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Thomas P. Quinn
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2020 – today
- 2024
- [j10]Simon Coghlan
, Thomas P. Quinn:
Ethics of using artificial intelligence (AI) in veterinary medicine. AI Soc. 39(5): 2337-2348 (2024) - 2023
- [j9]Tri Minh Nguyen
Explaining Black Box Drug Target Prediction Through Model Agnostic Counterfactual Samples. IEEE ACM Trans. Comput. Biol. Bioinform. 20(2): 1020-1029 (2023) - 2022
- [j8]Thomas P. Quinn, Stephan Jacobs
The three ghosts of medical AI: Can the black-box present deliver? Artif. Intell. Medicine 124: 102158 (2022) - [c2]Elliott Gordon-Rodríguez, Thomas P. Quinn, John P. Cunningham:
Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome. NeurIPS 2022 - [i9]Elliott Gordon-Rodríguez, Thomas P. Quinn, John P. Cunningham:
Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome. CoRR abs/2205.09906 (2022) - 2021
- [j7]Haripriya Harikumar
Personalized single-cell networks: a framework to predict the response of any gene to any drug for any patient. BioData Min. 14(1) (2021) - [j6]Thin Nguyen
GraphDTA: predicting drug-target binding affinity with graph neural networks. Bioinform. 37(8): 1140-1147 (2021) - [j5]Elliott Gordon-Rodríguez
Learning sparse log-ratios for high-throughput sequencing data. Bioinform. 38(1): 157-163 (2021) - [j4]Thomas P. Quinn
Trust and medical AI: the challenges we face and the expertise needed to overcome them. J. Am. Medical Informatics Assoc. 28(4): 890-894 (2021) - [j3]Thin Nguyen
PAN: Personalized Annotation-Based Networks for the Prediction of Breast Cancer Relapse. IEEE ACM Trans. Comput. Biol. Bioinform. 18(6): 2841-2847 (2021) - [i8]Tri Minh Nguyen, Thomas P. Quinn, Thin Nguyen, Truyen Tran:
Counterfactual Explanation with Multi-Agent Reinforcement Learning for Drug Target Prediction. CoRR abs/2103.12983 (2021) - [i7]Thomas P. Quinn, Simon Coghlan:
Readying Medical Students for Medical AI: The Need to Embed AI Ethics Education. CoRR abs/2109.02866 (2021) - [i6]Thomas P. Quinn, Sunil Gupta, Svetha Venkatesh, Vuong Le:
A Field Guide to Scientific XAI: Transparent and Interpretable Deep Learning for Bioinformatics Research. CoRR abs/2110.08253 (2021) - 2020
- [c1]Thomas P. Quinn, Dang Nguyen, Santu Rana, Sunil Gupta, Svetha Venkatesh:
DeepCoDA: personalized interpretability for compositional health data. ICML 2020: 7877-7886 - [i5]Thomas P. Quinn, Dang Nguyen, Santu Rana, Sunil Gupta, Svetha Venkatesh:
DeepCoDA: personalized interpretability for compositional health data. CoRR abs/2006.01392 (2020) - [i4]Thomas P. Quinn, Manisha Senadeera, Stephan Jacobs, Simon Coghlan, Vuong Le:
Trust and Medical AI: The challenges we face and the expertise needed to overcome them. CoRR abs/2008.07734 (2020) - [i3]Thomas P. Quinn, Stephan Jacobs, Manisha Senadeera, Vuong Le, Simon Coghlan:
The Three Ghosts of Medical AI: Can the Black-Box Present Deliver? CoRR abs/2012.06000 (2020)
2010 – 2019
- 2018
- [j2]Thomas P. Quinn
Understanding sequencing data as compositions: an outlook and review. Bioinform. 34(16): 2870-2878 (2018) - [j1]Thomas P. Quinn
Benchmarking differential expression analysis tools for RNA-Seq: normalization-based vs. log-ratio transformation-based methods. BMC Bioinform. 19(1): 274:1-274:15 (2018) - [d2]Thomas P. Quinn
Improving the classification of neuropsychiatric conditions using gene ontology terms as features: Figure Data. Version 1. Zenodo, 2018 [all versions] - [d1]Thomas P. Quinn
Improving the classification of neuropsychiatric conditions using gene ontology terms as features: Figure Data. Version 2. Zenodo, 2018 [all versions] - [i2]Thomas P. Quinn
Visualizing balances of compositional data: A new alternative to balance dendrograms. F1000Research 7: 1278 (2018) - 2016
- [i1]Thomas P. Quinn
exprso: an R-package for the rapid implementation of machine learning algorithms. F1000Research 5: 2588 (2016)
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