Orla M. Hardiman

The paper identifies seven sex-dependent differentially methylated positions in ALS blood samples, suggesting sex-specific biological mechanisms in the disease.

The paper identifies shared and distinct genetic loci linking ALS, sFTD, and cognitive traits using genome-wide association data.

This study identifies rare neurological and neurodevelopmental variants in ALS patients, showing associations with onset, survival, and family history patterns across two cohorts.

This study uses high-density electrospinography to examine frequency-specific spinal activity and cortico-spinal connectivity during pincer-grip, revealing distinct spatial patterns and beta-band activation.

This study shows that low-level muscle activity below standard rejection thresholds increases the likelihood of evoking motor evoked potentials in healthy individuals and those with ALS.

The paper introduces a high-density electrode system for recording spinal cord neural activity, demonstrating its ability to capture evoked potentials and validate its design through topographic mapping.

This paper develops a deep learning model using SNP microarray data to predict ALS risk with high accuracy across diverse populations and genotyping platforms.

The paper investigates oligogenic risk in ALS using genome sequencing data, finding that multiple rare variants in known ALS genes increase disease risk but not clinical outcomes.

The paper uses genetic data to infer demographic history in Ireland and Britain, finding population bottlenecks, migration patterns, and surname enrichments in specific communities.

This paper introduces a method to mine past discoveries from the biomedical literature by detecting surges in relation prevalence over time, providing a benchmark dataset for Literature-Based Discovery evaluation.

The paper introduces a method to compress genetic data using autoencoders that leverage linkage disequilibrium structure, achieving high reconstruction accuracy while reducing data size.

This study analyzes genetic and clinical data from PLS patients to assess the role of genetic testing and identify potential disease-associated variants.

The paper analyzes CAV1 and CAV2 expression in ALS brain tissue and finds higher expression in ALS patients, associated with increased survival and slower disease progression.

This study identifies NEFH tail domain mutations as risk factors for ALS through meta-analysis and variant burden analysis in case-control studies and whole-genome sequencing data.

This study identifies 690 candidate ALS genes using a hierarchical Bayesian model, highlighting genetic convergence on the distal axon and functional significance in motor neurons.

This paper introduces a contrastive approach to Literature-Based Discovery (LBD) to improve relation identification between concepts in biomedical literature, demonstrating its effectiveness through case studies on neurodegenerative diseases.

This paper introduces non-parametric rank statistics for robust spectral power and coherence analysis in neural signals, showing improved robustness against artefacts and enabling new testing scenarios.

This study uses a crowdsourcing approach to stratify ALS patients, identifying four distinct clusters with unique predictors and improving outcome prediction.

This paper uses haplotype-based methods to reveal genetic structure in Ireland, showing regional diversity and historical migrations, with implications for genetic association studies.

The paper introduces ExpansionHunter, a tool for detecting long repeat expansions from PCR-free WGS data, achieving high sensitivity and specificity in identifying pathogenic expansions.

EyeBallGUI provides a flexible GUI for visual inspection and binary marking of multi-channel bio-signals to improve artefact rejection and data processing.