GOexpress: an R/Bioconductor package for the identification and visualisation of robust gene ontology signatures through supervised learning of gene expression data. - Kevin C. Rue-Albrecht; Paul A. McGettigan; Belinda Hernández; Nicolas C. Nalpas; David A. Magee; Andrew C. Parnell; Stephen V. Gordon; David E. MacHugh (BMC Bioinform., 2016)

An Evaluation of Methods for Real-Time Anomaly Detection using Force Measurements from the Turning Process. - Yuanzhi Huang; Eamonn Ahearne; Szymon Baron; Andrew Parnell (CoRR, 2018)

Contrasting prediction methods for early warning systems at undergraduate level. - Emma Howard; Maria Meehan; Andrew Parnell (Internet High. Educ., 2018)

Bayesian Additive Regression Trees using Bayesian model averaging. - Belinda Hernández; Adrian E. Raftery; Stephen R Pennington; Andrew C. Parnell (Stat. Comput., 2018)

Prediction of tool-wear in turning of medical grade cobalt chromium molybdenum alloy (ASTM F75) using non-parametric Bayesian models. - Damien McParland; Szymon Baron; Sarah O'Rourke; Denis Dowling; Eamonn Ahearne; Andrew Parnell (J. Intell. Manuf., 2019)

A Machine Learning Approach for Lamb Meat Quality Assessment Using FTIR Spectra. - Rocío Alaíz-Rodríguez; Andrew C. Parnell (IEEE Access, 2020)

Generalizing Gain Penalization for Feature Selection in Tree-Based Models. - Bruna D. Wundervald; Andrew C. Parnell; Katarina Domijan (IEEE Access, 2020)

Machine Learning Techniques for the Detection of Inappropriate Erotic Content in Text. - Gonzalo Molpeceres Barrientos; Rocío Alaíz-Rodríguez; Víctor González-Castro; Andrew C. Parnell (Int. J. Comput. Intell. Syst., 2020)

An information theoretic approach to quantify the stability of feature selection and ranking algorithms. - Rocío Alaíz-Rodríguez; Andrew C. Parnell (Knowl. Based Syst., 2020)

Bayesian Optimisation for Sequential Experimental Design with Applications in Additive Manufacturing. - Mimi Zhang; Andrew Parnell; Dermot Brabazon; Alessio Benavoli (CoRR, 2021)

Semi-parametric Bayesian Additive Regression Trees. - Estevão B. Prado; Andrew C. Parnell; Nathan McJames; Ann O'Shea; Rafael A. Moral (CoRR, 2021)

Estimating the conditional probability of developing human papilloma virus related oropharyngeal cancer by combining machine learning and inverse Bayesian modelling. - Prerna Tewari; Eugene Kashdan; Cathal Walsh; Cara M. Martin; Andrew C. Parnell; John J. O'Leary (PLoS Comput. Biol., 2021)

Bayesian additive regression trees with model trees. - Estevão B. Prado; Rafael A. Moral; Andrew C. Parnell (Stat. Comput., 2021)

Hierarchical Embedded Bayesian Additive Regression Trees. - Bruna D. Wundervald; Andrew C. Parnell; Katarina Domijan (CoRR, 2022)

Variational Inference for Additive Main and Multiplicative Interaction Effects Models. - Antônia A. L. Dos Santos; Rafael A. Moral; Danilo A. Sarti; Andrew C. Parnell (CoRR, 2022)

NEAL: An open-source tool for audio annotation. - Anthony Gibbons; Ian Donohue; Courtney E. Gorman; Emma King; Andrew Parnell (CoRR, 2022)

Visualizing Variable Importance and Variable Interaction Effects in Machine Learning Models. - Alan Inglis; Andrew Parnell; Catherine B. Hurley (J. Comput. Graph. Stat., 2022)

Output Feedback Reinforcement Learning for Temperature Control in a Fused Deposition Modelling Additive Manufacturing System. - Eleni Zavrakli; Andrew Parnell; Subhrakanti Dey (CoDIT, 2023)

Bayesian Additive Main Effects and Multiplicative Interaction Models using Tensor Regression for Multi-environmental Trials. - Antônia A. L. Dos Santos; Danilo A. Sarti; Rafael A. Moral; Andrew C. Parnell (CoRR, 2023)

Bayesian Causal Forests for Multivariate Outcomes: Application to Irish Data From an International Large Scale Education Assessment. - Nathan McJames; Andrew C. Parnell; Yong Chen Goh; Ann O'Shea (CoRR, 2023)

SERT: A Transfomer Based Model for Spatio-Temporal Sensor Data with Missing Values for Environmental Monitoring. - Amin Shoari Nejad; Rocío Alaíz-Rodríguez; Gerard D. McCarthy; Brian Kelleher; Anthony Grey; Andrew C. Parnell (CoRR, 2023)

Review of Clustering Methods for Functional Data. - Mimi Zhang; Andrew Parnell (ACM Trans. Knowl. Discov. Data, 2023)

vivid: An R package for Variable Importance and Variable Interactions Displays for Machine Learning Models. - Alan Inglis; Andrew Parnell; Catherine Hurley (R J., 2023)

Machine Learning Applied to the Detection of Mycotoxin in Food: A Review. - Alan Inglis; Andrew Parnell; Natarajan Subramani; Fiona Doohan (CoRR, 2024)

Bayesian Causal Forests for Longitudinal Data: Assessing the Impact of Part-Time Work on Growth in High School Mathematics Achievement. - Nathan McJames; Ann O'Shea; Andrew Parnell (CoRR, 2024)

Generative AI-based data augmentation for improved bioacoustic classification in noisy environments. - Anthony Gibbons; Emma King; Ian Donohue; Andrew Parnell (CoRR, 2024)

SERT: A transformer based model for multivariate temporal sensor data with missing values for environmental monitoring. - Amin Shoari Nejad; Rocío Alaíz-Rodríguez; Gerard D. McCarthy; Brian Kelleher; Anthony Grey; Andrew Parnell (Comput. Geosci., 2024)

GP-BART: A novel Bayesian additive regression trees approach using Gaussian processes. - Mateus Maia; Keefe Murphy; Andrew C. Parnell (Comput. Stat. Data Anal., 2024)

Visualisations for Bayesian Additive Regression Trees. - Alan Inglis; Andrew Parnell; Catherine Hurley (J. Data Sci. Stat. Vis., 2024)

ExActR: A Shiny app for creating ecosystem extent accounts. - Anthony Gibbons; Francesco Martini; Cian White; Emma King; Jane C. Stout; Ian Donohue; Andrew Parnell (Ecol. Informatics, 2025)

Permutation-based visualisation of input and encoded space in autoencoders. - Alan Inglis; Andrew Parnell (J. Data Sci. Stat. Vis., 2025)

reslr: An R Package for Relative Sea Level Modelling. - Maeve Upton; Andrew Parnell; Niamh Cahill (R J., 2025)

Predicting BVD Re-emergence in Irish Cattle From Highly Imbalanced Herd-Level Data Using Machine Learning Algorithms. - Niamh Mimnagh; Andrew Parnell; Conor McAloon; Jaden Carlson; Maria Guelbenzu; Jonas Brock; Damien Barrett; Guy McGrath; Jamie Tratalos; Rafael A. Moral (CoRR, 2025)