Short Courses of the SBCAS 2026

Abo El-Enen, M., Saad, S. and Nazmy, T. (2025) “A survey on retrieval-augmentation generation (RAG) models for healthcare applications”, Neural Computing and Applications, v. 37, p. 28191-28267.

Abrahão, M. T., Nobre, M. R., Madril, P. J., O estado da arte em pesquisa observacional de dados de saúde: A iniciativa OHDSI 2019. [link] [link]

Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O.,Willmore, L., Ballard, A. J., Bambrick, J., et al. (2024). Accurate structure prediction of biomolecular interactions with alphafold 3. Nature, 630:493–500.

Ackloo, S., Li, F., Szewczyk, M., et al. (2025). A target class ligandability evaluation of wd40 repeat-containing proteins. Journal of Medicinal Chemistry, 68(2).

Afolalu, O. O., Akpor, O. A. and Afolalu, S. A. (2025) “A systematic review of interventions for reducing and reporting adverse events in emergency departments: Multidisciplinary approaches and technological innovations”, Collegian, v. 32, p. 34-45. DOI: 10.1016/j.colegn.2024.12.001.

Ahmad, S., Xu, J., Feng, J., Hutchinson, A., Zeng, H., Ghiabi, P., Dong, A., Centrella, P., Clark, M., Guié, M.-A., et al. (2023). Discovery of a first-inclass small-molecule ligand for wdr91 using dna-encoded chemical library selection followed by machine learning. Journal of Medicinal Chemistry, 66(23):16051–16061.

Akheel, S. A. (2025) “Guardrails for Large Language Models: A Review of Techniques and Challenges”, Journal of Artificial Intelligence, Machine Learning and Data Science, v. 3, n. 1, p. 2504-2512. DOI: 10.51219/JAIMLD/syed-arhamakheel/536.

Alabi, R. O., Elmusrati, M., Leivo, I., Almangush, A., and Mäkitie, A. (2023). Machine learning explainability in nasopharyngeal cancer survival using lime and shap. Scientific Reports, 13.

Alammar, J. (2018) “The illustrated transformer”, The illustrated transformer–Jay Alammar–visualizing machine learning one concept at a time, v. 27, n. 1.

Aldhafeeri, F. (2025). Governing artificial intelligence in radiology: A systematic review of ethical, legal, and regulatory frameworks. Diagnostics, 15.

Allen, W. J., Balius, T. E., Mukherjee, S., Brozell, S. R., Moustakas, D. T., Lang, P. T., Case, D. A., Kuntz, I. D., and Rizzo, R. C. (2015). Dock 6: impact of new features and current docking performance. Journal of Computational Chemistry, 36(15):1132–1156.

ALOMARI, L. M. et al. Safety and accuracy of ai in triaging patients in the emergency department. International Journal of Emergency Medicine, Springer, v. 18, n. 1, p. 243, 2025.

Altschuh, D., Lesk, A. M., Bloomer, A. C., and Klug, A. (1987). Correlation of coordinated amino acid substitutions with function in viruses related to tobacco mosaic virus. Journal of Molecular Biology, 193(4):693–707.

Amann, J., Blasimme, A., Vayena, E., Frey, D., Madai, V. I., and Consortium, P. (2020). Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC medical informatics and decision making, 20(1):310.

Amaro, R. E., Baudry, J., Chodera, J., Demir, O., McCammon, J. A., Miao, Y., and Smith, J. C. (2018). Ensemble docking in drug discovery. Biophysical journal, 114(10):2271–2278.

ANTHROPIC. Model Context Protocol Introduction. 2024. [link]. Acesso em: 10 maio 2026.

Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., et al. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58:82–115.

Arrua, O. E., Aderhold, A., Werhli, A. V., and Machado, K. D. S. (2024). Rfl-score: random forest with lasso scoring function for protein-ligand molecular docking. In 2024 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pages 1–8. IEEE.

Associação Nacional de Hospitais Privados (Anahp). A Inteligência Artificial já está sendo usada na saúde: descubra como isso te beneficia. 2025. Publicado com base nos dados apresentados no Showcase de IA nos Hospitais Brasileiros. Disponível em: [link].

ATLAS Tutorial: Explore Concept Sets Video: [link]

Auzine, M. M., Khan, M. H.-M., Baichoo, S., Sahib, N. G., Bissoonauth-Daiboo, P., Gao, X., and Heetun, Z. (2024). Development of an ensemble cnn model with explainable ai for the classification of gastrointestinal cancer. PLOS ONE, 19.

Bachmann, S. (2025). Efficient xai: A low-cost data reduction approach to shap interpretability. J. Artif. Intell. Res., 83.

Bajwa, A., Nosheen, N., Talpur, K., and Akram, S. (2023). A prospective study on diabetic retinopathy detection based on modify convolutional neural network using fundus images at sindh institute of ophthalmology and visual sciences. Diagnostics, 13.

Ballester, P. J. and Mitchell, J. B. (2010). A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking. Bioinformatics, 26(9):1169–1175.

BASTIAN, H.; GLASZIOU, P.; CHALMERS, I. Seventy-five trials and eleven systematic reviews a day: how will we ever keep up? PLoS medicine, Public Library of Science San Francisco, USA, v. 7, n. 9, p. e1000326, 2010.

Belle, V. and Papantonis, I. (2020). Principles and practice of explainable machine learning. Frontiers in Big Data, 4.

Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., Shindyalov, I. N., and Bourne, P. E. (2000). The protein data bank. Nucleic acids research, 28(1):235–242.

BHAVNANI, S. P.; NARULA, J.; SENGUPTA, P. P. Mobile technology and the digitization of healthcare. European heart journal, v. 37, n. 18, p. 1428, 2016. Citado na página page.2424.

Bifarin, O. O. (2022). Interpretable machine learning with tree-based shapley additive explanations: Application to metabolomics datasets for binary classification. PLOS ONE, 18.

Binzagr, F. (2024). Explainable ai-driven model for gastrointestinal cancer classification. Frontiers in medicine, 11:1349373.

Bishop, C. M. and Bishop, H. (2024). Deep Learning: Foundations and Concepts. Springer, Cham.

Bjerrum, E. J. (2017). Smiles enumeration as data augmentation for neural network modeling of molecules. arXiv preprint arXiv:1703.07076.

Bodria, F., Giannotti, F., Guidotti, R., Naretto, F., Pedreschi, D., and Rinzivillo, S. (2021). Benchmarking and survey of explanation methods for black box models. Data Mining and Knowledge Discovery, 37:1719–1778.

Branden, C. I. and Tooze, J. (2012). Introduction to protein structure. Garland Science.

Bray, F., Laversanne, M., Sung, H., Ferlay, J., Siegel, R. L., Soerjomataram, I., and Jemal, A. (2024). Global cancer statistics 2022: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 74(3):229–263.

Breiman, L. (2001). Random forests. Machine Learning, 45(1):5–32.

Brenning, A. (2021). Interpreting machine-learning models in transformed feature space with an application to remote-sensing classification. Machine Learning, 112:3455–3471.

Brown, T. B. et al. (2020) “Language models are few-shot learners”, Advances in Neural Information Processing Systems (NeurIPS), v. 33, p. 1877–1901.

Burley, S. K., Bhikadiya, C., Bi, C., Bittrich, S., Chen, L., Crichlow, G. V., Christie, C. H., Dalenberg, K., Di Costanzo, L., Duarte, J. M., et al. (2021). Rcsb protein data bank: powerful new tools for exploring 3d structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. Nucleic acids research, 49(D1):D437–D451.

Burrell, J. (2016). How the machine ’thinks’: Understanding opacity in machine learning algorithms. Big Data and Society, 3.

CACHE Initiative (2024). Cache challenge #2 results. Accessed March 2026.

Carhart, R. E., Smith, D. H., and Venkataraghavan, R. (1985). Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences, 25(2):64–73.

Cereto-Massagué, A., Ojeda, M. J., Valls, C., Mulero, M., Garcia-Vallvé, S., and Pujadas, G. (2015). Molecular fingerprint similarity search in virtual screening. Methods, 71:58–63.

Cha, Y., Shin, J., Go, B., Lee, D.-S., Kim, Y., Kim, T., and Park, Y.-S. (2021). An interpretable machine learning method for supporting ecosystem management: Application to species distribution models of freshwater macroinvertebrates. Journal of environmental management, 291:112719.

Chai Discovery Team (2024). Chai-1: Decoding the molecular interactions of life. bioRxiv. Preprint.

Chen, C., Isa, N. A. M., and Liu, X. (2024). A review of convolutional neural network based methods for medical image classification. Computers in biology and medicine, 185:109507.

Chen, I. Y., Pierson, E., Rose, S., Joshi, S., Ferryman, K., and Ghassemi, M. (2021). Ethical machine learning in healthcare. Annual review of biomedical data science, 4(1):123–144.

Chen, T. and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In KDD, pages 785–794.

Chithrananda, S., Grand, G., and Ramsundar, B. (2020). Chemberta: large-scale self-supervised pretraining for molecular property prediction. arXiv preprint arXiv:2010.09885.

COLLINS, F. S.; VARMUS, H. A new initiative on precision medicine. New England journal of medicine, Mass Medical Soc, v. 372, n. 9, p. 793–795, 2015. page.2323.

Columbia DBMI: OHDSI Sets Path for Enhancing Trust in Science, Engaging Global Community at 2025 Symposium. COLUMBIA UNIVERSITY, Department of Biomedical Informatics (DBMI). 2025. Disponível em: [link]

Concept Sets in Capr: [link]

Contreras, J., Winterfeld, A., Popp, J., and Bocklitz, T. (2024). Spectral zones-based shap/lime: Enhancing interpretability in spectral deep learning models through grouped feature analysis. Analytical Chemistry, 96:15588 – 15597.

Copeland, R. A., Pompliano, D. L., and Meek, T. D. (2006). Drug–target residence time and its implications for lead optimization. Nature Reviews Drug Discovery, 5(9):730–739.

Covert, I., Lundberg, S. M., and Lee, S.-I. (2020). Understanding global feature contributions with additive importance measures. Advances in neural information processing systems, 33:17212–17223.

Crampon, K., Giorkallos, A., Deldossi, N., Baud, S., and Steffenel, L. A. (2022). Machine-learning methods for ligand–protein molecular docking. Drug Discovery Today, 27(1):151–164.

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Daoud, H. G. and Bayoumi, M. (2019). Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems, 13:804–813.

Daylight Chemical Information Systems (2024). Daylight theory manual: Fingerprints. Accessed: 2026-05-11.

Dekkers OM, Egger M, Altman DG, Vandenbroucke JP. Distinguishing case series from cohort studies. Ann Intern Med. 2012 Jan 3;156(1 Pt 1):37-40. DOI: 10.7326/0003-4819-156-1-201201030-00006. PMID: 22213493.

Diniz, D. N., Rezende, M. T., Bianchi, A. G. C., Carneiro, C. M., Ushizima, D. M., Medeiros, F. N. S. d., and Souza, M. J. F. (2021). A hierarchical feature-based methodology to perform cervical cancer classification. Applied Sciences, 11(9):4091.

Distrito; Associação Brasileira de Startups de Saúde e HealthTechs (ABSS). HealthTech Recap 2024. [S.l.], 2025. Relatório lançado em fevereiro de 2025 com dados do exercício de 2024. Disponível em: [link].

Dong, Y. et al. (2024) “Building guardrails for large language models”, arXiv preprint arXiv:2402.01822.

Durant, J. L., Leland, B. A., Henry, D. R., and Nourse, J. G. (2002). Reoptimization of mdl keys for use in drug discovery. Journal of Chemical Information and Computer Sciences, 42(6):1273–1280.

Edwards, A. M. et al. (2025). Protein–ligand data at scale to support machine learning. Nature Reviews Chemistry, 9:634–645.

Elshawi, R., Al-Mallah, M., and Sakr, S. (2019). On the interpretability of machine learning-based model for predicting hypertension. BMC Medical Informatics and Decision Making, 19.

ESTEVA, A. et al. A guide to deep learning in healthcare. Nature medicine, Nature Publishing Group US New York, v. 25, n. 1, p. 24–29, 2019.

ESTEVA, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. nature, Nature Publishing Group UK London, v. 542, n. 7639, p. 115–118, 2017.

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., and Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639):115–118.

FDA – regulatory information: Real-World Data: Assessing Electronic Health Records and Medical Claims Data to Support Regulatory Decision-Making for Drug and Biological Products Guidance for Industry Final version: [link] More info: [link]

FDA: Food and Drug Administration. Real-World Evidence. 2023–2025. [link]

Ferrari, A. M., Wei, B. Q., Costantino, L., and Shoichet, B. K. (2004). Soft docking and multiple receptor conformations in virtual screening. Journal of medicinal chemistry, 47(21):5076–5084.

Ferreira, L. B. et al. (2024) “Chatbots para Pré-triagem Odontológica: Validação Preditiva e Fluxo de Pacientes em Clínicas Universitárias”, Revista Brasileira de Informática em Saúde (RBIS), v. 21, n. 1.

Ferreira, L. G., dos Santos, R. N., Oliva, G., and Andricopulo, A. D. (2015). Molecular docking and structure-based drug design strategies. Molecules, 20(7):13384–13421.

Foley, E., Jacob, A. and Kapoor, R. (2024) “Generating Test Cases Through Large Language Models”, Major Qualifying Project Report, Worcester Polytechnic Institute, Worcester, MA.

Franzini, R. M., Neri, D., and Scheuermann, J. (2014). Dnaencoded chemical libraries: Advancing beyond conventional small-molecule libraries. Accounts of Chemical Research, 47(4):1247–1255.

Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, pages 1189–1232.

Ganesan, A., Coote, M. L., and Barakat, K. (2017). Molecular dynamics-driven drug discovery: leaping forward with confidence. Drug discovery today, 22(2):249–269.

Gevaert, A. and Saeys, Y. (2022). Pdd-shap: Fast approximations for shapley values using functional decomposition.

Ghasemi, A., Hashtarkhani, S., Schwartz, D. L., and Shaban-Nejad, A. (2024). Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review. Cancer Innovation, 3.

Ghosh, S. K. and Khandoker, A. (2024). Investigation on explainable machine learning models to predict chronic kidney diseases. Scientific Reports, 14.

Gilson, M. K., Liu, T., Baitaluk, M., Nicola, G., Hwang, L., and Chong, J. (2015). Bindingdb in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Research, 44(D1):D1045–D1053.

Gironda-Martínez, A., Donckele, E. J., Samain, F., and Neri, D. (2021). Dna-encoded chemical libraries: A comprehensive review with successful stories and future challenges. ACS Pharmacology & Translational Science, 4(4):1265–1279.

Goldstein, B. A., Navar, A. M., Pencina, M. J., and Ioannidis, J. P. (2017). Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. Journal of the American Medical Informatics Association, 24(1):198–208.

Gomes, K. A. S. (2023) “Gissa Chatbot: uma proposta de Agente Conversacional Inteligente RASA Open-Source para assistência no período gestacional”, Dissertação (Mestrado em Engenharia Elétrica e de Computação), Universidade Federal do Ceará, Sobral.

Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., and Aspuru-Guzik, A. (2018). Automatic chemical design using a data-driven continuous representation of molecules. ACS central science, 4(2):268–276.

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Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning. MIT Press, Cambridge, MA.

Goodman, B. and Flaxman, S. (2017). European union regulations on algorithmic decision-making and a “right to explanation”. AI Magazine, 38(3):50–57.

Goodnow, R. A., Dumelin, C. E., and Keefe, A. D. (2016). Dnaencoded chemistry: enabling the deeper sampling of chemical space. Nature Reviews Drug Discovery, 16(2):131–147.

GRABER, M. L. The incidence of diagnostic error in medicine. BMJ quality & safety, BMJ Publishing Group Ltd, v. 22, n. Suppl 2, p. ii21–ii27, 2013.

Greenwell, B. M. (2017). pdp: An r package for constructing partial dependence plots. R J., 9:421.

Greenwell, B. M. and Boehmke, B. C. (2020). Variable importance plots - an introduction to the vip package. R J., 12:343.

GULSHAN, V. et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. jama, American Medical Association, v. 316, n. 22, p. 2402–2410, 2016.

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HABERLE, T. et al. The impact of nuance dax ambient listening ai documentation: a cohort study. Journal of the American Medical Informatics Association, Oxford University Press, v. 31, n. 4, p. 975–979, 2024.

Hakkoum, H., Idri, A., and Abnane, I. (2024). Global and local interpretability techniques of supervised machine learning black box models for numerical medical data. Eng. Appl. Artif. Intell., 131:107829.

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Hooshyar, D. and Yang, Y. (2024). Problems with shap and lime in interpretable ai for education: A comparative study of post-hoc explanations and neural-symbolic rule extraction. IEEE Access, 12:137472–137490.

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Hu, C., Li, L., ping Huang, W., Wu, T., Xu, Q., Liu, J., and Hu, B. (2022). Interpretable machine learning for early prediction of prognosis in sepsis: A discovery and validation study. Infectious Diseases and Therapy, 11:1117 – 1132.

Hu, X., Zhu, M., Feng, Z., and Stanković, L. (2024b). Manifold-based shapley explanations for high dimensional correlated features. Neural networks : the official journal of the International Neural Network Society, 180:106634.

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Kelodjou, G., Rozé, L., Masson, V., Galárraga, L., Gaudel, R., Tchuente, M., and Termier, A. (2024). Shaping up shap: enhancing stability through layer-wise neighbor selection. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 13094–13103.

Kernohan, K. D. and Boycott, K. M. (2024). The expanding diagnostic toolbox for rare genetic diseases. Nature Reviews Genetics, 25(6):401–415.

Khan, M. M., Shah, N., Shaikh, N., Thabet, A., Alrabayah, T., and belkhair, S. (2024). Towards secure and trusted ai in healthcare: A systematic review of emerging innovations and ethical challenges. International journal of medical informatics, 195:105780.

Kheder, W., Leblouba, M., Rego, R., and Hamdoon, Z. (2025). Multicentre validation and clinical interpretation of an explainable gradient-boosting model for dental-implant survival/failure prediction. Journal of dentistry, page 106166.

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