Héctor Climente

Héctor Climente

Postdoctoral researcher in machine learning & human genetics



I am a postdoctoral researcher at the High-Dimensional Statistical Modeling Team, led by Makoto Yamada.

My goal is to find genetic predictors of disease that are useful for diagnosis, treatment, and understanding the disease’s biology. Traditionally, complex diseases like Alzheimer’s disease are studied through genome-wide association studies ( GWAS). However, conventional GWAS have failed to find a significant share of those genetic causes. I am interested in closing this gap by researching machine learning methods that extract more information from GWAS. Specifically, I superimpose GWAS information onto biological networks. Then, I explore such networks considering both the statistical association of each genetic factor - obtained from GWAS - and the biological context - obtained from the network. However, there are many open questions about how to do this, for instance:

  • How to exactly integrate GWAS information with networks?
  • What is the right strategy to find truly associated genetic factors? Which underlying assumptions are warranted?
  • What sources of information should we consider when building those networks?
  • What is the suitable unit of analysis? The SNP? The gene? The pathway?

With my multidisciplinary background in machine learning, statistical genetics, and biotechnology, I believe I am in a privileged position to tackle such questions


  • Genetics & heritability
  • Machine learning
  • Biological networks
  • Multi-omics integration
  • Multidisciplinary research


  • PhD in Bioinformatics, 2020

    Paris Sciences & Lettres

  • MSc in Bioinformatics for Health Sciences, 2014

    Pompeu Fabra University

  • BSc in Biochemistry, 2013

    Autonomous University of Barcelona

  • BSc in Biotechnology, 2012

    Autonomous University of Barcelona

Selected experience

For more details, visit my LinkedIn


Postdoctoral researcher


May 2020 – Present Kyoto

RIKEN AIP is a research center devoted to machine learning research.

I develop machine learning methods to discover the genetics of complex phenotypes in the UK Biobank. Specifically, I focus on deep learning and non-linear feature selection methods.


PhD Student


Oct 2016 – Apr 2020 Paris

CBIO is a joint research team between Mines ParisTech and Institut Curie, devoted to applying machine learning to diagnosis and treatment of cancer. I was supervised by Chloé-Agathe Azencott.

In my thesis, Network-guided genome-wide association studies, I explore existing machine learning algorithms, and develop novel procedures, to better understand the genetics of cancer susceptibility. All those methods leverage on prior biological knowledge, modeled as a graph, to boost discovery.


Head of Biocomputing

Anaxomics Biotech Ltd.

Sep 2014 – May 2015 Barcelona
Anaxomics combines systems biology and machine learning to model human cells on which to perform in silico experiments. I was responsible for developing and maintaining pipelines for the statistical treatment of omics data, whose results were to be included in the models.

Research assistant


Dec 2013 – Aug 2016 Barcelona
In a large-scale study supervised by Eduardo Eyras, we examined the involvement of alternative splicing on 11 different tumor types.


For more details, visit my GitHub



General purpose, dockerized pipelines for GWAS.


Network-based GWAS.


Non-linear feature selection.


Detect differential alternative splicing.