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Dr Nuno Rocha Nene
Dr Nuno Rocha Nene profile picture
  • Senior Research Fellow - Data Scientist in Cancer Immunotherapy
  • Research Department of Oncology
  • Cancer Institute
  • Faculty of Medical Sciences

I graduated from Instituto Superior Técnico, Lisbon, with an Integrated Masters in Physics Engineering, specialisation in Quantum Mechanics/Nuclear Physics (2003). Subsequently, I continued developing my expertise in Nuclear Physics and Machine Learning/AI while collaborating with world-leading experts at the nuclear technology campus of the University of Lisbon. Following this work, I commenced my postgraduate studies (2005) at University College London (UCL), funded by an EPSRC scholarship for the doctoral programme (MRes+PhD) organised by CoMPLEX. During this research, I worked on a framework for understanding rate-dependent effects near the critical transitions of intra-cellular networks. 

Since finishing my PhD (2011), I have focused on projects in very stimulating areas in Systems and Evolutionary Biology, at Imperial College London (Mathematics, 2011-2014) and the University of Cambridge (Genetics, 2014-2017), respectively. The complexity of the subjects studied required the application and implementation of a collection of advanced Graph Theory, Machine Learning and Data Science techniques for processing genomic time-series.

In pursuit of my long-term interest in Cancer Biology, I joined the Cancer Proteomics group, led by Dr John Timms, at the UCL Institute for Women’s Health (2018). There, I’m extending change-point detection and other machine learning methods for the analysis of longitudinal proteomic data and early detection of ovarian and pancreatic cancer. This work is funded by Cancer Research UK and is being carried out in collaboration with Prof Alexey Zaikin. I have also been collaborating with the Translational Research Centre, led by Prof Martin Widschwendter, where I’m working towards a deep-learning protocol for analysing methylation signatures from multi-tissue samples collected for the FORECEE project."

Research Groups
Research Summary

Main topics of research:

  • Change-point detection, mean trend, deep learning and Bayesian latent class methods for the analysis of longitudinal proteomic data with the objective of identifying and testing potential biomarkers of ovarian and pancreatic cancers.
  • Parenclitic network classifiers with high-dimensional manifold identification routines. Applications include the prediction of cancer and age from DNA methylation data.
  • Deep learning methods based on convolutional networks and  RNNs, e.g. LSTMs and GRUs, as well as elastic networks, for the analysis of DNA methylation signatures.
  • HMMs for inference of population parameters from partially observed evolutionary time-series.
  • Gaussian Process Regression for the analysis of dynamical activity patterns of large bio-networks.
  • Rate-dependent effects in information processing near critical points of canonical normals forms and bio-circuits and their sensitivity to initial conditions.

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