NOVA LINCS Group: Intelligent Systems Research Topics: - Bioinformatics, Natural Language Processing, Deep learning Google Scholar: https://scholar.google.com/citations?user=CusKTEIAAAAJ&hl=en&authuser=1 Andre Lamurias has been an Assistant Professor in the Department of Computer Science at FCT, NOVA University of Lisbon since 2023. He completed his PhD in 2019 as part of the BioSYS PhD programme in University of Lisbon. He holds a master's degree in Bioinformatics and Computational Biology from the same institution. Previously, he worked at Priberam Labs as a research scientist and as a postdoctoral researcher at Aalborg University. He has been a team member of the Deep Semantic Tagger (DeST) and Data Science meets Microbial Dark Matter projects. Among his most recent and significant achievements is the development of innovative models such as BO-LSTM for incorporating biomedical ontology information into deep learning neural networks, and GraphMB, a metagenomics approach that generates feature representations of DNA sequences based on graphs. He has supervised two MSc students and has four on-going MSc student supervisions. In 2020 he won the FCiencias.ID excellence award for best PhD thesis in the Information and Communication Technologies research area. Selected Publications: - Lamurias, A., Sousa, D., Clarke, L. A., & Couto, F. M. (2019). BO-LSTM: classifying relations via long short-term memory networks along biomedical ontologies. BMC bioinformatics, 20(1), 1-12. - Sousa, D., Lamurias, A., & Couto, F. M. (2019, June). A Silver Standard Corpus of Human Phenotype-Gene Relations. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (pp. 1487-1492), Minneapolis, Minnesota. - Sousa, D., Lamurias, A., & Couto, F. M. (2020). A hybrid approach toward biomedical relation extraction training corpora: combining distant supervision with crowdsourcing. Database, Volume 2020 - Lamurias, A., Sereika, M., Albertsen, M., Hose, K., & Nielsen, T. D. (2022). Metagenomic binning with assembly graph embeddings. Bioinformatics, 38(19), 4481-4487. - Lamurias, A., Tibo, A., Hose, K., Albertsen, M., & Nielsen, T. D. (2023, July). Metagenomic binning using connectivity-constrained variational autoencoders. In International Conference on Machine Learning (pp. 18471-18481). PMLR, Honolulu, Hawaii.