Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Wysocki, Oskar"'
Autor:
Wysocki, Oskar, Wysocka, Magdalena, Carvalho, Danilo, Bogatu, Alex Teodor, Gusicuma, Danilo Miranda, Delmas, Maxime, Unsworth, Harriet, Freitas, Andre
We present BioLunar, developed using the Lunar framework, as a tool for supporting biological analyses, with a particular emphasis on molecular-level evidence enrichment for biomarker discovery in oncology. The platform integrates Large Language Mode
Externí odkaz:
http://arxiv.org/abs/2406.18626
Inferring over and extracting information from Large Language Models (LLMs) trained on a large corpus of scientific literature can potentially drive a new era in biomedical research, reducing the barriers for accessing existing medical evidence. This
Externí odkaz:
http://arxiv.org/abs/2305.17819
The recent evolution in Natural Language Processing (NLP) methods, in particular in the field of argumentation mining, has the potential to transform the way we interact with text, supporting the interpretation and analysis of complex discourse and d
Externí odkaz:
http://arxiv.org/abs/2303.03235
There is an increasing interest in the use of Deep Learning (DL) based methods as a supporting analytical framework in oncology. However, most direct applications of DL will deliver models with limited transparency and explainability, which constrain
Externí odkaz:
http://arxiv.org/abs/2207.00812
Autor:
Bogatu, Alex, Wysocka, Magdalena, Wysocki, Oskar, Butterworth, Holly, Landers, Donal, Kilgour, Elaine, Freitas, Andre
Cytokine release syndrome (CRS), also known as cytokine storm, is one of the most consequential adverse effects of chimeric antigen receptor therapies that have shown promising results in cancer treatment. When emerging, CRS could be identified by th
Externí odkaz:
http://arxiv.org/abs/2206.10612
Autor:
Wysocki, Oskar, Davies, Jessica Katharine, Vigo, Markel, Armstrong, Anne Caroline, Landers, Dónal, Lee, Rebecca, Freitas, André
This paper contributes with a pragmatic evaluation framework for explainable Machine Learning (ML) models for clinical decision support. The study revealed a more nuanced role for ML explanation models, when these are pragmatically embedded in the cl
Externí odkaz:
http://arxiv.org/abs/2204.05030
Autor:
Wysocki, Oskar, Zhou, Zili, O'Regan, Paul, Ferreira, Deborah, Wysocka, Magdalena, Landers, Dónal, Freitas, André
Publikováno v:
Computational Linguistics 2022
Specialised transformers-based models (such as BioBERT and BioMegatron) are adapted for the biomedical domain based on publicly available biomedical corpora. As such, they have the potential to encode large-scale biological knowledge. We investigate
Externí odkaz:
http://arxiv.org/abs/2202.02432
This paper proposes a novel statistical corpus analysis framework targeted towards the interpretation of Natural Language Processing (NLP) architectural patterns at scale. The proposed approach combines saturation-based lexicon construction, statisti
Externí odkaz:
http://arxiv.org/abs/2107.08124
Publikováno v:
In Proceedings of the 2nd Workshop on Evaluation and Comparison of NLP Systems, Association for Computational Linguistics. 2021
SemEval is the primary venue in the NLP community for the proposal of new challenges and for the systematic empirical evaluation of NLP systems. This paper provides a systematic quantitative analysis of SemEval aiming to evidence the patterns of the
Externí odkaz:
http://arxiv.org/abs/2005.14299
Autor:
Bogatu, Alex, Wysocka, Magdalena, Wysocki, Oskar, Butterworth, Holly, Pillai, Manon, Allison, Jennifer, Landers, Dónal, Kilgour, Elaine, Thistlethwaite, Fiona, Freitas, André
Publikováno v:
In Journal of Biomedical Informatics June 2023 142