Francisco Requena

Francisco Requena

PhD Student - Bioinformatics

Institut Imagine.


I am a PhD Student at the Clinical Bioinformatics in the Institut Imagine (Paris). My current work focus on the development of new tools for the interpretation of variants in rare diseases.


  • Human genetics
  • Rare diseases
  • Machine learning
  • Non-coding DNA


  • Msc Bioinformatics, 2019

    Autonomous University of Madrid

  • Msc Translational Research, 2016

    University of Granada

Recent Posts

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.

Poisson distribution applied in genomics

Overview In this post, I will discuss briefly what is the Poisson distribution and describe two examples extracted from research articles in the genomics field. One of them based on the distribution of structural variants across the genome and other about de novo variants in a patient cohort.

Estimating pi value with Monte Carlo simulation

# Load of libraries library(tidyverse) library(sp) library(gganimate) n_simulations <- 3000 df <- tibble( values_x = runif(n_simulations,0,1), values_y = runif(n_simulations,0,1) ) circleFun <- function(center=c(0,0), diameter=1, npoints=100, start=0, end=2) { tt <- seq(start*pi, end*pi, length.




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.


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..


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.


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