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The MAB team (Methods and Algorithms for Bioinformatics) proposes mathematical and algorithmic methods (text and tree algorithms, combinatorial algorithms and optimization, probabilistic modeling, statistical machine learning) to address biological issues such as evolution, phylogeny, comparative genomics, functional annotation of genes and proteins, malaria, HIV, cancer. The software systems developed by MAB are available through the ATGC platform.
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170
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Data structure
Accuracy
Suffix tree
Reads
Génomique comparative
Parsimony
Complexité
Phylogenetic inference
Scalability
Complexity
Genome
Deep sequencing
Algorithms
Gene tree
Periodicity
Genome evolution
Alignment
Motif
Graph
Next Generation Sequencers
Reconciliation
Index
Bioinformatique
Indexation
Coleoptera
Assembly
Dynamic update
Phylogenetic trees
Plasmodium falciparum
Overlap
Greedy
Tandem repeat
Supertree
Phylogeography
Sequence analysis
Transcriptome
Computer simulations
Compatibility
Bayes
Approximation algorithm
Recombination
Transcriptomics
Algorithm
Construction algorithms
Period
Comparative genomics
HMM
Sequence comparison
Consensus
Combinatorics
Phylogeny
Next Generation Sequencing
Superstring
DNA
Trees
Algorithmique
Suffix array
Analyse
Computational biology
Maximum likelihood
Contracted de Bruijn graph
NGS
Duplication
VNTR
Tool
Stringology
Distance methods
Tandem repeats
Evolution
Least-squares
Software
Bioinformatics
Species tree
RNA-seq
MCMC
Pattern matching
Phylogenomics
Correlation
Indexing
Molecular evolution
Phylogenetics
Data structures
Dynamic programming
Cancer
Arabidopsis thaliana
Phylogenetic
Approximation
Alignement
RNA
Border
ChIP-seq
Sequence alignment
Autocorrelation
Automata
Malaria
Genomics
K-mer
Machine learning
Overlap graph
Bayesian inference