Thomas P. Quinn

发表

propr: An R-package for Identifying Proportionally Abundant Features Using Compositional Data Analysis

David Lovell, Thomas P. Quinn, Mark F. Richardson, 2017 .

Amalgams: data-driven amalgamation for the dimensionality reduction of compositional data

Thomas P. Quinn, Ionas Erb, I. Erb, 2020, NAR genomics and bioinformatics.

Differential expression analysis of log-ratio transformed counts: benchmarking methods for RNA-Seq data

Thomas P. Quinn, Mark F. Richardson, Tamsyn M. Crowley, 2017, bioRxiv.

Novel Mobile handheld and laptop based Indocyanine Green Detection Systems: report of first experience.

Omar Alonso, Thomas P. Quinn, Juan Pablo Gambini, 2015 .

Understanding sequencing data as compositions: an outlook and review

Thomas P. Quinn, Mark F. Richardson, Tamsyn M. Crowley, 2017 .

Test Set Verification Is An Essential Step in Model Building

Thomas P. Quinn, Vuong Le, Adam P.A. Cardilini, 2020 .

Understanding sequencing data as compositions: an outlook and review

Thomas P. Quinn, Mark F. Richardson, Tamsyn M. Crowley, 2017, bioRxiv.

Another look at microbe–metabolite interactions: how scale invariant correlations can outperform a neural network

Thomas P. Quinn, Ionas Erb, I. Erb, 2019, bioRxiv.

A random walk down personalized single-cell networks: predicting the response of any gene to any drug for any patient

Santu Rana, Sunil Gupta, Svetha Venkatesh, 2019, bioRxiv.

Portable Indocyanine Green (ICG) detection system – intraoperative applications

Guang-Zhong Yang, Santiago Matalonga, Omar Alonso, 2019 .

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

Thomas P. Quinn, Mark F. Richardson, Tamsyn M. Crowley, 2018, BMC Bioinformatics.

Deep in the Bowel: Highly Interpretable Neural Encoder-Decoder Networks Predict Gut Metabolites from Gut Microbiome

Truyen Tran, Svetha Venkatesh, Thomas P. Quinn, 2019, BMC Genomics.

A single pipeline for any-omics data

Thomas P. Quinn, 2018 .

Trust and Medical AI: The challenges we face and the expertise needed to overcome them

Stephan Jacobs, Manisha Senadeera, Thomas P. Quinn, 2020, J. Am. Medical Informatics Assoc..

Editorial: Compositional data analysis and related methods applied to genomics—a first special issue from NAR Genomics and Bioinformatics

Gregory B. Gloor, Thomas P. Quinn, Ionas Erb, 2020, NAR genomics and bioinformatics.

Author response for "Test Set Verification Is An Essential Step in Model Building"

Thomas P. Quinn, Vuong Le, Adam P.A. Cardilini, 2020 .

GraphDTA: Predicting drug–target binding affinity with graph neural networks

Svetha Venkatesh, Thin Nguyen, Thomas P. Quinn, 2019 .

Learning distance-dependent motif interactions: an interpretable CNN model of genomic events

Sunil Gupta, Svetha Venkatesh, Dang Nguyen, 2020 .

DeepCoDA: personalized interpretability for compositional health data

Santu Rana, Sunil Gupta, Dang Nguyen, 2020, ICML.

A field guide for the compositional analysis of any-omics data

Thomas P. Quinn, Mark F. Richardson, Tamsyn M. Crowley, 2018, bioRxiv.

DeepTRIAGE: interpretable and individualised biomarker scores using attention mechanism for the classification of breast cancer sub-types

Svetha Venkatesh, Truyen Tran, Adham Beykikhoshk, 2019, bioRxiv.

Machine-learning classification of 22q11.2 deletion syndrome: A diffusion tensor imaging study

Nikos Makris, Ioana L. Coman, Wendy R. Kates, 2017, NeuroImage: Clinical.

Differential proportionality –a normalization-free approach to differential gene expression

Thomas P. Quinn, Ionas Erb, David R. Lovell, 2017, bioRxiv.