Jose A. Seoane

Dr. Jose A. Seoane is a cancer computational biologist with extensive international experience, having worked in the UK for two years and the US for seven years. He earned his degrees in computer science from Universidade da Coruña, where he also completed a Master in Statistical Learning and Data Mining and an Expertise in Applied Statistical Methods. His PhD, awarded in 2012 by the University of A Coruña under the supervision of Julian Dorado and Fernando Martin, was recognised with the "Extraordinary PhD Award" in Computer Science and Mathematics. It focused on models of biomedical data integration, funded by a Xunta de Galicia Fellowship "Isabel Barreto".

Dr. Seoane's postdoctoral journey began at the University of Bristol's School of Social and Community Medicine, enhancing his skills in bioinformatics, genetics, genomics, and machine learning. His research into breast cancer datasets sparked a profound interest in cancer systems biology, leading him to further positions at the University of Southern California and Stanford University. At Stanford, he was Postdoc and Instructor at Christina Curtis' lab, but also collaborated with eminent researchers like Gerald Crabtree to explore how chromatin remodeling affects cancer progression and drug resistance, resulting in a Nature Medicine publication.

His work has significantly contributed to projects within The Cancer Genome Atlas and the Human Tumor Atlas Network, alongside collaborations with leading cancer researchers across the globe. In 2020, he was awarded a prestigious Ramón y Cajal Fellowship, securing his role as a Principal Investigator at the Vall D'Hebron Institute of Oncology since May 2021. Dr. Seoane actively supervises a dynamic team of PhD and master's students, continually pushing the boundaries of cancer research and epigenetics.

Silvana CE Maas

Dr. Silvana CE Maas holds a Bachelor of Science in Biology and Applied Medical Laboratory Technology from Avans University of Applied Sciences in the Netherlands. Obtaining a NIHES scholarship, she earned a MSc in Health Sciences with a specialization in genetic epidemiology from Erasmus University Rotterdam, where her research focused on the molecular mechanisms linking smoking to cardio-metabolic traits, published in Clinical Epigenetics (2020).

During her PhD, Dr. Maas investigated the links between lifestyle factors, DNA methylation and non-communicable diseases, critiquing reliance on self-reported questionnaires and developing more reliable DNA methylation-based models for lifestyle assessment. Her research is published in top journals including the European Journal of Epidemiology (2019), Clinical Epigenetics (2021) , and Nature Communications (2021). Dr. Maas has also contributed to significant studies on the impact of lifestyle factors on miRNA expression and the integration of multi-omics data to explore their roles in cardiometabolic traits and bone health, with findings disseminated in respected scientific publications.

Currently, as a postdoctoral researcher at VHIO (Barcelona) in the Cancer Computational Biology group, she focuses on the epigenetic effects of lifestyle on cancer, especially in early-onset forms. Her work is supported by the prestigious Juan de la Cierva Scholarship from the Spanish Ministry of Science and aims to offer new insights into disease mechanisms and personalised medicine, emphasizing the importance of lifestyle in cancer epidemiology.

Lea Lemler

Dr. Lea Lemler completed her Master of Science in Computer Science with distinction at Queen Mary, University of London, in 2014. Afterwards she joined Hewlett Packard Enterprise Germany in the beginning of 2015 where she worked for 4.5 years as a data scientist in international projects.

In 2019 she started her PhD as part of the Precision Medicine Doctoral Training Programme with integrated study at the University of Edinburgh, which was funded by the Medical Research Council. As part of the Colon Cancer Genetics Group, led by Professor Malcolm Dunlop, she performed a large-scale transcriptome-wide association analysis with the aim of understanding the relationship between gene expression and survival outcome in colorectal cancer (CRC) patients.

Following her interest in computer science and cancer biology she joined the Cancer Computational Biology Group at Vall d'Hebron Institute of Oncology in 2023. As part of the group, she works on multiple multi-omics projects however with a particular focus transcriptomics and the BRAF-V600E mutation in CRC, a subpopulation of CRC with particularly aggressive disease. Her aim is to develop a powerful state-of-the-art transcriptomic subtyping system for BRAF-V600E in CRC using artificial intelligence & single-cell RNA sequencing to better understand the heterogeneity in response and duration of response within BRAF inhibition strategies.

Hubert Bretonnière

Hubert Bretonnière got a PhD in Astronomy and Astrophysics in 2022 from the University Paris-Saclay. During this PhD, he developed new Deep Learning methods to generate and analyse galaxy images for the European Space Agency (ESA)'s mission Euclid. His work led to three main author publications, two in one of the major Astrophysics journal (Astronomy and Astrophycics) and one in the Deep Learning conference NeurIPS. These works focuses on the conditonal generation of realistic galaxies images with a Flow-Variational Auto-Encoder, the probabilistic segmentation of overlapping galaxies with a U-net and the analysis of a high-dimensional fitting challenge involving many groups of experts.

With a Postdoc at the University of California, Santa Cruz (UCSC) in 2023, he continued developing his skills with various deep learning architectures (Variational Auto-Encoders, Generative Adversarial Networks, Normalizing Flows) in particular for the segmentatiob of sub-regions of galaxies, and outlier detection for the JWST NASA mission.

He is particularly interested in the interactive and probabilistic visualisation of deep learning methods, to provide safe and easy-to-use products to the community.

He joined the Cancer Computational Biology group as a postdoctoral fellow in July 2024, where he is currently applying the same type of technics applied to histopathology images and multi-omics data to predict various valuable information.

Maria Farina-Morillas

Maria Farina-Morillas holds a Bsc in Biotechnology from Universitat Autònoma de Barcelona (UAB). While studying, they carried out a year-long internship at Vall d'Hebron Research Institute (VHIR), Barcelona, on computational characterization of host-pathogen protein-protein interfaces.

In 2018, they earned their MSc in Bioinformatics from UAB, conducting their Master thesis at Institute of Biotechnology and Biomedicine (IBB), Barcelona, on the identification of host-pathogen protein-protein interactions that could be potential vaccine candidates.

In 2019, they joined the Cluster of Excellence Cellular Stress Responses in Aging-Associated Disease (CECAD), Cologne, associated to the University Hospital of Cologne, as a bioinformatician. They applied molecular dynamics tools to explore the conformational landscape of LYN kinase, as well as to study the changes in the tridimensional structure of PI3K kinase (Hassenrück, F.; Farina-Morillas M.; et al, 2023, Communications biology).

In May 2022, they joined the Cancer Computational Biology group at Vall d'Hebron Institute of Oncology (VHIO) as a PhD student, with a FPI fellowship from the Spanish Agencia Estatal de Investigación. Their research is focused on the combination of multi-omics data for the development of a prediction algorithm to target epigenetic synthetic lethality in gastrointestinal cancers. In addition, they are an advisory board member of VHIO's Patient and Citizen Involvement (PATIENT) Program, an initiative aimed at educating patients and citisens in oncology, to introduce the patient perspective in the design of oncology research projects.

Maria Butjosa

I am a biochemist by Universitat Autònoma de Barcelona (UAB) - 2020. I joined the lab in 2021 as a master Student as part of my Bioinformatics and Biostatistics master (UOC) and after finishing I continued in the lab as a PhD student (June 2022). My work in the lab includes analysis of the microbiome, drug response. On top of my PhD work, I am very interested in SciCom, specially for children and visual science communication through graphic design.

Enric Álvarez

My background is biomedical sciences and MSc in Bioinformatics and Biostatistics. Since late 2016 until December 2024 I have been working in VHIO as clinical study coordinator in oncology in several diseases mainly focused in breast, lung and genitourinary tumors.

In December 2022, in combination with my profesional duties, I decided to start a part-time PhD in Medicine with Dr Cristina Saura and Dr Jose A Seoane as directors. The aim of the PhD project is to integrate clinical survival data with genomic and histopathological images from tumor slides to determine the best sequence of therapy in patients with HER2 positive breast cancer.

Arnau Llinàs

Arnau Llinas-Bertran holds a BSc in Biomedical Sciences (2020) from Universitat Autònoma de Barcelona (UAB) and a MSc in Bioinformatics for Health Sciences (2022) from Universitat Pompeu Fabra (UPF) in Barcelona. He joined the group in 2021 as a master's student and later in 2022 he started his PhD in breast cancer, studying epigenetic changes under endocrine therapy and how these relate to distant relapse risk for the patients.

Since 2022 he is also part of the AURORA (BIG 14-01) metastatic breast cancer molecular study, where he is part of the Data Analysis Committee, in charge of managing and analysing transcriptomics data, and member of the Triple Negative Breast Cancer working group.

Laia Ollé i Monràs

Laia Ollé i Monràs holds a Bsc in Biotechnology (2021) from the Universitat de Barcelona (UB). During her academic journey, she developed expertise in the application of computational tools to biological research, with a focus on big data analysis for global health challenges. Her final Bsc project at the Barcelona Supercomputing Center (BSC) involved the identification of micropeptides associated with pancreatic adenocarcinoma.

She later pursued a Msc in Computational Biology from the Universidad Politécnica de Madrid (UPM) (2022). Her contributions to cancer research started at the National Center for Oncological Investigations (CNIO) (2022), where she worked in Dr. Solip Park's Computational Cancer Genomics Group, investigating high-order genomic interactions across tumour types.

She joined the Cancer Computational Biology Group as a bioinformatician in 2023, working on multiple projects analysing large multi-omics data and developing and applying algorithms using machine learning to study cancer progression and fitness. She recently became a PhD student to study chromatin structure and its influence on cell states, and their role in cancer initiation and progression.

Juan Rafael Valera

Juan Rafael Valera holds a BSc in Biochemistry (2022) from the University of Córdoba (UCO), where his academic focus was primarily on genetics and biotechnology. Driven by his interest in these fields, he pursued the MSc in Genetics and Evolution at the University of Granada (UGR). During his formation, he worked in collaboration with the Genetics of Complex Diseases Group in the Pfizer-University of Granada-Junta de Andalucía Center for Genomics and Oncology Research (GENYO). Focused on the analysis of microbial effects on systemic autoimmune diseases, he raised his interest in bioinformatics and biomedical research.

Also inspired by the growing potential of computational biology and artificial intelligence (AI), he completed a second MSc in Bioinformatics and Biostatistics at the Universitat Oberta de Catalunya (UOC). During this program, he conducted an exposome and multi-omics data integration project on hypertension to identify environmental exposures and cardiovascular risk biomarkers in collaboration with the Environment and Health over the Lifecourse Programme in the Barcelona Institute for Global Health (ISGlobal).

Currently, he joined the Cancer Computational Biology Group as a data scientist in 2024, applying machine learning (ML) and deep learning (DL) methods to advance cancer diagnosis, prognosis, and treatment-response prediction using multi-omics and histopathology data. He also became a PhD student in Bioinformatics at the Autonomous University of Barcelona (UAB), aiming to further contribute to the integration of AI-driven approaches in oncology research.