Francisco Requena

Francisco Requena

PhD Student - Bioinformatics

Institut Imagine.

Biography

I did my PhD at the Clinical Bioinformatics lab in the Imagine Institute (Paris). My current work focuses on the development of computational methods, including machine-learning, for the clinical interpretation of variants in rare disease patients.

Interests

  • Human genetics
  • Drug discovery
  • Machine learning
  • Non-coding DNA

Education

  • PhD Clinical Bioinformatics, 2022

    Paris University

  • Msc Bioinformatics, 2019

    Autonomous University of Madrid

  • Msc Translational Research, 2016

    University of Granada

Recent Posts

Human genetics as a tool for drug discovery

For children with a rare disease, an accurate diagnosis is crucial to provide advice, possible therapies and assess the potential risk for family members in future generations. Public initiatives such as the International Rare Diseases Research Consortium (IRDiRC) set the goal for 2017-2027 to “enable all people living with a rare disease to receive an accurate diagnosis, care, and available therapy soon after seeking medical care” (1).

How many genes have been associated with cancer in PubMed?

In the biomedical literature, it is common to find sentences like: “Besides, the gene [gene symbol] has been associated with [type of cancer(s)] [References]” The structure of these sentences can change from article to article, but the underlying idea and goal are the same.

Extracting gene panels from the Genomics England Panelapp

The Genomics England PanelApp provides panels of genes related to human disorders manually curated by healthcare experts. From a clinical and research perspective, this is a remarkable resource. At the time of writing this post, over 320 panels have been published.

An introduction to ROC curves with animated examples

Overview Receiver operating characteristic (ROC) curves is one of the concepts I have struggled most. As a personal view, I do not find it intuitive or clear at first glance.

An introduction to uncertainty with Bayesian models

Overview In this post, we will get a first approximation to the “uncertainty” concept. First, we will train two models: logistic regression and its “Bayesian version” and compare their performance.

Projects

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Drugsplot

Web application which analyzes data from the European MonitoringC entre for Drugs and Drug Addiction (EMCDDA) with more than 500 variables throughdata visualization such as interactive boxplots, shapefile maps and automated reports. Developed with R and Shiny.

HealthPlot

Dashboard of 40 individual datasets and more than 50 graphics divided into 13 categories (health, religion, politics, genre, security, ancestry, immigration, demography, economic, logistic, languages and population) that reflect some aspects of the North American public health..

Rsciencexplorer

This application analyzes more than 12.000 articles and 22.000 tweets obtained through relevant scientific journals (and their twitter accounts). This app was built with R and Shiny.

ScienceNet

Query a PMID publication and retrieve information such as cites network, centrality measure by article…Project using API to the NCBI database.