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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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Number of full texts
502
Number of records
173
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Tags
Periodicity
Period
Sequence comparison
Recombination
Genome
Supertree
Superstring
Consensus
Phylogenetic trees
Construction algorithms
Molecular evolution
Greedy
Border
Reconciliation
Software
Phylogeography
Complexity
Alignement
Alignment
Sequence analysis
Distance methods
Dynamic programming
Index
Suffix array
DNA
Overlap graph
Accuracy
Least-squares
Algorithms
Indexation
Computational biology
Species tree
Automata
Indexing
Computer simulations
Tandem repeat
RNA
Next Generation Sequencers
Tandem repeats
Bayes
Graph
MCMC
Bioinformatique
Data structures
Bioinformatics
Approximation algorithm
Coleoptera
Machine learning
Phylogenetics
Autocorrelation
Overlap
Cancer
Evolution
Genomics
Trees
Algorithmique
Approximation
ChIP-seq
Reads
Genome evolution
Pattern matching
Phylogenomics
K-mer
Gene tree
Combinatorics
Dynamic update
Assembly
Transcriptome
Comparative genomics
Stringology
Phylogenetic inference
Malaria
Complexité
Data structure
Plasmodium falciparum
Parsimony
Algorithm
RNA-seq
Sequence alignment
Motif
Next Generation Sequencing
Génomique comparative
Maximum likelihood
Gene expression
Arabidopsis thaliana
Phylogenetic
Duplication
Phylogeny
Contracted de Bruijn graph
Bayesian inference
Compatibility
Suffix tree
VNTR
NGS
HMM
Scalability
Transcriptomics
Analyse
Deep sequencing
Tool