Immune selection determines mutational landscape of cancer and predict response to immunotherapies.
Abstract: Natural selection forces govern somatic cell evolution. One of these forces is the immune system that, besides protecting us from viruses and bacteria, recognises our own faulty somatic cells and ultimately shapes the emergence of tumors during a process called immunoediting. However, to what extent genetic variation in cancer undergoes immunoediting, and what are the determinants that predispose somatic cells to become malignant remain poorly understood. By exploiting 10K genomic datasets from 33 tumor types, we have uncovered the extent of natural selection in the cancer genome...
Read MoreThe Limitations of Machine Learning & Us.
Abstract: Machine learning (ML), particularly deep learning, is being used everywhere. However, not always is used well, ethically and scientifically. In this talk we first do a deep dive in the limitations of supervised ML and data, its key component. We cover small data, datification, bias, predictive optimization issues, evaluating success instead of harm, and pseudoscience, among other problems. The second part is about our own limitations using ML, including different types of human incompetence: cognitive biases, unethical applications, no administrative competence, copyright...
Read MoreA variational and numerical approach to model inverse problems applied in subduction earthquakes.
Abstract: This talk presents a mixed variational formulation for the problem of the elasticity equation with jump conditions in an interface with the purpose of modeling subduction earthquakes by introducing the concept of coseismic jump. For this new problem, we introduced an optimal control problem that seeks to recover the coseismic jump from boundary observations. Both problems can be discretized by applying mixed finite elements. Synthetic results applied to a realistic context will be presented. Finally, we analyze some improvements for the numerical discretization and preliminary...
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