Dr Inti Anabela Pagnuco

Dr Inti Anabela Pagnuco

Institute of Cancer and Genomic Sciences
Research Fellow

Contact details

Address
Centre for Computational Biology
Haworth Building
University of Birmingham
Edgbaston
Birmingham
B15 2TT

Dr Pagnuco is a Research Fellow in the Institute of Cancer and Genomic Sciences. Her principal interests are genomic diseases and how to use machine learning techniques to gain novel insights into large datasets.

Qualifications

  • PhD in Electronic Engineering 2016, School of Engineering, National University of Mar del Plata, Argentina
  • BSc in Bioinformatic 2010, School of Engineering, National University of Entre Ríos, Argentina

Biography

Dr Pagnuco received a degree in Bioinformatics in 2010 from the School of Engineering, National University of Entre Ríos, Argentina. In 2016, she received a PhD in Electronic Engineering from the School of Engineering at the National University of Mar del Plata (UNMdP). Her research topic was 'Supervised classification techniques applied to high-density genetic data'.

She worked as teaching assistant in the area of Applied Mathematics and informatics, assigned to the courses “Algorithms and Data Structures”, “Basic Statistic”, “Programming”, “Numerical Methods” and “Data Bases” in the School of Engineering of the National University of Mar del Plata, Argentina.

In 2018, she did a postdoctoral experience at System Biology Ireland, University College Dublin. The focus of the project was to build a dynamic modeling of the interconnected p53/p63/p73 DNA damage response network and consider the p53 mutant status. The model was applied to breast cancer data through patient-specific simulations to predict clinical outcomes as patient survival and clinical drug-response.

Research

Dr Pagnuco's research involves generating genome-wide distribution of cis-regulatory elements, in particular enhancers and exploring the mechanism of gene-expression programs using mathematical modelling and quantitative methods such as the iterative Random Forest (iRF). Specifically, her aim is to use machine learning to predict the functional activity of cis-regulatory elements genome-wide, using ChIP-seq, DNase-seq, digital footprinting, and motifs as inputs and use validated enhancer elements as training sets.

Research Groups and Centres

Centre for Computational Biology

Publications

Velázquez-Castro, et al. ‘A mathematical model of anarchy in bees’. Apidologie (2020). https://doi.org/10.1007/s13592-020-00789-8

Pagnuco, I, et al. ‘HMMER Cut-off Threshold Tool (HMMERCTTER): Supervised Classification of Superfamily Protein Sequences with a reliable Cut-off Threshold’.  Plos One. 13(3): e0193757.  (2018) . https://doi.org/10.1371/journal.pone.0193757.

Pagnuco, I., Pastore,  J.,  Abras, G., Brun, M. , Ballarin, V. ‘Analysis of genetic association using Hierarchical Clustering and Cluster Validation Indices’. Genomics. (2017). pii: S0888-7543(17)30057-5. doi: 10.1016/j.ygeno.2017.06.009.

Pagnuco, I., Pastore,  J.,  Abras, G., Brun, M. , Ballarin, V. ‘Analysis of genetic association in Listeria and Diabetes using Hierarchical Clustering and Silhouette Index’. Journal of Physics: Conference Series 705 (2016) 012002. doi:10.1088/1742-6596/705/1/012002

Quintana, S, et al. ‘A new method for simultaneous detection and discrimination of Bovine herpesvirus types 1 (BoHV-1) and 5 (BoHV-5) using real time PCR with high resolution melting (HRM) analysis’. Journal of Virological Methods. (2015).

Quintana, S, et al. ‘Mutations associated with resistance to pyrethroids in coumaphos and Varroa Destructor’. Journal of Basic & Applied Genetics,  (2014), Volumen 25, Issue 1,  Supp.  ISSN: 1852-6233.

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