Dr Catherine Mooney

Lecturer/Assistant Professor

 email:  catherine.mooney@ucd.ie
 Phone: +353 1 716 2917
 Room: A1.10
 Computer Science Building
 Belfield 
 Dublin 4

 Full Profile

Data Mining and Machine Learning

Introduction to Software Engineering

 

Machine Learning, Computational Biology, Bioinformatics

Peer Reviewed Journal Publications  

[1] A. Antoniadi, M. Galvin, M. Heverin, O. Hardiman, C. Mooney The prediction of caregiver burden in ALS: A machine learning approach using Random Forests. BMJ Open, 2020.

[2] C. Mooney, P. McKiernan, R. Raoof, D. Henshall, B. Linnane, PG. McNally, A. Glasgow, C. Greene Plasma microRNA levels in paediatric males versus females with cystic fibrosis. Scientific Reports, 10(1):1-8, 2020. 

[3] K. O’Brien, C. Mooney, C. Lopez, G. Pollastri, DC. Shields Prediction of polyproline II secondary structure propensity in proteins. Royal Society Open Science, 7(1):191239, 2020.

[4] C. Mazoa, C. Kearns, C. Mooney*, W. Gallagher* Clinical Decision Support Systems in Breast Cancer: A Systematic Review. Cancers, 12(2):369, 2020. *Joint senior author. 

[5] A. Batool, T. Hill, N. Nguyen, E. Langa, M. Diviney, C. Mooney, G. Brennan, N. Connolly, A. Rodriguez, B. Cavanagh, D. Henshall Altered biogenesis and microRNA content of hippocampal exosomes following experimental status epilepticus. Frontiers in Neuroscience, 13:1404, 2019.

[6] C. Mazoa, S. Barron, C. Mooney, W. Gallagher Multi-Gene Prognostic Signatures and Prediction of Pathological Complete Response of ER-Positive HER2-Negative Breast Cancer Patients to Neo-Adjuvant Chemotherapy. Annals of Oncology 30(Supplement 5):257p, 2019.

[7] C. Reschke, et al. Potent and lasting seizure suppression by systemic delivery of antagomirs targeting miR-134 timed with blood-brain barrier disruption. bioRxiv, 2019. 

[8] M. Kaleel, M. Torrisi, C. Mooney, G. Pollastri PaleAle 5.0: Prediction of protein relative solvent accessibility by Deep Learning. Amino Acids, 51(9):1289-1296, 2019. 

[9] G. Brennan,D.M. Vitsios,A. Looney, B. Hallberg, D. Henshall, G.B. Boylan, D.M. Murray, C. Mooney. RNA-sequencing analysis of umbilical cord plasma microRNAs from healthy newborns. PloS ONE, 13(12):e0207952, 2018. 

[10] R. Raoof, S. Bauer, H. El Naggar, N. Connolly, G. Brennan, E. Brindley, T. Hill, H. McArdle, E. Spain, R. Forster, J. Prehn, H. Hamer, N. Delanty, F. Rosenow, C. Mooney*, D. Henshall*. Dual-center, dual-platform microRNA profiling identifies plasma biomarkers of adult temporal lobe epilepsy. EBioMedicine, 38:127-141, 2018. *Joint senior author. 

[11] T. Hassan, C. Santi, C. Mooney, N.G. McElvaney, C.M. Greene. Alpha-1 antitrypsin augmentation therapy decreases miR-199a-5p, miR-598 and miR-320a expression in monocytes via inhibition of NFkB. Scientific Reports, 7:13803, 2017.  

[12] R. Raoof, E.M. Jimenez-Mateos, S. Bauer, B. Tackenberg, F. Rosenow, J. Lang, M. Dogan, H. Hamer, T. Huchtemann, P. Kortvelyessy, M. Farrell, D.F. O’Brien, D. Henshall, C. Mooney. Cerebrospinal fluid microRNAs are potential biomarkers of temporal lobe epilepsy and status epilepticus. Scientific Reports, 7:3328, 2017.  

[13] C.M. Mooney, E.M. Jimenez-Mateos, T. Engel, C. Mooney, M. Diviney, M. Venø, J. Kjems, M. Farrell, D.F. O’Brien, N. Delanty, et al. RNA sequencing of synaptic and cytoplasmic Upf1-bound transcripts supports contribution of nonsense-mediated decay to epileptogenesis. Scientific Reports, 7:41517, 2017. 

[14] P. Bielefeld, C. Mooney, D. Henshall, C. Fitzsimons. miRNA-Mediated Regulation of Adult Hippocampal Neurogenesis. Brain Plasticity, 3(1):43-59, 2017.

[15] C. Mooney, B. Becker, R. Raoof, and D.C. Henshall. EpimiRBase: a comprehensive database of microRNA-epilepsy associations. Bioinformatics, 32(9):1436-1438, 2016. 

[16] B. Becker, G. Glanville, R. Iwashima, C. McDonnell, K. Goslin, C. Mooney. Effective compiler error message enhancement for novice programming students. Computer Science Education, 1-28, 2016. 

[17] K. Hennigan, P.J. Conroy, M.T. Walsh, M. Amin, R. O’Kennedy, P. Ramasamy, G. Gleich, Z. Siddiqui, S. Glynn, O. McCabe, C. Mooney, B. Harvey, R. Costello, J. McBryan. Eosinophil peroxidase activates cells by HER2 receptor engagement and β1-integrin clustering with downstream MAPK cell signaling. Clinical Immunology, 171:1-11, 2016. 

[18] C. Mooney, R. Raoof, H. El-Naggar, A. Sanz-Rodriguez, E.M. Jimenez-Mateos, and D.C. Henshall. High Throughput qPCR Expression Profiling of Circulating MicroRNAs Reveals Minimal Sex-and Sample Timing-Related Variation in Plasma of Healthy Volunteers. PloS ONE, 10(12):e0145316, 2015. Impact factor: 2.776 (2018).  

[19] E.M. Jimenez-Mateos, M. Arribas-Blazquez, A. Sanz-Rodriguez, C. Concannon, L.A. Olivos-Ore, C.R. Reschke, C.M. Mooney, C. Mooney, E. Lugara, J. Morgan, et al. microRNA targeting of the P2X7 purinoceptor opposes a contralateral epileptogenic focus in the hippocampus. Scientific Reports, 5, 2015. . 

[20] A. Bianchin, A. Bell, A.J. Chubb, N. Doolan, D. Leneghan, I. Stavropoulos, D.C. Shields, and C. Mooney. Design and Evaluation of Antimalarial Peptides Derived from Prediction of Short Linear Motifs in Proteins Related to Erythrocyte Invasion. PloS ONE, 10(6):e0127383, 2015.

[21] C. Sheedy, C. Mooney, E. Jimenez-Mateos, A. Sanz-Rodriguez, E. Langa, C. Mooney, and T. Engel. De-repression of myelin-regulating gene expression after status epilepticus in mice lacking the C/EBP homologous protein CHOP. International Journal of Physiology, Pathophysiology and Pharmacology, 6(4):185, 2014.  

[22] A.B. Nongonierma, C. Mooney, D.C. Shields, and R.J. FitzGerald. In silico approaches to predict the potential of milk protein-derived peptides as dipeptidyl peptidase IV (DPP-IV) inhibitors. Peptides, 57:43–51, 2014. 

[23] M. Kruer, M. Salih, H. Azzedine, C. Mooney, S. Elmalik, M. Kabiraj, A. Khan, and F. Alkuraya. C19orf12 mutation leads to a pallido-pyramidal syndrome. Gene, 537(2):352–356, 2014. 

[24] M. Kruer, T. Jepperson, S. Dutta, R. Steiner, L. Sanford, M. Merkens, B. Russman, P. Blasco, G. Fan, J. Pollock, S. Stanfield, R. Woltjer, C. Mooney, D. Kretzschmar, C. Paisán-Ruiz, and H. Houlden. Mutations in gamma adducin are associated with inherited cerebral palsy. Annals of Neurology, 74(6):805–814, 2013. 

[25] W. Khan, G. Pollastri, F. Duffy, D.C. Shields, and C. Mooney. Predicting Binding within Disordered Protein Regions to Structurally Characterised Peptide-Binding Domains. PLoS ONE, 8(9):e72838, 2013. 

[26] C. Mooney, D.C. Shields, and G. Pollastri. SCL-Epred: A generalised de novo eukaryotic protein subcellular localisation predictor. Amino Acids, pages 1–9, 2013. 

[27] T. Holton, G. Pollastri, D.C. Shields, and C. Mooney. CPPpred: Cell penetrating peptide prediction. Bioinformatics, 29(23):3094–3096, 2013. 

[28] C. Mooney, N.J. Haslam, T. Holton, G. Pollastri, and D.C. Shields. PeptideLocator: Prediction of bioactive peptides in protein sequences. Bioinformatics, 29(9):1120–1126, 2013. 

[29] A. Nongonierma, C. Mooney, D.C. Shields, and R.J. FitzGerald. Inhibition of dipeptidyl peptidase IV and xanthine oxidase by amino acids and dipeptides. Food Chemistry, 141(1):644–653, 2013.  

[30] R. Pushker, C. Mooney, N. Davey, J-M. Jacqué, and D.C. Shields. Marked variability in the extent of protein disorder within and between viral families. PLoS ONE, 8(4):e60724, 2013.

[31] R. Norris, F. Casey, R.J. FitzGerald, D.C. Shields, and C. Mooney. Predictive modelling of angiotensin converting enzyme inhibitory dipeptides. Food Chemistry, 133(4):1349–354, 2012. 

[32] C. Mooney, N.J. Haslam, G. Pollastri, and D.C. Shields. Towards the improved discovery and design of functional peptides: Common features of diverse classes permit generalized prediction of bioactivity. PLoS ONE, 7(10):e45012, 2012.

[33] C. Mooney, K. O’Brien, N.J. Haslam, R.J. Edwards, N.E. Davey, F.J. Duffy, and D.C. Shields. Computational prediction of bioactive peptides. Journal of Peptide Science, 18:S38–S39, 2012. 

[34] C. Mooney, G. Pollastri, D.C. Shields, and N.J. Haslam. Prediction of short linear protein binding regions. Journal of Molecular Biology, 415(1):193–204, 2011. J

[35] C. Mooney, Y.H. Wang, and G. Pollastri. SCLpred: protein subcellular localization prediction by N-to-1 neural networks. Bioinformatics, 27(20):2812–2819, 2011.

[36] I. Walsh, D. Baù, A.J.M. Martin, C. Mooney, A. Vullo, and G. Pollastri. Ab initio and template-based prediction of multi-class distance maps by two-dimensional recursive neural networks. BMC Structural Biology, 9(1):5, 2009.  

[37] I. Walsh, A.J.M. Martin, C. Mooney, E. Rubagotti, A. Vullo, and G. Pollastri. Ab initio and homology based prediction of protein domains by recursive neural networks. BMC Bioinformatics, 10(1):195, 2009. 

[38] C. Mooney and G. Pollastri. Beyond the Twilight Zone: Automated prediction of structural properties of proteins by recursive neural networks and remote homology information. Proteins: Structure, Function, and Bioinformatics, 77(1):181–190, 2009. 

[39] G. Pollastri, A.J.M. Martin, C. Mooney, and A. Vullo. Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information. BMC Bioinformatics, 8(1):201, 2007. 

[40] D. Baù, A.J.M. Martin, C. Mooney, A. Vullo, I. Walsh, and G. Pollastri. Distill: A suite of web servers for the prediction of one-, two-and three-dimensional structural features of proteins. BMC Bioinformatics, 7(1):402, 2006. 

[41] C. Mooney, A. Vullo, and G. Pollastri. Protein structural motif prediction in multidimensional φ-ψ space leads to improved secondary structure prediction. Journal of Computational Biology, 13(8):1489–1502, 2006. 

Book Chapters 

[1] E.M. Jimenez-Mateos, T. Engel, C. Mooney, and D.C. Henshall. MicroRNAs in Epileptogenesis and Epilepsy. In Christian Barbato and Francesca Ruberti, editors, Mapping of Nervous System Diseases via MicroRNAs, volume 6 of Frontiers in Neurotherapeutics Series, pages 153–182. CRC Press, 2016. 

[2] C. Mooney, Y.H. Wang, and G. Pollastri. De novo protein subcellular localization prediction by N-to-1 neural networks. In Riccardo Rizzo and PauloJ.G. Lisboa, editors, Computational Intelligence Methods for Bioinformatics and Biostatistics, volume 6685 of Lecture Notes in Computer Science, pages 31–43. Springer Berlin Heidelberg, 2011. 

[3] C. Mooney, N. Davey, A.J.M. Martin, I. Walsh, D.C. Shields, and G. Pollastri. In silico protein motif discovery and structural analysis. In Bing Yu and Marcus Hinchcliffe, editors, In Silico Tools for Gene Discovery, volume 760 of Methods in Molecular Biology, pages 341–353. Humana Press, 2011. 

[4] A.J.M. Martin, C. Mooney, I. Walsh, and G. Pollastri. Contact map prediction by machine learning. In Introduction to Protein Structure Prediction, pages 137–163. John Wiley and Sons, Inc., 2010. 

Conference Publications and Posters 

[1] C. Mazoa, S. Barron, C. Mooney, W. Gallagher Multi-Gene Prognostic Signatures and Prediction of Pathological Complete Response of ER-Positive HER2-Negative Breast Cancer Patients to Neo-Adjuvant Chemotherapy . European Society for Medical Oncology (ESMO), Barcelona, Spain, 2019. 

[2] C. Mazoa, C. Mooney, W. Gallagher A HistologyGenomic Integration Analysis Using Machine Learning for Predicting Risk of Recurrence of Breast Cancer Machine Learning Summer School (MLSS) 2019 

[3] Automated annotation of EEG in a mouse model of epilepsy L. Wei, R. Gerbatin, C. Reschke, D. Henshall, G. Morris, C. Mooney womENcourage, Rome, Italy, 2019. 

[4] The Multi-Dimensional Process of Developing a Clinical Decision Support System using Machine Learning A. Antoniadi, C. Mooney womENcourage, Rome, Italy, 2019. 

[5] G. Brennan, S. Bauer, T. Engel, E. Jimenez-Mateos, B. Salvetti, F. Del Gallo, R. Raoof, A. Sanz Rodriguez, H. El Naggar, N. Connolly, T. Hill, C. Reudell Reschke, N. Delanty, J. Prehn, P. Fabene, F. Rosenow, C. Mooney, D. Henshall Genome-wide microRNA profiling of plasma from three different animal models identifies biomarkers of temporal lobe epilepsy. Epilepsia, 59(S3):S152, 2018. 

[6] R. Raoof, S. Bauer, H. El Naggar, G. Brennan, T. Hill, E. Brindley, N. Connolly, H. McArdle, E. Spain, R. Forster, J. Prehn, H. Hamer, N. Delanty, F. Rosenow, C. Mooney, D. Henshall Dual-Center, Dual-Platform microRNA Profiling Identifies Plasma Biomarkers of Adult Temporal Lobe Epilepsy. Epilepsia, 59(S3):S151, 2018. 

[7] A. Glasgow, P. McKiernan, C. Mooney, D. Henshall, B. Linnane, PG. McNally, C. Greene Plasma microRNA levels in paediatric males versus females with cystic fibrosis. Pediatric Pulmonology, 53:206-207, 2018. 

[8] M. Kaleel, A. Khalid, T. Kumar, Z. Yandan, C. Jialiang, F. Xuanming, G. Pollastri, C. Mooney DeepSCLpred: Protein subcellular localization prediction by Deep N-to-1 neural networks. 17th European Conference on Computational Biology (ECCB), Athens, Greece, 2018. 

[9] A. Antoniadi, L. Salmon, B. Becker, C. Mooney A case study of the gender gap in the UCD School of Computer Science and actions to address it. 1st International Workshop on Gender Equality in Software Engineering, Gothenburg, Sweden, 2018. 

[10] C. Mooney, B. Becker, L. Salmon, E. Mangina. Computer science identity and sense of belonging: a case study in Ireland. Proceedings of the 1st International Workshop on Gender Equality in Software Engineering, Gothenburg, Sweden, 2018. 

[11] L. Yang, J. Ng, C. Mooney, R. Dong. Multi-level Attention-Based Neural Networks for Distant Supervised Relation Extraction. 25th Irish Conference on Artificial Intelligence and Cognitive Science, 2017. 

[12] P. McKiernan, C. Mooney, D. Henshall, B. Linnane, P. McNally, C. Greene. Serum microRNA levels in paediatric females versus males with cystic fibrosis. European Respiratory Society, 2016. 

[13] B. Becker, C. Mooney. Categorizing Compiler Error Messages with Principal Component Analysis. 12th China-Europe International Symposium On Software Engineering Education (CEISEE), Northeast University, Shenyang, China, 28-29 May 2016. 

[14] H El-Naggar, R Raoof, C. Mooney, P Moloney, A Sanz-Rodriguez, E Jimenez-Mateos, S Bauer, K Klein, F Rosenow, N Delanty, D Henshall. Plasma microRNA Profiling Identifies Potential Biomarkers of Human Temporal Lobe Epilepsy 31st International Epilepsy Congress Istanbul, Turkey, 2015 

[15] C. Mooney, A.M. Looney, E. Jimenez-Mateos, A. Sanz Rodriguez, B. Hallberg, D.C. Henshall, D.M. Murray, G.B. Boylan. Circulating microRNAs: novel biomarkers for neonatal seizures. ISCA-Japan Hoshi University-RCSI Workshop: Future Healthcare – Biomedical Sciences, Technologies and Applications, Hoshi University, Tokyo, Japan, 2015. 

[16] C. Mooney, R. Raoof, H. ElNaggar, A. Sanz Rodriguez, E. Jimenez-Mateos, S. Bauer, F. Rosenow, N. Delanty and D.C. Henshall. Computational discovery of plasma microRNA profiles as biomarkers of temporal lobe epilepsy. International Conference on Intelligent Systems for Molecular Biology and European Conference on Computational Biology (ISMB/ECCB), Dublin, Ireland, 2015. 

[17] C. Mooney, A.M. Looney, E. Jimenez-Mateos, A. Sanz Rodriguez, B. Hallberg, D.C. Henshall, D.M. Murray, G.B. Boylan. Altered serum microRNA expression profiles in neonatal seizures. Inaugural INFANT research day, Cork University Maternity Hospital, Cork, Ireland, 2015. 

[18] C. Mooney, D.C. Shields and T. Holton. Comparative proteomic characterization of protein disorder distribution across eukaryota. Society for Molecular Biology and Evolution Conference (SMBE), Dublin, Ireland, 2012. 

[19] C. Mooney, N.J. Haslam and D.C. Shields. De novo SLiM discovery: Implications for disease understanding and drug design in malaria. Research Advances in Malaria, Tres Cantos, Spain, 2011. 

[20] A. Chubb, K. O’Brien, C. Mooney, D.C. Shields, The FM@H Team. FightMalaria@Home: Crowd- sourcing anti-malarial drug discovery. Research Advances in Malaria, Tres Cantos, Spain, 2011. 

[21] N.J. Haslam, K. O’Brien, C. Mooney, D.C. Shields. De novo protein motif prediction: Implications for disease understanding. Quantitative Biology and Bioinformatics in Modern Medicine, UCD, Dublin, Ireland, 2011. 

[22] N.J. Haslam, K. O’Brien, I. Stavropoulos, E. O’Keefe, A. Chubb, F. Duffy, K. Golla, N. Khaldi, C. Mooney et al. The search for rules of protein association and regulation. The Conway Festival of Research and Innovation, UCD, Dublin, Ireland, 2010. 

[23] A.J.M. Martin, D. Baù, C. Mooney, C. Roche, E. Rubagotti, A. Vullo, I. Walsh and G. Pollastri. protein structural features prediction and modelling of Cα traces through predicted structural constraints. Eight Meeting on the Critical Assessment of Techniques for Protein Structure Prediction (CASP8), Cagliari, Sardinia, Italy, 2008. 

[24] C. Mooney and G. Pollastri. Automated prediction of protein backbone structural motifs by recursive neural networks and remote homology information. European Conference on Computational Biology (ECCB), Cagliari, Sardinia, Italy, 2008. 

[25] C. Mooney, A.J.M. Martin, A. Vullo and G. Pollastri. Exploiting similarity to proteins of known structure leads to improved protein structural motif prediction. International Conference on Intelligent Systems for Molecular Biology and European Conference on Computational Biology (ISMB/ECCB), Vienna, Austria, 2007. 

[26] D. Baù, A.J.M. Martin, C. Mooney, A. Vullo, I. Walsh and G. Pollastri. Modelling of protein Cα traces through structural constraints predicted by machine learning. Seventh Meeting on the Critical Assessment of Techniques for Protein Structure Prediction (CASP7), California, USA, 2006. 

[27] C. Mooney, A. Vullo and G. Pollastri. Porter+: A server for protein structural motif prediction. International Conference on Intelligent Systems for Molecular Biology (ISMB), Fortaleza, Brazil, 2006.