
TE4med
Transposcriptome-based identifier for precision medicine

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Luis is originally from Spain, where he completed his bachelor studies on Electrical engineering, and my Ms.C. on signal theory and communications, both at the University of Seville. During his Ph.D. he started focusing on machine learning methods, more specifically message passing techniques for channel coding, and Bayesian methods for channel equalisation. He carried it out between the University of Seville and the University Carlos III in Madrid, also spending some time at the EPFL, Switzerland, and Bell Labs, USA, where he worked on advanced techniques for optical channel coding. When he completed his Ph.D. in 2013, he moved to the Luxembourg Center on Systems Biomedicine, where he switched his interest to neuroscience, neuroimaging, life sciences, etc., and the application of machine learning techniques to these fields. During his 4 and a half years there as a Postdoc, he worked on many different problems as data scientist, encompassing topics such as microscopy image analysis, neuroimaging, single cell gene expression analysis, etc. He joined the SDSC in April 2018.
description
Problem:
Precision oncology relies on the characterization of molecular signatures of diagnostic and treatment selection value. An algorithm that ensures not only high sensitivity and specificity, but also extreme precision in the identification of tumor subtypes is key and missing.
Proposed approach:
Combine:
- Advanced ML techniques.
- The power of Transposable Elements as a highdensity barcode for cell identity.
- High-throughput sequencing big data. To obtain biomarkers that define specific, sensitive and robust classifiers.
Impact:
- Trace the origin of tumors.
- Predict their sensitivity to particular treatments.
- Monitor therapeutic responses.
- Detect relapses as early as possible.
- Identify new therapeutic targets.
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