Thomas P. Quinn
Thomas P. Quinn
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Cited by
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Understanding sequencing data as compositions: an outlook and review
TP Quinn, I Erb, MF Richardson, TM Crowley
Bioinformatics 34 (16), 2870-2878, 2018
Intramyocardial transplantation of cardiac telocytes decreases myocardial infarction and improves post‐infarcted cardiac function in rats
B Zhao, Z Liao, S Chen, Z Yuan, C Yilin, KKH Lee, X Qi, X Shen, X Zheng, ...
Journal of cellular and molecular medicine 18 (5), 780-789, 2014
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
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
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
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
GraphDTA: prediction of drug–target binding affinity using graph convolutional networks
T Nguyen, H Le, S Venkatesh
BioRxiv, 684662, 2019
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
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
Differential proportionality-a normalization-free approach to differential gene expression
I Erb, T Quinn, D Lovell, C Notredame
The 7th International Workshop on Compositional Data, CoDaWork 2017 …, 2017
exprso: an R-package for the rapid implementation of machine learning algorithms
T Quinn, D Tylee, S Glatt
F1000Research 5, 2016
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
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
Visualizing balances of compositional data: a new alternative to balance dendrograms
TP Quinn
F1000Research 7, 2018
Interpretable log contrasts for the classification of health biomarkers: a new approach to balance selection
TP Quinn, I Erb
Msystems 5 (2), 2020
Cancer as a tissue anomaly: classifying tumor transcriptomes based only on healthy data
TP Quinn, T Nguyen, SC Lee, S Venkatesh
Frontiers in genetics 10, 599, 2019
Deep in the bowel: Highly interpretable neural encoder-decoder networks predict gut metabolites from gut microbiome. bioRxiv
V Le, TP Quinn, T Tran, S Venkatesh
June, 2019
Another look at microbe–metabolite interactions: how scale invariant correlations can outperform a neural network
TP Quinn, I Erb
bioRxiv, 847475, 2019
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, 1-11, 2020
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