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
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.
Statistical Methods in Medical Research
American Journal of Medical Genetics B Neuropsychiatric Genetics
Applied Computing and Geosciences
NAR Genomics and Bioinformatics
PLoS Computational Biology
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). http://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. http://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. http://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). http://doi.org/10.1038/s41380-020-0673-0.
Nguyen T, Lee SC, Quinn TP, et al., & Le TD. 2019. Personalized Annotation-based Networks (PAN) for the Prediction of Breast Cancer Relapse. bioRxiv 534628. http://doi.org/10.1101/534628.
Allnutt TR, Wade B, Quinn TP, Richardson MF, & Crowley TM. 2018. Shortlisting Aptamer Candidates from HT-SELEX data. Aptamers 2. http://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. http://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. http://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. http://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. http://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. http://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. http://dx.doi.org/10.1002/ajmg.b.32284.
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.