Camila Guerrero de Blois
Senior Bioinformatics Scientist @Mavatar
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WORK HISTORY
Senior Bioinformatics Scientist @Mavatar
I collaborate cross-functionally with biologists, data scientists, software developers and clinicians to drive insights from omics data toward translational and clinical outcomes in three different teams- Single-cell team: Designing and developing robust pipelines for preprocessing and analysis of single-cell RNA-seq data, including quality control, normalization, clustering, and annotation- Drug predictions team: Leveraging machine learning and AI tools to predict drug responses and biomarkers; extensive use of R for data analysis and visualisation. Interpretation of biological and clinical data- Scientific communications: Actively contributing to the scientific communications strategy by attending conferences, engaging with clients, and presenting complex analyses in a clear, accessible manner to both technical and non-technical audiences.Some responsibilities include maintaining reproducibility and transparency through version-controlled workflows, documented pipelines, and collaborative codes using Azure/GitHub.
EDUCATION
Concordia University
Biochemistry , Semester abroad
University of Navarra
Doctor of Philosophy - PhD, PhD in Applied Medicine and bioinformatics (Onco-hematology research)
University of Navarra
Bachelor of Applied Science (B.A.Sc.), Biochemistry
University of Navarra
Master of Science - MS, Biomedical Research, Cancer specialisation
ABOUT CAMILA GUERRERO DE BLOIS
Passionate about bridging the gap between laboratory bench discoveries and patient care using big data, multi-omics and AI.My scientific career has been dedicated to applying computational and statistical methods to understand biological and clinical data and translate these findings into personalised treatment strategies for patients. Key strengths- Genomics and transcriptomics (bulk and single-cell level) from pre-processing, pipeline development (Snakemake, Nextflow) and quality control, to downstream data visualisation- Machine learning (scikit-learn) and biostatistics for modelling complex clinical, immune and biological data (classification, regression, dimensionality reduction, survival analyses and Bayesian statistics)- Clinical trials data management and analysis- Programming languages: R (>4 yrs), UNIX/Linux command line (4 yrs), and Python (2 yrs)- Passionate about scientific communications - both written and verbally Highlighted projects- Led a multi-omics bioinformatics project involving the analysis of over 700 patients’ samples to enhance the understanding of myeloma’s pathobiology, therapeutic responses, and resistance mechanisms- Developed a machine learning model integrating clinical, genetics, and tumor immune microenvironment data to predict patients who will achieve an undetectable measurable residual disease (MRD) assessment after first-line of therapy with an accuracy >70%. Facilitated this model in an open-access calculator: Collaborated in COVID-19 research to define immune biomarkers to predict vaccine effectiveness in patients with haematological malignancies- Implemented a mathematical model to predict MRD-resurgence and/or progressive disease in multiple myeloma patients (published in Blood)- Optimised pipelines for the automated analysis of transcriptomics and genomics data (RNAseq, scRNAseq and whole exome sequencing) that reduced costs and time by over 50%- Engineered a mathematical model to classify patients with multiple myeloma that present an MGUS-like phenotype at diagnosis that confers better clinical outcomes. Facilitated this model in an open-access calculator: Mentored PhD students in bioinformatics and data analytics best practices Soft skills- Time-management, communication, creativeness, leadership
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