Biographical Sketch

NAME: Ibon Martínez Arranz

POSITION TITLE: Data Science Manager at OWL

EDUCATION/TRAINING:

INSTITUTION AND LOCATION	DEGREE	COMPLETION DATE	FIELD OF STUDY
University of Basque Country (UPV/EHU) (Biscay, Spain)	BSc.	06/2003	Mathematics
National University of Distance Education (Spain)	Expert degree	07/2007	Statistics Applied to Health Sciences
National University of Distance Education (Spain)	Expert degree	07/2008	Advanced Methods of Applied Statistics
National University of Distance Education (Spain)	MSc.	07/2012	Current Applied Statistics Techniques
National University of Distance Education (Spain)	Expert degree	07/2013	Multivariate Statistical Techniques
University of Basque Country (UPV/EHU) (Biscay, Spain)	MSc.	05/2016	Mathematical Modeling, Statistical Analysis and Computation
University of basque Country (UPV/EHU) (Biscay, Spain)	Ongoing PhD.	Expected 2022	Applied Mathematics

Currently, I am doing a doctoral thesis entitled “Genetic Algorithms Applied to Translational Strategy in NASH. Learning from Mouse Models” under the supervision of José María Mato (General Director of CIC bioGUNE) and Dae-Jin (Data Science line leader of Basque Center for Applied Mathematics, BCAM) in which I apply genetic algorithms for the selection of NAFLD subtypes. This procedure could be applied to any disease that is based on metabolic changes. This work also has implications in the field of precision medicine.

A. Personal Statement

Ibon Martínez-Arranz got his BSc. in Mathematics from the University of the Basque Country (EHU/UPV) and performed a MSc. in Current Applied Statistics Techniques and a MSc. in Mathematical Modeling Research, Statistical Analysis and Computing Sciences. In 2004, he joined the Basque Country Health Service (The Department of Health of the Basque Country Government and Osakidetza) as a data analyst and his work was mainly focused on the epidemiological report of renal patients. When he joined OWL Metabolomics in 2010, Ibon first worked as a researcher in the Metabolomics Department and in 2017, he became the head of the Data Science Department, being responsible for prediction and statistical computation management since then. This highly experienced team psupports metabolomics services, laboratory processes, data handling, R&D projects and technology transfer processes.

B. Positions and Honors

Head of Data Science Department at OWL. (2017 - to date).
Researcher in Metabolomics Department at OWL Metabolomics. (2010 - 2017).

C. Contributions to Science

Author of 24 publications Google Scholar H-index = 16 Researcher unique identifier

• ORCID
• Scopus
• pubmed

Data mining of 'omics' data

'Omics' research generates a large amount of data for every sample. OWL has established a well-defined workflow and a set of guidelines for analyzing omics data. It includes statistical design of experiments, data structuration and predictive modelling.

Peer-reviewed papers:

1. Martínez-Arranz, I, et al. Enhancing metabolomics research through data mining, J. Proteomics, 2015;127(B)275-288.
2. Barr J, et al. Obesity-dependent metabolic signatures associated with nonalcoholic fatty liver disease progression. J. Proteome Res 2012;11(4),2521-32
3. Arbelaiz A, et al. Serum extracellular vesicles contain protein biomarkers for primary sclerosing cholangitis and cholangiocarcinoma. Hepatology. 2017; 10.1002/hep.29291.
4. Cano A, et al. A Metabolomics Signature Linked To Liver Fibrosis In The Serum Of Transplanted Hepatitis C Patients. Scientific Reports. 2017;7(1):10497.

Predictive algorithms for disease diagnosis

We have developed predictive algorithms for several diseases. We have been really involved in the development and validation of the OWLiver, OWLiver F2+ and OWLiver DM2 tests.

Peer-reviewed papers:

1. Barr J, et al. Obesity-dependent metabolic signatures associated with nonalcoholic fatty liver disease progression. J. Proteome Res. 2012;11(4):2521-32.
2. Cano A, et al. A Metabolomics Signature Linked To Liver Fibrosis In The Serum Of Transplanted Hepatitis C Patients. Scientific Reports. 2017;7(1):10497.
3. Herreros-Villanueva M, et al. Plasma MicroRNA Signature Validation for Early Detection of Colorectal Cancer. Clin Transl Gastroenterol. 2019 Jan;10(1).
4. Matorras R, et al. The lipidome of endometrial fluid differs between implantative and non-implantative IVF cycles. J Assist Reprod Genet. 2020;37(2):385-94.
5. Martínez-Arranz I, et al. Metabolomic-based noninvasive serum test to diagnose nonalcoholic steatohepatitis: Results from discovery and validation cohorts. Hepatol Commun. 2018 May 4;2(7):807-820.

Selected publications focused in Machine Learning and Modelling (within last 5 years)

Peer-reviewed papers:

1. Alonso C, et al. Metabolomic Identification of Subtypes of Nonalcoholic Steatohepatitis. Gastroenterology 2017;152(6):1449-61.
2. Iruarrizaga-Lejarreta M, et al. Role of Aramchol in steatohepatitis and fibrosis in mice. Hepatology Communications 2017;1(9):911-27.
3. Banales JM, et al. Serum metabolites as diagnostic biomarkers for cholangiocarcinoma, hepatocellular carcinoma and primary sclerosing cholangitis. Hepatology 2019;70(2):547-65.
4. Herreros-Villanueva M, et al. Plasma MicroRNA Signature Validation for Early Detection of Colorectal Cancer. Clin Transl Gastroenterol. 2019 Jan;10(1).
5. Matorras R, et al. The lipidome of endometrial fluid differs between implantative and non-implantative IVF cycles. J Assist Reprod Genet. 2020;37(2):385-94.

D. Patents Granted and Pending

PUBLICATION NUMBER	TITLE	INTERNATIONAL FILING DATE
WO2021028562	Lipid signatures for determining the outcome of embryo implantation during in vitro fertilization	14.08.2020
WO2018007511	Diagnostic methods based on lipid profiles	06.07.2017
WO2018007422	Identification of human Non-Alcoholic Fatty Liver Disease (NAFLD) subtypes	05.07.2017
WO2017055397	Metabolomic signature of diagnosis and disease progression in Non-Alcoholic Fatty Liver Disease (NAFLD)	29.09.2016