Héctor Climente-González

Senior Scientist at Novo Nordisk. London, United Kingdom.


As a Senior Data Scientist in Novo Nordisk, my goal is to find genetic predictors of disease that are useful for diagnosis, treatment, and understanding the underlying biology. To that end, I use machine learning to assist with modeling, biomarker discovery and variant interpretation.

I have over 10 years of experience in computational biology and applied statistics. I completed my PhD in MINES ParisTech, under the supervision of Chloé-Agathe Azencott. In my thesis I explored graph-based methods for genetic studies. Afterwards, I was accepted into RIKEN’s SPDR program to conduct my postdoctoral research with Makoto Yamada. I focused on the development and application of novel machine learning methods for feature selection.


Mar 7, 2024 Our model for cardiovascular disease risk prediction got highlighted in Novo Nordisk’s Capital Markets Day.
Jan 13, 2024 Our preprint on predicting cardiovascular disease risk using interpretable AI was just published on medRxiv. This was a joint work with Microsoft scientists.
Nov 9, 2023 Our preprint on detecting epistasis using :sparkles: quantum computing :sparkles: was just published on medRxiv.
Mar 1, 2023 I joined the Novo Nordisk Research Center Oxford as Senior Data Scientist. I will be applying machine learning to genetics.

latest posts

selected publications

  1. The functional impact of alternative splicing in cancer
    Héctor Climente-González, Eduard Porta-Pardo, Adam Godzik, and 1 more author
    Cell reports, 2017
  2. Block HSIC Lasso: model-free biomarker detection for ultra-high dimensional data
    Héctor Climente-González, Chloé-Agathe Azencott, Samuel Kaski, and 1 more author
    Bioinformatics, 2019
  3. Interpretable network-guided epistasis detection
    Diane Duroux, Héctor Climente-González, Chloé-Agathe Azencott, and 1 more author
    GigaScience, 2022
  4. A network-guided protocol to discover susceptibility genes in genome-wide association studies using stability selection
    Héctor Climente-González, Chloé-Agathe Azencott, and Makoto Yamada
    STAR protocols, 2023
  5. Interpretable Machine Learning Leverages Proteomics to Improve Cardiovascular Disease Risk Prediction and Biomarker Identification
    Héctor Climente-González, Min Oh, Urszula Chajewska, and 8 more authors
    medRxiv, 2024