Thomas P. Quinn

According to our database, Thomas P. Quinn authored at least 18 papers between 2016 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Explaining Black Box Drug Target Prediction Through Model Agnostic Counterfactual Samples.

Tri Minh Nguyen, Thomas P. Quinn, Thin Nguyen, Truyen Tran

IEEE ACM Trans. Comput. Biol. Bioinform., 2023

2022

The three ghosts of medical AI: Can the black-box present deliver?

Thomas P. Quinn, Stephan Jacobs, Manisha Senadeera, Vuong Le, Simon Coghlan

Artif. Intell. Medicine, 2022

Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome.

Elliott Gordon-Rodríguez, Thomas P. Quinn, John P. Cunningham

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

PAN: Personalized Annotation-Based Networks for the Prediction of Breast Cancer Relapse.

Thin Nguyen, Samuel C. Lee, Thomas P. Quinn, Buu Minh Thanh Truong, Xiaomei Li, Truyen Tran, Svetha Venkatesh, Thuc Duy Le

IEEE ACM Trans. Comput. Biol. Bioinform., 2021

Trust and medical AI: the challenges we face and the expertise needed to overcome them.

Thomas P. Quinn, Manisha Senadeera, Stephan Jacobs, Simon Coghlan, Vuong Le

J. Am. Medical Informatics Assoc., 2021

A Field Guide to Scientific XAI: Transparent and Interpretable Deep Learning for Bioinformatics Research.

Thomas P. Quinn, Sunil Gupta, Svetha Venkatesh, Vuong Le

CoRR, 2021

Readying Medical Students for Medical AI: The Need to Embed AI Ethics Education.

Thomas P. Quinn, Simon Coghlan

CoRR, 2021

Counterfactual Explanation with Multi-Agent Reinforcement Learning for Drug Target Prediction.

Tri Minh Nguyen, Thomas P. Quinn, Thin Nguyen, Truyen Tran

CoRR, 2021

GraphDTA: predicting drug-target binding affinity with graph neural networks.

Thin Nguyen, Hang Le, Thomas P. Quinn, Tri Nguyen, Thuc Duy Le, Svetha Venkatesh

Bioinform., 2021

Learning sparse log-ratios for high-throughput sequencing data.

Elliott Gordon-Rodríguez, Thomas P. Quinn, John P. Cunningham

Bioinform., 2021

Personalized single-cell networks: a framework to predict the response of any gene to any drug for any patient.

Haripriya Harikumar, Thomas P. Quinn, Santu Rana, Sunil Gupta, Svetha Venkatesh

BioData Min., 2021

2020

DeepCoDA: personalized interpretability for compositional health data.

Thomas P. Quinn, Dang Nguyen, Santu Rana, Sunil Gupta, Svetha Venkatesh

Proceedings of the 37th International Conference on Machine Learning, 2020

2018

Improving the classification of neuropsychiatric conditions using gene ontology terms as features: Figure Data.

Thomas P. Quinn, Samuel C. Lee, Svetha Venkatesh, Thin Nguyen

Dataset, August, 2018

Improving the classification of neuropsychiatric conditions using gene ontology terms as features: Figure Data.

Thomas P. Quinn, Samuel C. Lee, Svetha Venkatesh, Thin Nguyen

Dataset, August, 2018

Visualizing balances of compositional data: A new alternative to balance dendrograms.

Thomas P. Quinn

F1000Research, 2018

Benchmarking differential expression analysis tools for RNA-Seq: normalization-based vs. log-ratio transformation-based methods.

Thomas P. Quinn, Tamsyn M. Crowley, Mark F. Richardson

BMC Bioinform., 2018

Understanding sequencing data as compositions: an outlook and review.

Thomas P. Quinn, Ionas Erb, Mark F. Richardson, Tamsyn M. Crowley

Bioinform., 2018

2016

exprso: an R-package for the rapid implementation of machine learning algorithms.

Thomas P. Quinn, Daniel Tylee, Stephen J. Glatt

F1000Research, 2016