The Educational Materials of the OLISSIPO Schools are publicly available and they target senior and ESR researchers:

• OLISSIPO School on Modelling and Analysis of Single Cell Multiple Biological Omics, February 5-10, 2023, https://olissipo.inesc-id.pt/modelling-analysis-single-cell-multiple-biological-omics/ 
• EMBL Training, May 9-12 2023, https://olissipo.inesc-id.pt/2023/04/19/olissipo-workshops-embl-training/ 
• OLISSIPO School on Mathematical modelling of inter-cell and communities’ interactions with a focus on metabolism, 6-9 Feb 2024 [https://olissipo.inesc-id.pt/olissipo-school-on-mathematical-modelling/ 

The Consortium has also completed two modules covering topics such as:

1. From molecules to computing: Digital Biology;
2. Introduction to Computational Biology (presentation, notebook and example datasets).
   
   
   

Notably, the OLISSIPO YouTube channel provides additional content that can be easily reused for educational purposes, in particular:

• Isto é Matemática – A Inteligência Artificial e o futuro da medicina
  
• Machine Learning na Biomedicina e Biotecnologia
  

   

• Twin Seminars [link]

1. Godinho, J., Carvalho, A. M., & Vinga, S. (2020, Dec 10-12). Latent Variable Modelling and Variational Inference for scRNA-seq Differential Expression Analysis. Paper presented at the 10th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), Electr Network.
2. 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
3. Constantino, C. S., Carvalho, A. M., & Vinga, S. (2021). Coupling sparse Cox models with clustering of longitudinal transcriptomics data for trauma prognosis. BioData Min, 14(1), 25. doi:10.1186/s13040-021-00257-8
4. 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
5. 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
6. 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
7. Neto, J. P., Alho, I., Costa, L., Casimiro, S., Valério, 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
8. 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
9. Ferrarini, M. G., Ziska, I., Andrade, R., Julien-Laferriere, A., Duchemin, L., Cesar, R. M., . . . Sagot, M. F. (2022). Totoro: Identifying Active Reactions During the Transient State for Metabolic Perturbations. Frontiers in Genetics, 13. doi:10.3389/fgene.2022.815476
10. 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, 31(5), 947-958. doi:10.1177/09622802211072456
11. Leitao, B. N., Faustino, P., & Vinga, S. (2022, Aug 31-Sep 02). Comparative Evaluation of Classification Indexes and Outlier Detection of Microcytic Anaemias in a Portuguese Sample. Paper presented at the 21st EPIA Conference on Artificial Intelligence (EPIA), Univ Lisbon, Lisbon, PORTUGAL.
12. 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). doi:10.3390/toxins14100679
13. Peixoto, C., Martins, M., Costa, L., & Vinga, S. (2022). Kidney Cancer Biomarker Selection Using Regularized Survival Models. Cells, 11(15). doi:10.3390/cells11152311
14. Baiao, A. R., Peixoto, C., Lopes, M. B., Costa, P. R., Carvalho, A. M., & Vinga, S. (2023, Sep 05-08). Evaluating the Causal Role of Environmental Data in Shellfish Biotoxin Contamination on the Portuguese Coast. Paper presented at the 22nd EPIA Conference on Artificial Intelligence (EPIA), Azores, PORTUGAL.
15. Branco, A. P., Vaz, C., & Francisco, A. P. (2023). Computing RF Tree Distance over Succinct Representations. Algorithms, 17(1). doi:10.3390/a17010015
16. Costa, L. M., Colaco, J., Carvalho, A. M., Vinga, S., & Teixeira, A. S. (2023). Using Markov chains and temporal alignment to identify clinical patterns in Dementia. Journal of Biomedical Informatics, 140. doi:10.1016/j.jbi.2023.104328
17. Espada, J., Francisco, A. P., Rocher, T., Russo, L. M. S., & Vaz, C. (2023). On Finding Optimal (Dynamic) Arborescences. Algorithms, 16(12). doi:10.3390/a16120559
18. Ferraz, F., Ribeiro, D., Lopes, M. B., Pedro, S., Vinga, S., & Carvalho, A. M. (2023, Sep 22-26). Comparative Analysis of Machine Learning Models for Time-Series Forecasting of Escherichia Coli Contamination in Portuguese Shellfish Production Areas. Paper presented at the 9th Annual Conference on Machine Learning, Optimization and Data Science (LOD), Grasmere, ENGLAND.
19. Luo, X. G., Kuipers, J., & Beerenwinkel, N. (2023). Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees. Nature Communications, 14(1). doi:10.1038/s41467-023-39400-w
20. Mussbacher, M., Derler, M., Basílio, J., & Schmid, J. A. (2023). NF-κB in monocytes and macrophages – an inflammatory master regulator in multitalented immune cells. Frontiers in Immunology, 14. doi:10.3389/fimmu.2023.1134661
21. Peixoto, C., Lopes, M. B., Martins, M., Casimiro, S., Sobral, D., Grosso, A. R., . . . Vinga, S. (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
22. 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
23. Lourenço, B., Vaz, C., Coimbra, M. E., & Francisco, A. P. (2024). phyloDB: A framework for large-scale phylogenetic analysis of sequence based typing data. Softwarex, 26. doi:10.1016/j.softx.2024.101668
24. Baciu-Dragan, M. A., & Beerenwinkel, N. (2024). Oncotree2vec-a method for embedding and clustering of tumor mutation trees. Bioinformatics, 40, i180-i188. doi:10.1093/bioinformatics/btae214
25. Mussbacher, M., Basilio, J., Belakova, B., Pirabe, A., Ableitner, E., Campos-Medina, M., & Schmid, J. A. (2024). Effects of Chronic Inflammatory Activation of Murine and Human Arterial Endothelial Cells at Normal Lipoprotein and Cholesterol Levels In Vivo and In Vitro. Cells, 13(9), Article 773.
    doi:10.3390/cells13090773
26. Coletti, R., de Mendonça, M. L., 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, Article 11779322241271535. doi:10.1177/11779322241271535