AS
SUNY Orange: January 2007 to May 2009
Graduated summa cum laude, obtaining an Associate’s Degree
in Science with a concentration in biology
Open source software
R packages which I designed and maintain,
balance | CRAN
Balances have become a cornerstone of compositional data analysis.
However, conceptualizing balances is difficult, especially for
high-dimensional data. Most often, investigators visualize balances with
“balance dendrograms”. However, this visualization tool does not scale
well for large data. This package provides an alternative scheme for
visualizing balances, described in [Quinn (2018)]. This package also
provides a method for principal balance analysis.
exprso | CRAN
Supervised machine learning has an increasingly important role in
data analysis. This package introduces a framework for rapidly building
and deploying supervised machine learning in a high-throughput manner.
This package provides a user-friendly interface that empowers
investigators to execute state-of-the-art binary and multi-class
classification, as well as regression, with minimal programming
experience necessary.
peakRAM | CRAN
When working with big data sets, RAM conservation is critically
important. However, it is not always enough to just monitor the size of
the objects created. So-called “copy-on-modify” behavior, characteristic
of R, means that some expressions or functions may require an
unexpectedly large amount of RAM overhead. For example, replacing a
single value in a matrix duplicates that matrix in the back-end, making
this task require twice as much RAM as that used by the matrix itself.
This package makes it easy to monitor the total and peak RAM used so
that developers can quickly identify and eliminate RAM hungry code.
The bioinformatic evaluation of gene co-expression often begins with
correlation-based analyses. However, correlation lacks validity when
applied to relative data, including count data generated by
next-generation sequencing. This package implements several metrics for
proportionality, including phi [Lovell et al (2015)] and rho [Erb and
Notredame (2016)]. This package also implements several metrics for
differential proportionality. Unlike correlation, these measures give
the same result for both relative and absolute data.
R packages to which I contributed,
This package performs a differential abundance analysis of
biological count data using log-ratio transformations.
Professional experience
In industry,
Transport Accident Commission: October 2022 to
present
Employed as a Data Scientist.
In research,
Deakin University: March 2021 to October 2022
Employed as an Alfred Deakin Postdoctoral Fellow in
the Applied Artificial Intelligence Institute (A2I2) lab under
Dr. Svetha Venkatesh to study interpretable deep models of RNA
splicing.
Deakin University: October 2019 to March 2021
Employed as an Associate Research Fellow in the
Applied Artificial Intelligence Institute (A2I2) lab under Dr. Svetha
Venkatesh to study XAI for health applications. This role involved close
collaboration with computer science experts to design and implement
neural network architectures that enable the interpretable analysis of
high-dimensional genomic data.
Deakin University: January 2018 to August 2019
Employed as a Casual Research Fellow in the Pattern
Recognition and Data Analysis (PRaDA) lab under Dr. Svetha Venkatesh to
study autism spectrum disorder and cancer genomics. This role involved
pre-processing genomic data, building and deploying classifiers based on
gene expression signatures, and supervising a research assistant.
SUNY Upstate Medical University: August 2012 to May
2016
Employed as a Student Research Assistant in the
Psychiatric Genetic Epidemiology and Neurobiology (PsychGENe) lab under
Dr. Stephen Glatt to study the molecular underpinnings of psychiatric
disorders. This role involved the development of bioinformatic tools for
the analysis of single nucleotide polymorphisms and microarray-based
gene expression data. Extensive time devoted to the implementation
machine learning for the classification of autism spectrum
disorder.
SUNY New Paltz: September 2009 to August 2010
Employed as a Student Research Assistant in an
organic chemistry lab under Dr. Frantz Folmer-Andersen to study
synthetic diamine macrocycles which have the capability to distinguish
between mirror image molecules.
SUNY Purchase: May 2009 to July 2009
Employed as a Student Research Assistant in an
environmental science lab under Dr. Kristopher Baker to study the impact
of anthropogenic input on the biodiversity of the Hudson River
estuary.
In education,
Deakin University: March 2017 to May 2019
Employed as a Graduate Assistant for two courses
titled, Principles of Pharmacology (x3) and Therapeutic
Development (x2).
Coursera: September 2016 to October 2016
Employed as a Graduate Assistant for an online
course titled, Big Data, Genes, and Medicine.
SUNY New Paltz: September 2009 to May 2011
Employed as a Teaching Assistant for an
undergraduate organic chemistry laboratory class.
SUNY Orange: September 2008 to August 2009
Employed as a Tutor in a university math learning
center.
Awards and scholarships
2020 Shortlisted for Alfred Deakin Medal for Doctoral Thesis
2019 People’s Choice Award for Outstanding Talk at Joint GIW/ABACBS
2019
2017 Travel Award to Winter School in Mathematical &
Computational Biology
2016 Travel Award to 2nd Bioconductor Asia-Pacific Developers’
Meeting
2014 Student Research Celebration Presenter Research Project
Award
2010 New Paltz Student Undergraduate Research Experience (SURE)
stipend
2009 Purchase Bridges to Baccalaureate Program stipend
2009 Outgoing Faculty Annual Scholarship awarded by SUNY Orange
2009 Outgoing William F. Ehlers Jr. Memorial Scholarship for
excellence in anthropology awarded by SUNY Orange
2009 Outgoing Dr. Cortland R. Mapes Scholarship for excellence in
biology awarded by SUNY Orange
2008 American Chemical Society award for excellence in chemistry
2007-2009 Recipient of President’s Scholarship for Outstanding
Academic Achievement at SUNY Orange
Presentations
Quinn TP. April 2021. Appropriate statistics and
data transformations for compositional data. Invited talk presented at
Australasian Human Microbiome Research Network Virtual Conference
(online delivery).
Quinn TP. December 2020. Deep learning: The quest
for interpretability. Invited talk presented at Centre for Genomic
Regulation in Barcelona, Spain (online delivery).
Quinn TP. November 2020. Deep learning: The quest
for interpretability. Invited talk presented at WEHI ML Interest Group
Seminar Series in Melbourne, Australia (online delivery).
Quinn TP. November 2020. HyperXPair: Learning
distance-dependent motif interactions. Accepted talk presented at ABACBS
2020 Virtual Conference (online delivery).
Quinn TP. September 2020. Risk Prediction: An
Introduction. Invited talk presented at Victoria University
Bioinformatics Seminar in Melbourne, Australia (online delivery).
Quinn TP. July 2020. DeepCoDA: Personalized
Interpretability for Compositional Health Data. Accepted talk presented
at ICML 2020 in Vienna, Austria (online delivery).
Quinn TP. July 2020. On Compositions and Deep
Learning. Invited talk presented at Monash Bioinformatics Seminar Series
in Melbourne, Australia (online delivery).
Quinn TP. June 2020. DeepCoDA: Personalized
Interpretability for Compositional Health Data. Invited talk presented
at Gen(e)quality “Demystifying ‘omic’ Risk Scores” workshop in Geelong,
Australia (online delivery).
Quinn TP. June 2020. Risk Prediction: An
Introduction. Invited talk presented at Gen(e)quality “Demystifying
‘omic’ Risk Scores” workshop in Geelong, Australia (online
delivery).
Quinn TP. May 2020. The microbiome as compositions:
a methodological survey. Invited talk presented at San Diego State
University in San Diego, California (online delivery).
Quinn TP. December 2019. Deep in the Bowel: Highly
Interpretable Neural Encoder-Decoder Networks Predict Gut Metabolites
from Gut Microbiome. Accepted talk presented at Joint GIW/ABACBS 2019 in
Sydney, Australia.
Quinn TP. December 2019. DeepTRIAGE: Interpretable
and Individualised Biomarker Scores using Attention Mechanism for the
Classification of Breast Cancer Sub-types. Accepted talk presented at
Joint GIW/ABACBS 2019 in Sydney, Australia.
Quinn TP. June 2019. Using balances to engineer
features for the classification of health biomarkers: a new approach to
balance selection. Accepted talk presented at CoDaWork 2019 in Terrassa,
Spain.
Quinn TP. March 2019. A practical guide to
(supervised) machine learning using the exprso R package. Invited talk
presented at Deakin University in Geelong, Victoria.
Quinn TP. October 2018. Understanding sequencing
data as compositions: theory and application. Invited talk presented at
Centre for Genomic Regulation in Barcelona, Spain.
Quinn TP. October 2017. A compositionally valid
pipeline for any-omics data. Accepted talk presented at Australasian
Genomic Technologies Association in Hobart, Tasmania.
Peer-review contributions
Publons
Wellcome Trust
Statistical Methods in Medical Research
PeerJ
American Journal of Medical Genetics B Neuropsychiatric Genetics
Bioinformatics
Applied Computing and Geosciences
NAR Genomics and Bioinformatics
PLoS Computational Biology
BMC Genomics
Science Engineering Ethics
Frontiers in Genetics
Patterns
Lead Author Publications
Quinn TP+, Hess JL+, et al., & Glatt SJ. 2022.
Signal from Noise: Using Machine Learning to Distil Knowledge from Data
in Biological Psychiatry. PsyArXiv. https://psyarxiv.com/dz7gt.
Quinn TP, Jacobs S, Senadeera M, Le V, & Coghlan
S. 2021. The three ghosts of medical AI: Can the black-box present
deliver? Artificial Intelligence in Medicine. https://doi.org/10.1016/j.artmed.2021.102158.
Quinn TP. 2021. Stool Studies Don’t Pass the Sniff
Test: A Systematic Review of Human Gut Microbiome Research Suggests
Widespread Misuse of Machine Learning. arXiv. https://arxiv.org/abs/2107.03611.
Quinn TP, Gordon-Rodriguez E, & Erb I. 2021. A
Critique of Differential Abundance Analysis, and Advocacy for an
Alternative. arXiv. https://arxiv.org/abs/2104.07266.
Quinn TP, Nguyen D, Gupta S, & Venkatesh S.
2021. A Neural Model of RNA Splicing: Learning Motif Distances with
Self-Attention and Toeplitz Max Pooling. bioRxiv 445518. https://doi.org/10.1101/2021.05.24.445518.
Quinn TP & Erb I. 2021. Examining
microbe–metabolite correlations by linear methods. Nature Methods
18:37-39. https://doi.org/10.1038/s41592-020-01006-1.
Quinn TP, Le V, & Cardilini APA. 2021. Test Set
Verification Is An Essential Step in Model Building. Methods in Ecology
and Evolution 12(1). Forum Article. https://doi.org/10.1111/2041-210X.13495.
Harikumar H+, Quinn TP+, Rana S, Gupta S, &
Venkatesh S. 2021. Personalized single-cell networks: a framework to
predict the response of any gene to any drug for any patient. BioData
Mining 14(37). https://doi.org/10.1186/s13040-021-00263-w.
Quinn TP, Senadeera M, Jacobs S, Coghlan S, & Le
V. 2020. Trust and Medical AI: The challenges we face and the expertise
needed to overcome them. JAMIA. Online ahead of print. https://doi.org/10.1093/jamia/ocaa268.
Quinn TP & Erb I. 2020. Amalgams: data-driven
amalgamation for the dimensionality reduction of compositional data. NAR
Genomics and Bioinformatics 2(4). https://doi.org/10.1093/nargab/lqaa076.
Le V+, Quinn TP+, Tran T, & Venkatesh S. 2020.
Deep in the Bowel: Highly Interpretable Neural Encoder-Decoder Networks
Predict Gut Metabolites from Gut Microbiome. BMC Genomics 21(256). https://doi.org/10.1186/s12864-020-6652-7.
Quinn TP, Nguyen D, Nguyen P, Gupta S, &
Venkatesh S. 2020. Learning distance-dependent motif interactions: an
interpretable CNN model of genomic events. bioRxiv 270967. https://doi.org/10.1101/2020.08.27.270967.
Quinn TP+, Nguyen D+, Rana S, Gupta S, &
Venkatesh S. 2020. DeepCoDA: personalized interpretability for
compositional health data. ICML 2020 (A* conference, acceptance rate =
21.8%). https://arxiv.org/abs/2006.01392.
Loughman A+, Quinn TP+, et al., & Tang ML. 2020.
Infant microbiota in colic: predictive associations with problem crying
and subsequent child behavior. Journal of Developmental Origins of
Health and Disease. Online ahead of print. https://doi.org/10.1017/S2040174420000227.
Quinn TP & Erb I. 2020. Interpretable Log
Contrasts for the Classification of Health Biomarkers: a New Approach to
Balance Selection. mSystems 5(2). https://doi.org/10.1128/mSystems.00230-19.
Beykikhoshk A+, Quinn TP+, Lee SC, Tran T, &
Venkatesh S. 2020. DeepTRIAGE: Interpretable and Individualised
Biomarker Scores using Attention Mechanism for the Classification of
Breast Cancer Sub-types. BMC Medical Genomics 13(20). https://doi.org/10.1186/s12920-020-0658-5.
Quinn TP, Erb I, Gloor G, Notredame C, Richardson
MF, & Crowley TM. 2019. A field guide for the compositional analysis
of any-omics data. GigaScience giz107. https://doi.org/10.1093/gigascience/giz107.
Lee SC+, Quinn A, Nguyen T, Venkatesh S, & Quinn
TP+. 2019. A cross-cancer metastasis signature in the
microRNA-mRNA axis of paired tissue samples. Molecular Biology Reports.
https://doi.org/10.1007/s11033-019-05025-w.
Quinn TP, Nguyen T, Lee SC, & Venkatesh S. 2019.
Cancer as a tissue anomaly: classifying tumor transcriptomes based only
on healthy data. Frontiers in Genetics 10:599. https://doi.org/10.3389/fgene.2019.00599.
Quinn TP+, Lee SC+, Venkatesh S, & Nguyen T.
2019. Improving the classification of neuropsychiatric conditions using
gene ontology terms as features. American Journal of Medical Genetics B
Neuropsychiatric Genetics 180(7):508-18. https://doi.org/10.1002/ajmg.b.32727.
Lee SC+, Quinn TP+, et al., & Nguyen T. 2018.
Solving for X: evidence for sex-specific autism biomarkers across
multiple transcriptomic studies. American Journal of Medical Genetics B
Neuropsychiatric Genetics 180(6):377-89. https://doi.org/10.1002/ajmg.b.32701.
Quinn TP. 2018. Visualizing Balances of
Compositional Data: A New Alternative to Balance Dendrograms [version 1;
referees: 2 approved]. F1000Research 7:1278. https://doi.org/10.12688/f1000research.15858.1.
Quinn TP, Crowley TM, & Richardson MF. 2018.
Benchmarking differential expression analysis tools for RNA-Seq:
normalization-based vs. log-ratio transformation-based methods. BMC
Bioinformatics 19(1). https://doi.org/10.1186/s12859-018-2261-8.
Quinn TP, Erb I, Richardson MF, & Crowley TM.
2018. Understanding sequencing data as compositions: an outlook and
review. Bioinformatics 34(16). https://doi.org/10.1093/bioinformatics/bty175.
Quinn T, Richardson MF, Lovell D, & Crowley T.
2017. propr: An R-package for Identifying Proportionally Abundant
Features Using Compositional Data Analysis. Scientific Reports 7:16252.
https://dx.doi.org/10.1038/s41598-017-16520-0.
Quinn T, Tylee D, & Glatt S. 2017. exprso: an
R-package for the rapid implementation of machine learning algorithms
[version 2; referees: 2 approved]. F1000Research 5:2588. https://dx.doi.org/10.12688/f1000research.9893.2.
Quinn TP, Atwood PD, Tanski JM, Moore TF, &
Folmer-Andersen J. 2011. Aza-crown macrocycles as chiral solvating
agents for mandelic acid derivatives. Journal of Organic Chemistry
76(24):10020-30. https://dx.doi.org/10.1021/jo2018203.
Other Publications
Greenacre M, Grunsky E, Bacon-Shone J, Erb I, & Quinn
T. 2022. Aitchison’s Compositional Data Analysis 40 Years On: A
Reappraisal. arXiv. https://arxiv.org/abs/2201.05197.
Gordon-Rodriguez E, Quinn TP, & Cunningham JP.
2022. Data Augmentation for Compositional Data: Advancing Predictive
Models of the Microbiome. arXiv. https://arxiv.org/abs/2205.09906.
Nguyen TM, Quinn TP, Nguyen T, & Tran T. 2022.
Explaining Black Box Drug Target Prediction through Model Agnostic
Counterfactual Samples. IEEE/ACM Transactions on Computational Biology
and Bioinformatics. https://doi.org/10.1109/TCBB.2022.3190266.
Gordon-Rodriguez E, Quinn TP, & Cunningham JP.
2021. Learning sparse log-ratios for high-throughput sequencing data.
Bioinformatics. https://doi.org/10.1093/bioinformatics/btab645.
Aslam H, et al., Quinn TP, et al., & Loughman A.
2021. Gut Microbiome Diversity and Composition Are Associated with
Habitual Dairy Intakes: A Cross-Sectional Study in Men. The Journal of
Nutrition. https://doi.org/10.1093/jn/nxab252.
Gao Y, et al., Quinn TP, et al., & Vuillermin P.
2021. The maternal gut microbiome during pregnancy and offspring allergy
and asthma. Journal of Allergy and Clinical Immunology. Online ahead of
print. https://doi.org/10.1016/j.jaci.2021.07.011.
Nguyen T, Lee SC, Quinn TP, et al., & Le TD.
2021. PAN: Personalized Annotation-based Networks for the Prediction of
Breast Cancer Relapse. IEEE/ACM Transactions on Computational Biology
and Bioinformatics. https://doi.org/10.1109/TCBB.2021.3076422.
Erb I, Gloor GB, & Quinn TP. 2020. Editorial:
Compositional data analysis and related methods applied to genomics—a
first special issue from NAR Genomics and Bioinformatics. NAR Genomics
and Bioinformatics 2(4). https://doi.org/10.1093/nargab/lqaa103.
Nguyen T, Le H, Quinn TP, Le TD, & Venkatesh S.
2020. GraphDTA: Predicting drug–target binding affinity with graph
neural networks. Bioinformatics. Online ahead of print. https://doi.org/10.1093/bioinformatics/btaa921.
Hess JL, Chen S, Quinn TP, et al., & Glatt SJ.
2020. BrainGENIE: The Brain Gene Expression and Network Imputation
Engine. bioRxiv 356766. https://doi.org/10.1101/2020.10.27.356766.
Bruxel EM, Moreira-Maia CR, Akutagava-Martins GC, Quinn
TP, et al., & Hutz MH. 2020. Meta-analysis and systematic
review of ADGRL3 (LPHN3) polymorphisms in ADHD susceptibility. Molecular
Psychiatry (2020). https://doi.org/10.1038/s41380-020-0673-0.
Allnutt TR, Wade B, Quinn TP, Richardson MF, &
Crowley TM. 2018. Shortlisting Aptamer Candidates from HT-SELEX data.
Aptamers 2. https://japtamers.co.uk/shortlisting-aptamer-candidates-from-ht-selex-data.
Erb I, Quinn T, Lovell D, & Notredame C. 2017.
Differential Proportionality - A Normalization-Free Approach To
Differential Gene Expression. Proceedings of CoDaWork 2017, The 7th
Compositional Data Analysis Workshop; available under bioRxiv 134536. https://dx.doi.org/10.1101/134536.
Tylee DS, Kikinis Z, Quinn TP, et al., & Makris
N. 2017. Machine-learning classification of 22q11.2 deletion syndrome: A
diffusion tensor imaging study. NeuroImage: Clinical 15(1):832-42. https://dx.doi.org/10.1016/j.nicl.2017.04.029.
Englebert C, Quinn T, & Bichindaritz I. 2017.
Feature selection for survival analysis in bioinformatics. CEUR Workshop
Proceedings 1942:30-35. https://ceur-ws.org/Vol-1942/paper5.pdf.
Tylee DS, Hess JL, Quinn TP, et al., & Glatt SJ.
2016. Blood transcriptomic comparison of individuals with and without
autism spectrum disorder: A combined-samples mega-analysis. American
Journal of Medical Genetics B Neuropsychiatric Genetics 174(3):181-201.
https://dx.doi.org/10.1002/ajmg.b.32511.
Bichindaritz IB, Cole E, et al., & Quinn TP.
2016. Machine Learning Based Automatic Multilevel Stress Detection from
ECG Signals. IJCAI 2016: Workshop on Knowledge Discovery in Healthcare
Data. https://sites.google.com/site/ijcai2016kdhealth/accepted-papers.
Hess JL, Quinn TP, Akbarian S, & Glatt SJ. 2015.
Bioinformatic analyses and conceptual synthesis of evidence linking
ZNF804A to risk for schizophrenia and bipolar disorder. American Journal
of Medical Genetics B Neuropsychiatric Genetics 168(1):14-35. https://dx.doi.org/10.1002/ajmg.b.32284.
Funding
Project: “An Interpretable Deep Learning Model of RNA Splicing”.
Year: 2021. Provisioner: Alfred Deakin Postdoctoral Fellowship. Award:
$203,994. Role: Awardee. Contribution: Project lead.
Project: “The Pregnancy Research and Translation Ecosystem (PRT-E)”.
Year: 2021. Provisioner: Western Alliance Flagship Research Program.
Award: $200,000. Role: CI. Contribution: Design of data collection and
clinical translation methodology.
Project: “Optimising treatments in mental health using AI”. Year:
2020. Provisioner: MRFF Applied Artificial Intelligence Research in
Health Fund. Award: $4,995,434. Role: CI. Contribution: Design and
implementation of trial analysis protocol.
Project: “How does the gut microbiome contribute to individual
responses to psychobiotics?”. Year: 2020. Provisioner: Deakin IMPACT
Seed Fund. Award: $15,000. Role: AI. Contribution: Design and
implementation of statistical and machine learning analyses.