| Dynamic Bayesian network learning to infer sparse models from time series gene expression data HB Ajmal, MG Madden IEEE/ACM transactions on computational biology and bioinformatics 19 (5 …, 2021 | 29 | 2021 |
| Agent-based modelling of mpox infection dynamics: simulating disease transmission and control strategies H Ajmal, E Hunter, J Duggan, C Timoney, C Walsh Journal of Artificial Societies and Social Simulation 27 (2), 2024 | 8 | 2024 |
| A Review of Bayesian Modelling Methods for Gene Regulatory Network Inference [J] HB Ajmal, MG Madden Bioinformatics 1, 2023 | 3 | 2023 |
| Dealing with stochasticity in biological ODE models H Ajmal, M Madden, C Enright arXiv preprint arXiv:1910.04909, 2019 | 3 | 2019 |
| Inferring dynamic gene regulatory networks with low-order conditional independencies–an evaluation of the method HB Ajmal, MG Madden Statistical Applications in Genetics and Molecular Biology 19 (4-6), 20200051, 2020 | 2 | 2020 |
| PROFET: Construction and inference of DBNs based on mathematical models H Ajmal, M Madden, C Enright arXiv preprint arXiv:1910.04895, 2019 | 2 | 2019 |
| Benchmarking genetic interaction scoring methods for identifying synthetic lethality from combinatorial CRISPR screens H Ajmal, S Nandi, N Kebabci, CJ Ryan NAR Genomics and Bioinformatics 7 (3), lqaf129, 2025 | 1 | 2025 |
| Using crisis text messaging service data to measure the impact of the COVID-19 Pandemic on mental health in Ireland H Ajmal, R Melia, K Young, J Bogue, H Wood, M O'Sullivan, J Duggan Behaviour & Information Technology 44 (10), 2226-2243, 2025 | 1 | 2025 |
| Dynamic bayesian modelling of biological networks H Ajmal NUI Galway, 2021 | 1 | 2021 |
| A predicted cancer dependency map for paralog pairs N Kebabci, H Ajmal, DA Adams, CJ Ryan bioRxiv, 2026.01. 19.700065, 2026 | | 2026 |
| PROFET: Construction and Inference of DBNs Based on Mathematical Models H bint e Ajmal, M Madden, C Enright | | |
| Dealing with Stochasticity in Biological ODE Models H bint e Ajmal, M Madden, C Enright | | |