Financial support through the following projects:
UIDB/00297/2020
UID/MAT/00297/2019
UID/MAT/00297/2013
PEstOE/MAT/UI0297/2014
PEstOE/MAT/UI0297/2011
Mathematical Biology  Mathematics for Health 
Data Knowledge 
Ecology 
Care and Ageing  Big Data 
Epidemiology 

Machine Learning 
Evolution & Genetics 
Projects:

Project: Models of genetic evolution
Description: In this line, we study in detail and in an unified way, several models used in modelling evolutionary dynamics (i.e., Darwinian evolution). In particular, we rigorously study the WrightFisher and the Moran models (finite population stochastic processes), the Kimuraequation (a degenerated partialdifferential equation of driftdiffusion type, supplemented by conservation laws) and the replicator equation (an ordinary differential equation, popular in evolutionary game theory). Several different mathematical theories are used, e.g., stochastic process, theory of Markov chains, partial differential equations of elliptic and parabolic type, game theory etc. Currently, all these models are being reformulated in an unified way using variational formalism (gradient flows).
Methods: Probability  Linear Algebra  Differential Equations
CMA Researchers: Fabio A. C. C. Chalub
Publications:

Project: Plant breeding
Description:
Methods: Robust Statistics  Genetic statistics
CMA Researchers: Vanda Lourenço
Publications:

Project: Integration of HIV in the human genome
Description: Using data from the National Center for Biotechnology Information (NCBI), we determine the preferences of HIV integrations in the human genome.
Methods: Statistical analysis of genetic data  Nonparametric methods  Analysis of variance
CMA Researchers: Elsa Moreira, Inês Sequeira
Publications:

Project: Constitutional chromosomal rearrangements in the human genome
Description: Using the database of Chromosomal Imbalance and Phenotype in Humans using Ensembl Resources (DECIPHER), we determine which are the chromosomal fragile sites more involved in constitutional chromosomal rearrangements.
Methods: Statistical analysis of genetic data  Data display  Descriptive statistics  Linear regression analysis
CMA Researchers: Inês Sequeira, Dora Gomes
Publications: