancer omics data. Statistical Methods in Medical Research, 1-12, DOI:10.1177/09622802211072456

21. Lopes, M.B.; Martins, E.P.; Vinga, S.; Costa, B.M. (2021). The Role of Network Science in Glioblastoma. Cancers, 13(5): 1045, DOI:10.3390/cancers13051045

22. 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 (2021): 283, DOI:10.3390/jmse9030283

23. Marcos-Zambrano, L.J.; Karaduzovic-Hadziabdic, K.; Loncar Turukalo, T.; Przymus, P.; Trajkovik, V.; et al. (2021). Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment. Frontiers in Microbiology 12: 634511, DOI:10.3389/fmicb.2021.634511

24. Moreno-Indias, I.; Lahti, L.; Nedyalkova, M.; Elbere, I.; Roshchupkin, G.; Adilovic, M.; Aydemir, O.; et al. (2021). Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions. Frontiers in Microbiology, 12: 635781, DOI:10.3389/fmicb.2021.635781

25. 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): 488, DOI:10.3390/biomedicines8110488

26. Lopes, M.B., Vinga, S. (2020) Tracking intratumoral heterogeneity in glioblastoma via regularized classification of single-cell RNA-Seq data. BMC Bioinformatics, 21: 50, DOI:10.1186/s12859-020-3390-4

27. Lopes, M.B.; Casimiro, S.; Vinga, S. (2019). Twiner: correlation-based regularization for identifying common cancer gene signatures. BMC Bioinformatics, 20: 356, DOI:10.1186/s12858-019-2937-8

28. Segaert, P.; Lopes, M.B.; Vinga, S.; Rousseeuw, P.J. (2018). Robust identification of target genes and outliers in triple-negative breast cancer data. Statistical methods in medical research, 28(10-11): 3042-56, DOI:10.1177/0962280218794722

29. Lopes, M.B.; Veríssimo, A.; Carrasquinha, E.; Casimiro, S.; Beerenwinkel N.; Vinga, S. (2018). Ensemble outlier detection and gene selection in triple-negative breast cancer data. BMC bioinformatics, 9: 168, DOI:10.1186/s12859-018-2149-7

30. Lopes, M.B.; Amorim, A.; Calado, C.R.C.; Costa, P.R. (2018). Determination of cell abundances and paralytic shellfish toxins in cultures of the dinoflagellate Gymnodinium catenatum by Fourier Transform Near Infrared spectroscopy. Journal of Marine Science and Engineering, 6 (4): 147-147, DOI:10.3390/jmse6040147

31. Lopes, M.B.; Calado, C.R.C. (2018). Assessing plasmid bioprocess reproducibility and C-source uptake stage through multivariate analysis of offline and online data. Journal of Chemical Technology and Biotechnology, 93 (10): 3056-3066, DOI:10.1002/jctb.5666

32. Carrasquinha, E.; Veríssimo, 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: 1, DOI:10.1186/s13040-018-0162-z

33. Sampaio, P.N.; Sales, K.C.; Rosa, F.O.; Lopes, M.B.; Calado, C.R.C. (2017). High-throughput FTIR-based bioprocess analysis of recombinant cyprosin production. Journal of Industrial Microbiology and Biotechnology, 44 (1): 49-61, DOI:10.1007/s10295-016-1865-0

34. Sales, K.C.; Rosa, F.; Cunha, B.R.; Sampaio, P.N.; Lopes, M.B.; Calado, C.R.C. (2017). Metabolic profiling of recombinant Escherichia coli cultivations based on high-throughput FT-MIR spectroscopic analysis. Biotechnology Progress, 33 (2): 285-298, DOI:10.1002/btpr.2378

35. Lopes, M.B.; Calado, C.R.C.; Figueiredo, M.A.T.; Bioucas-Dias, J.M. (2016). Does nonlinear modeling play a role in plasmid bioprocess monitoring using Fourier Transform Infrared spectra? Applied Spectroscopy, 71(6): 1148-1156, DOI:10.1177/0003702816670913

36. Rosa, F.; Sales, K.C.; Carmelo, J.G.; Fernandes-Platzgummer, A.; da Silva, C.L.; Lopes, M.B.; Calado, C.R.C. (2016). Monitoring the ex-vivo expansion of human mesenchymal stem/stromal cells in xeno-free microcarrier-based reactor systems by MIR spectroscopy. Biotechnology Progress, 32 (2): 447-455, DOI:10.1002/btpr.2215

37. Lopes, M.B.; Gonçalves, G.A.L.; Felício-Silva, D.; Prather, K.L.J.; Monteiro, G.A.; Prazeres, D.M.F.; Calado, C.R.C. (2015). In situ NIR spectroscopy monitoring of plasmid production processes: Effect of producing strain, medium composition and the cultivation strategy. Journal of Chemical Technology and Biotechnology, 90 (2): 255-261, DOI:10.1002/jctb.4431

38. Rosa, F.; Sales, K.C.; Cunha, B.R.; Couto, A.; Lopes, M.B.; Calado, C.R.C. (2015). A comprehensive high-throughput FTIR spectroscopy-based method for evaluating the transfection event: estimating the transfection efficiency and extracting associated metabolic responses. Analytical and bioanalytical chemistry, 407 (26): 8097-8108, DOI:10.1007/s00216-015-8983-9

39. Sales, K.C.; Rosa, F.; Sampaio, P.N.; Fonseca, L.P.; Lopes, M.B.; Calado, C.R.C. (2015). In situ near-infrared (NIR) versus high-throughput mid-infrared (MIR) spectroscopy to monitor biopharmaceutical production. Applied spectroscopy, 69 (6): 760-772, DOI:10.1366/14-07588

40. Lopes, M.B.; Martins, G.; Calado, C.R.C. (2014). Kinetic modeling of plasmid bioproduction in Escherichia coli DH5a cultures over different carbon-source compositions. Journal of Biotechnology, 186: 38-48, DOI:10.1016/j.jbiotec.2014.06.022

41. Sampaio, P.N.; Sales, K.C.; Rosa, F.O.; Lopes, M.B.; Calado, C.R.C. (2014). In situ near infrared spectroscopy monitoring of cyprosin production by recombinant Saccharomyces cerevisiae strains. Journal of Biotechnology, 188: 148-157, DOI:10.1016/j.jbiotec.2014.07.454

42. Lopes, M.B.; Wolff, J.-C.; Bioucas-Dias, J.M.; Figueiredo, M.A.T. (2011). Study on the effect of pixel resolution and blending grade on near-infrared hyperspectral unmixing of tablets. Applied Spectroscopy, 65 (2): 193-200, DOI:10.1366/10-06079

43. Lopes, M.B.; Wolff, J.-C.; Bioucas-Dias, J.M.; Figueiredo, M.A.T. (2010). Quantification of components in non-homogenous pharmaceutical tablets using near infrared reflectance imaging. Journal of Near Infrared Spectroscopy, 18 (5): 333-340, DOI:10.1255/jnirs.897

44. Lopes, M.B.; Wolff, J.-C.; Bioucas-Dias, J.M.; Figueiredo, M.A.T. (2010). 'Reply to the comments on "near-infrared hyperspectral unmixing based on a minimum volume criterion for fast and accurate chemometric characterization of counterfeit tablets"'. Analytical Chemistry, 82 (20): 8753-8754, DOI:10.1021/ac102443w

45. Lopes, M.B.; Wolff, J.-C.; Bioucas-Dias, J.M.; Figueiredo, M.A.T. (2010). Near-infrared hyperspectral unmixing based on a minimum volume criterion for fast and accurate chemometric characterization of counterfeit tablets. Analytical Chemistry, 82 (4): 1462-1469, DOI:10.1021/ac902569e

46. Lopes, M.B.; Wolff, J.-C.; Bioucas-Dias, J.M.; Figueiredo, M.A.T. (2009). Determination of the composition of counterfeit Heptodin™ tablets by near infrared chemical imaging and classical least squares estimation. Analytica Chimica Acta, 641 (1-2): 46-51, DOI:10.1016/j.aca.2009.03.034

47. Lopes, M.B.; Wolff, J.-C. (2009). Investigation into classification/sourcing of suspect counterfeit Heptodin™ tablets by near infrared chemical imaging. Analytica Chimica Acta, 633 (1): 149-155, DOI:10.1016/j.aca.2008.11.036

48. Brás, L.P.; Lopes, M.B.; Ferreira, A.P.; Menezes, J.C. (2008). A bootstrap-based strategy for spectral interval selection in PLS regression. Journal of Chemometrics, 22 (11-12): 695-700, DOI:10.1002/cem.1153

49. Cabral, H.N.; Vasconcelos, R.; Vinagre, C. ; França, S.; Fonseca, V.; Maia, A.; Reis-Santos, P.; Lopes, M.B.; Ruano, M.; Campos, J.; Freitas, V.; Santos, P.T.; Costa, M.J. (2007). Relative importance of estuarine flatfish nurseries along the Portuguese coast. Journal of Sea Research, 57 (2-3): 209-217, DOI:10.1016/j.seares.2006.08.007

50. Lopes, M.B.; Murta, A.G.; Cabral, H.N. (2006). Discrimination of snipefish Macroramphosus species and boarfish Capros aper morphotypes through multivariate analysis of body shape. Helgoland Marine Research, 60 (1): 18-24, DOI:10.1007/s10152-005-0010-7

51. Lopes, M.B.; Murta, A.G.; Cabral, H.N. (2006). The ecological significance of the zooplanktivores, snipefish Macroramphosus spp. and boarfish Capros aper, in the food web of the south-east North Atlantic. Journal of Fish Biology, 69 (2): 363-378, DOI:10.1111/j.1095-8649.2006.01093.x

52. Cabral, H.N.; Lopes, M.B.; Loeper, R. (2002). Trophic niche overlap between flatfishes in a nursery area on the Portuguese coast. Scientia Marina, 66 (3): 293-300, DOI:10.3989/scimar.2002.66n3293

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ARTICLES IN CONFERENCE PROCEEDINGS

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1. Brandão, J.; Lopes, M.B. (2024). Carrasquinha E. Refining gene selection and outlier detection in glioblastoma based on a consensus approach for regularized survival models. In: Rojas, I., Ortuño, F., Rojas, F., Herrera, L.J., Valenzuela, O. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2024. Lecture Notes in Computer Science, vol 14848. Spinger, Cham, DOI:10.1007/978-3-031-64629-4_2

2. Ribeiro, D.; Ferraz, F.; Lopes, M.B.; Rodrigues, S.; Costa, P.R.; Vinga, S.; Carvalho, A.M. (2023). Causal graph discovery for explainable insights on marine biotoxin shellfish contamination. In: Quaresma, P., Camacho, D., Yin, H., Gonçalves, T., Julian, V., Tallón-Ballesteros, A.J. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2023. IDEAL 2023. Lecture Notes in Computer Science, vol 14404. Springer, Cham, DOI:10.1007/978-3-031-48232-8_44

3. Ferraz, F.; Ribeiro, D.; Lopes, M.B.; Pedro, S.; Susana Vinga, Carvalho A.M. (2023). Comparative analysis of machine learning models for time-series forecasting of Escherichia coli contamination in Portuguese shellfish production areas. In: Nicosia, G., Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P.M., Umeton, R. (eds) Machine Learning, Optimization, and Data Science. LOD 2023. Lecture Notes in Computer Science, vol 14505. Springer, Cham, DOI:10.1007/978-3-031-53969-5_14

4. Coletti, R.; Lopes, M.B. (2023) Multi-omics data integration and network inference for biomarker discovery in glioma. In: Moniz, N., Vale, Z., Cascalho, J., Silva, C., Sebastião, R. (eds) Progress in Artificial Intelligence. EPIA 2023. Lecture Notes in Computer Science, vol 14116. Springer, Cham, DOI:10.1007/978-3-031-49011-8_20

5. Baião, A.R.; Peixoto, C.; Lopes, M.B.; Costa, P.R.; Carvalho, A.M.; Vinga, S. (2023). Evaluating the causal role of environmental data in shellfish biotoxin contamination on the Portuguese coast. In: Moniz, N., Vale, Z., Cascalho, J., Silva, C., Sebastião, R. (eds) Progress in Artificial Intelligence. EPIA 2023. Lecture Notes in Computer Science, vol 14116. Springer, Cham, DOI:10.1007/978-3-031-49011-8_26

6. Ibrahimi, E.; Norouzirad, M.; Meto, M.; Lopes, M.B. (2023). Regularized generalized linear models to disclose host-microbiome associations in colorectal cancer. In Proceedings of the 6th International Conference on Mathematics and Statistics (ICoMS 2023). Association for Computing Machinery, New York, USA, DOI:10.1145/3613347.3613362

7. Diegues, I.; Vinga, S.; Lopes, M.B. (2020). Identification of common gene signatures in microarray and RNA-sequencing data using network-based regularization. In: Rojas I., Valenzuela O., Rojas F., Herrera L., Ortuño F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2020. Lecture Notes in Computer Science, vol 12108. Springer, Cham, DOI:10.1007/978-3-030-45385-5_2

8. Carrasquinha, E.; Veríssimo, A.; Lopes, M.B.; Vinga, S. (2020). Network-based variable selection for survival outcomes in oncological data. In: Rojas I., Valenzuela O., Rojas F., Herrera L., Ortuño F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2020. Lecture Notes in Computer Science, vol 12108. Springer, Cham, DOI:10.1007/978-3-030-45385-5_49

9. Veríssimo, A.; Lopes, M.B.; Carrasquinha, E.; Vinga, S. (2020). Random sample consensus for the robust identification of outliers in cancer data. In: Cazzaniga P., Besozzi D., Merelli I., Manzoni L. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2019. Lecture Notes in Computer Science, vol 12313. Springer, Cham, DOI:10.1007/978-3-030-63061-4_11

10. Villa-Brito, J.; Lopes, M.B.; Carvalho, A.; Vinga, S. (2020). Unravelling Breast and Prostate Common Gene Signatures by Bayesian Network Learning. In: Raposo M., Ribeiro P., Sério S., Staiano A., Ciaramella A. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2018. Lecture Notes in Computer Science, vol 11925. Springer, Cham, DOI:10.1007/978-3-030-34585-3_25

11. Lopes, M.B.; Veríssimo, A.; Carrasquinha, E.; Vinga, S. (2019) On the role of hub and orphan genes in the diagnosis of breast invasive carcinoma. In: Nicosia G., Pardalos P., Umeton R., Giuffrida G., Sciacca V. (eds) Machine Learning, Optimization, and Data Science. LOD 2019. Lecture Notes in Computer Science, vol 11943. Springer, Cham, DOI:10.1007/978-3-030-37599-7_52

12. Carrasquinha, E.; Veríssimo, A.; Lopes, M.B.; Vinga, S. (2019). Variable selection and outlier detection in regularized survival models: application to melanoma gene expression data. In: Nicosia G., Pardalos P., Giuffrida G., Umeton R., Sciacca V. (eds) Machine Learning, Optimization, and Data Science. LOD 2018. Lecture Notes in Computer Science, vol 11331. Springer, Cham, DOI:10.1007/978-3-030-13709-0_36

13. Sampaio, P.N.; Cunha, B.; Rosa, F.; Sales, K.; Lopes, M.B.; Calado, C.R.C. (2015). Molecular fingerprint of human gastric cell line infected by Helicobacter pylori, DOI:10.1109/ENBENG.2015.7088877

14. Lopes, M.B.; Sales, K.C.; Lopes, V.V.; Calado, C.R.C. (2013). Real-time plasmid monitoring of batch and fed-batch Escherichia coli cultures by NIR spectroscopy, DOI:10.1109/ENBENG.2013.6518394

15. Lopes, M.B.; Scholtz, T.; Silva, D.; Santos, I.; Silva, T.; Sampaio, P.; Couto, A.; Lopes, V.V.; Calado, C.R.C. (2012). Modelling, monitoring and control of plasmid bioproduction in Escherichia coli cultures, DOI:10.1109/ENBENG.2012.6331370

16. Lopes, M.B.; Bioucas-Dias, J.M.; Figueiredo, M.A.T.; Wolff, J.-C.; Mistry, N.; Warrack, J. (2011). Comparison of near infrared and Raman hyperspectral unmixing performances for chemical identification of pharmaceutical tablets, DOI:10.1109/WHISPERS.2011.6080869

17. Lopes, M.B.; Bioucas-Dias, J.M.; Figueiredo, M.A.T.; Wolff, J.-C. (2009). Spectral unmixing via minimum volume simplices: Application to near infrared spectra of counterfeit tablets, DOI:10.1109/WHISPERS.2009.5289081

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ARTICLES IN NATIONAL JOURNALS

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1. Santos, H.; Sousa, D.; Caldeira, P.; Lopes, M.B.; Açucena, F.; Ramos, A.; Moniz, M.; Lopes, A.; Guerreiro, H. (2008). Survival and prognosis of patients with percutaneous endoscopic gastrostomy. Revista APNEP, 2: 157-159 (in portuguese)

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ARTICLES IN SCIENTIFIC NEWSLETTERS

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1. Lopes, M.B.; Wolff, J.-C.; Bioucas-Dias, J.M.; Figueiredo, M.A.T. (2011). Investigating counterfeit medicines - the near infrared chemical imaging picture. NIR News, 22: 10-18, DOI:10.1255/nirn.1243

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SOFTWARE

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1. Veríssimo, A.; Vinga, S.; Carrasquinha, E.; Lopes, M.B. (2018). glmSparseNet - Network centrality metrics for elastic-net regularized models. Bioconductor R package 

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