Publications

@article{miller2025unraveling,

title = {Unraveling ADHD: genes, co-occurring traits, and developmental dynamics},

author = {Catriona J Miller and Evgenija Golovina and Sreemol Gokuladhas and J\"{o}rg Wicker and Jessie C Jacobson and Justin M O\'Sullivan},

doi = {10.26508/lsa.202403029},

year  = {2025},

date = {2025-02-25},

journal = {Life Science Alliance},

volume = {8},

number = {5},

abstract = {Attention-deficit/hyperactivity disorder (ADHD) is a heterogeneous neurodevelopmental condition with a high prevalence of co-occurring conditions, contributing to increased difficulty in long-term management. Genome-wide association studies have identified variants shared between ADHD and co-occurring psychiatric disorders; however, the genetic mechanisms are not fully understood. We integrated gene expression and spatial organization data into a two-sample Mendelian randomization study for putatively causal ADHD genes in fetal and adult cortical tissues. We identified four genes putatively causal for ADHD in cortical tissues (fetal: ST3GAL3, PTPRF, PIDD1; adult: ST3GAL3, TIE1). Protein{textendash}protein interaction databases seeded with the causal ADHD genes identified biological pathways linking these genes with conditions (e.g., rheumatoid arthritis) and biomarkers (e.g., lymphocyte counts) known to be associated with ADHD, but without previously shown genetic relationships. The analysis was repeated on adult liver tissue, where putatively causal ADHD gene ST3GAL3 was linked to cholesterol traits. This analysis provides insight into the tissue-dependent temporal relationships between ADHD, co-occurring traits, and biomarkers. Importantly, it delivers evidence for the genetic interplay between co-occurring conditions, both previously studied and unstudied, with ADHD.The multimorbid3D pipeline was created and run in Python (version 3.8.8). All visualizations and data analysis were performed in R (version 4.2.0) through RStudio (version 2022.02.2). Table S16 lists the datasets and software that have been used in our analyses. All scripts are available on GitHub (https://github.com/Catriona-Miller/ADHD_Co-occurring_Traits).Table S16. Software and datasets used for this analysis.Ethics statementEthics approval was obtained from the University of Auckland Human Participants Ethics Committee (Decoding SNPs in context, UAHPEC19373).},

keywords = {bioinformatics, Biological Sciences, biomarkers, computational sustainability, machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{bensemann2023from,

title = {From What You See to What We Smell: Linking Human Emotions to Bio-markers in Breath},

author = {Joshua Bensemann and Hasnain Cheena and David Tse Jung Huang and Elizabeth Broadbent and Jonathan Williams and J\"{o}rg Wicker},

url = {https://ieeexplore.ieee.org/document/10123109

https://doi.org/10.17608/k6.auckland.22777364 

https://doi.org/10.17608/k6.auckland.22777352 },

doi = {10.1109/TAFFC.2023.3275216},

issn = {1949-3045},

year  = {2023},

date = {2023-05-11},

urldate = {2023-05-11},

journal = {IEEE Transactions on Affective Computing},

pages = {1-13},

abstract = {Research has shown that the composition of breath can differ based on the human’s behavioral patterns and mental and physical states immediately before being collected. These breath-collection techniques have also been extended to observe the general processes occurring in groups of humans and can link them to what those groups are collectively experiencing. In this research, we applied machine learning techniques to the breath data collected from cinema audiences. These techniques included XGBOOST Regression, Hierarchical Clustering, and Item Basket analyses created using the Apriori algorithm. They were conducted to find associations between the biomarkers in the crowd’s breath and the movie’s audio-visual stimuli and thematic events. This analysis enabled us to directly link what the group was experiencing and their biological response to that experience. We first extracted visual and auditory features from a movie to achieve this. We compared it to the biomarkers in the crowd’s breath using regression and pattern mining techniques. Our results supported the theory that a crowd’s collective experience directly correlates to the biomarkers in the crowd’s breath. Consequently, these findings suggest that visual and auditory experiences have predictable effects on the human

body that can be monitored without requiring expensive or invasive neuroimaging techniques.},

keywords = {biomarkers, breath analysis, cheminformatics, cinema data mining, emotional response analysis, machine learning, smell of fear},

pubstate = {published},

tppubtype = {article}

}