GT-Scan: identifying unique genomic targets A O'Brien, TL Bailey Bioinformatics, 2673-2675, 2014 | 161 | 2014 |
The current state and future of CRISPR-Cas9 gRNA design tools LOW Wilson, AR O’Brien, DC Bauer Frontiers in pharmacology 9, 749, 2018 | 136 | 2018 |
Reproducibility of CRISPR-Cas9 methods for generation of conditional mouse alleles: a multi-center evaluation CB Gurumurthy, AR O’brien, RM Quadros, J Adams, P Alcaide, S Ayabe, ... Genome biology 20 (1), 1-14, 2019 | 89 | 2019 |
Artificial intelligence and machine learning in bioinformatics K Lai, N Twine, A O’brien, Y Guo, D Bauer Encyclopedia of Bioinformatics and Computational Biology: ABC of …, 2018 | 51 | 2018 |
High activity target-site identification using phenotypic independent CRISPR-Cas9 core functionality LOW Wilson, D Reti, AR O'Brien, RA Dunne, DC Bauer The CRISPR Journal 1 (2), 182-190, 2018 | 51 | 2018 |
Unlocking HDR-mediated nucleotide editing by identifying high-efficiency target sites using machine learning AR o’Brien, LOW Wilson, G Burgio, DC Bauer Scientific reports 9 (1), 2788, 2019 | 43 | 2019 |
VariantSpark: population scale clustering of genotype information AR O’Brien, NFW Saunders, Y Guo, FA Buske, RJ Scott, DC Bauer BMC genomics 16 (1), 1-9, 2015 | 36 | 2015 |
Mutation analysis of MATR3 in Australian familial amyotrophic lateral sclerosis JA Fifita, KL Williams, EP McCann, A O'Brien, DC Bauer, GA Nicholson, ... Neurobiology of aging 36 (3), 1602.e1-1602.e2, 2015 | 20 | 2015 |
Domain-specific introduction to machine learning terminology, pitfalls and opportunities in CRISPR-based gene editing AR O’Brien, G Burgio, DC Bauer Briefings in bioinformatics 22 (1), 308-314, 2021 | 17 | 2021 |
VariantSpark: Cloud-based machine learning for association study of complex phenotype and large-scale genomic data A Bayat, P Szul, AR O’Brien, R Dunne, B Hosking, Y Jain, C Hosking, ... GigaScience 9 (8), giaa077, 2020 | 14 | 2020 |
The current state and future of CRISPR-Cas9 gRNA design tools. Front Pharmacol 9: 749 LOW Wilson, AR O’Brien, DC Bauer | 6 | 2018 |
Response to correspondence on “Reproducibility of CRISPR-Cas9 methods for generation of conditional mouse alleles: a multi-center evaluation” CB Gurumurthy, AR O’Brien, RM Quadros, J Adams, P Alcaide, S Ayabe, ... Genome biology 22, 1-4, 2021 | 3 | 2021 |
VariantSpark, A Random Forest Machine Learning Implementation for Ultra High Dimensional Data A Bayat, P Szul, AR O’Brien, R Dunne, OJ Luo, Y Jain, B Hosking, ... bioRxiv, 702902, 2019 | 3 | 2019 |
Breaking the curse of dimensionality for machine learning on genomic data A O’Brien, P Szul, O Luo, A George, R Dunne, D Bauer IJCAI 2017, 2017 | 3 | 2017 |
Predicting CRISPR-Cas12a guide efficiency for targeting using machine learning A O’Brien, DC Bauer, G Burgio Plos one 18 (10), e0292924, 2023 | 2 | 2023 |
GOANA: A Universal High-Throughput Web Service for Assessing and Comparing the Outcome and Efficiency of Genome Editing Experiments D Reti, A O'Brien, P Wetzel, A Tay, DC Bauer, LOW Wilson The CRISPR Journal 4 (2), 243-252, 2021 | 2 | 2021 |
Generalisable Methods for Improving CRISPR Efficiency and Outcome Specificity using Machine Learning Algorithms AR O'Brien PQDT-Global, 2020 | 1 | 2020 |
Large-scale multi-omic analysis identifies noncoding somatic driver mutations and nominates ZFP36L2 as a driver gene for pancreatic ductal adenocarcinoma J Zhong, A O’Brien, M Patel, D Eiser, M Mobaraki, I Collins, L Wang, ... medRxiv, 2024 | | 2024 |
Allelic effects on KLHL17 expression likely mediated by JunB/D underlie a PDAC GWAS signal at chr1p36. 33 KE Connelly, K Hullin, E Abdolalizadeh, J Zhong, D Eiser, A O’Brien, ... medRxiv, 2024 | | 2024 |
Abstract C055: Unraveling pancreatic cancer susceptibility at 5p15. 33: Functional characterization of a novel VNTR element A O'Brien, H Kong, M Patel, KE Connelly, M Xu, I Collins, J Zhong, ... Cancer Research 84 (17_Supplement_2), C055-C055, 2024 | | 2024 |