I am an Assistant Professor in Bioinformatics at the Nottingham School of Veterinary Medicine and Science (since June, 2016). Previously, I was a research fellow at the Imperial College London (2011 - 2016). My research themes include the development of novel bioinformatics and machine learning solutions to understand and diagnose infectious diseases in humans and animals. I obtained my Laurea Degree in Biological Sciences from the University of Perugia and a PhD degree in Structural Biology and Bioinformatics from the University of Rome Tor Vergata.
I am an Assistant Professor in Bioinformatics at the University of Nottingham. My current interests are in the development of original methods and algorithms to gain deeper insight in biological problems related to human and animal health. To this purpose in my research I try to merge different disciplines and knowledge/skills, including: bioinformatics and machine learning to develop predictive models and solve data mining tasks, in particular in scenarios involving large-scale data analysis from omics technologies (genomics, transcriptomics, proteomics, etc). My research themes include the development of novel solutions to understand and diagnose infectious diseases. I am particularly interested in the study of insurgence and propagation of antimicrobial resistance in humans and animals, at the molecular and population level.
IZQUIERDO A, FAHRENBERGER M, FAHRENBERGER M, BENEDICT MQ, BENEDICT MQ, CATTERUCCIA F, EMES RD and DOTTORINI T, 2019. Evolution of gene expression levels in the male reproductive organs of Anopheles mosquitoes. Life Sci Alliance. 2(1):e201800191, WALTON E, CASEY C, MITSCH J, VÁZQUEZ-DIOSDADO JA, YAN J, DOTTORINI T, ELLIS KA, WINTERLICH A and KALER J, 2018. Evaluation of sampling frequency, window size and sensor position for classification of sheep behaviour. Royal Society open science. 5(2), 171442
TIAN AL, LU M, CALDERÓN-MANTILLA G, PETSALAKI E, DOTTORINI T, TIAN X, WANG Y, HUANG SY, HOU JL, LI X, ELSHEIKHA HM and ZHU XQ, 2018. A recombinant Fasciola gigantica 14-3-3 epsilon protein (rFg14-3-3e) modulates various functions of goat peripheral blood mononuclear cells. Parasites & vectors. 11(1), 152
2019 - 2022 FARMWATCH: Fight AbR with Machine learning and a Wide Array of sensing TeCHnologies, Principal Investigator (PI), Funded by InnovateUK and Chinese Ministry of Science and Technology. UK partners: Nimrod Veterinary Products Ltd; Chinese partners: Key Laboratory of Food Safety Risk Assessment of Ministry of Health, China National Center for Food Safety Risk Assessment, New Hope Liuhe Limited Company, Dongwa Software Company)
2019-2020 CARE Bangladesh: Cholera Antibiotic REsistance in Bangladesh: big data mining and machine learning to improve diagnostics and treatment selection. Principal Investigator (PI), Funded by GCRF. Partners: North South University, Icddr,b UNICEF, BCSIR, University of Maryland (USA)
2018- 2020 Reducing, Refining and Monitoring the Impact of Antibiotic Use in the Control of Bovine Mastitis. Co-Investigator (Co-I). Funded by Innovate UK. Partners: QMMS
2017-2020 Y-Ware. "Young-stock Welfare & Performance - Decision Making Platform". Co-investigator (Co-I), Funded by Innovate UK. Partners: PrognostiX
2011-2015 EU-FP7-PEOPLE-IEF Senior Fellowship - VECTRAP. "Sex determination pathway in the malaria vector Anopheles gambiae". PI.
Current opportunities in my group: