Thomas P. Quinn
Thomas P. Quinn
Independent Scientist
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Cited by
Cited by
Understanding sequencing data as compositions: an outlook and review
TP Quinn, I Erb, MF Richardson, TM Crowley
Bioinformatics 34 (16), 2870-2878, 2018
GraphDTA: Predicting drug–target binding affinity with graph neural networks
T Nguyen, H Le, TP Quinn, T Nguyen, TD Le, S Venkatesh
Bioinformatics 37 (8), 1140-1147, 2021
A field guide for the compositional analysis of any-omics data
TP Quinn, I Erb, G Gloor, C Notredame, MF Richardson, TM Crowley
GigaScience 8 (9), giz107, 2019
propr: an R-package for identifying proportionally abundant features using compositional data analysis
TP Quinn, MF Richardson, D Lovell, TM Crowley
Scientific reports 7 (1), 1-9, 2017
Aza-crown macrocycles as chiral solvating agents for mandelic acid derivatives
TP Quinn, PD Atwood, JM Tanski, TF Moore, JF Folmer-Andersen
The Journal of Organic Chemistry 76 (24), 10020-10030, 2011
Blood transcriptomic comparison of individuals with and without autism spectrum disorder: A combined‐samples mega‐analysis
DS Tylee, JL Hess, TP Quinn, R Barve, H Huang, Y Zhang‐James, ...
American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 174 …, 2017
Benchmarking differential expression analysis tools for RNA-Seq: normalization-based vs. log-ratio transformation-based methods
TP Quinn, TM Crowley, MF Richardson
BMC bioinformatics 19 (1), 1-15, 2018
Deep in the bowel: highly interpretable neural encoder-decoder networks predict gut metabolites from gut microbiome
V Le, TP Quinn, T Tran, S Venkatesh
BMC genomics 21 (4), 1-15, 2020
Bioinformatic analyses and conceptual synthesis of evidence linking ZNF804A to risk for schizophrenia and bipolar disorder
JL Hess, TP Quinn, S Akbarian, SJ Glatt
American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 168 …, 2015
Machine-learning classification of 22q11. 2 deletion syndrome: a diffusion tensor imaging study
DS Tylee, Z Kikinis, TP Quinn, KM Antshel, W Fremont, MA Tahir, A Zhu, ...
NeuroImage: Clinical 15, 832-842, 2017
Trust and medical AI: the challenges we face and the expertise needed to overcome them
TP Quinn, M Senadeera, S Jacobs, S Coghlan, V Le
Journal of the American Medical Informatics Association 28 (4), 890-894, 2021
Interpretable log contrasts for the classification of health biomarkers: a new approach to balance selection
TP Quinn, I Erb
Msystems 5 (2), e00230-19, 2020
Differential proportionality-a normalization-free approach to differential gene expression
I Erb, T Quinn, D Lovell, C Notredame
bioRxiv, 134536, 2018
Meta-analysis and systematic review of ADGRL3 (LPHN3) polymorphisms in ADHD susceptibility
EM Bruxel, CR Moreira-Maia, GC Akutagava-Martins, TP Quinn, M Klein, ...
Molecular psychiatry 26 (6), 2277-2285, 2021
Amalgams: data-driven amalgamation for the dimensionality reduction of compositional data
TP Quinn, I Erb
NAR genomics and bioinformatics 2 (4), lqaa076, 2020
DeepTRIAGE: interpretable and individualised biomarker scores using attention mechanism for the classification of breast cancer sub-types
A Beykikhoshk, TP Quinn, SC Lee, T Tran, S Venkatesh
BMC medical genomics 13 (3), 1-10, 2020
The three ghosts of medical AI: Can the black-box present deliver?
TP Quinn, S Jacobs, M Senadeera, V Le, S Coghlan
Artificial intelligence in medicine 124, 102158, 2022
Visualizing balances of compositional data: A new alternative to balance dendrograms
TP Quinn
F1000Research 7, 2018
exprso: an R-package for the rapid implementation of machine learning algorithms
T Quinn, D Tylee, S Glatt
F1000Research 5, 2016
Infant microbiota in colic: predictive associations with problem crying and subsequent child behavior
A Loughman, T Quinn, ML Nation, A Reichelt, RJ Moore, TTH Van, ...
Journal of Developmental Origins of Health and Disease 12 (2), 260-270, 2021
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