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le (26m54s)

Comprendre les origines de la pensée : du big data sur des images cérébrales

Comment reconnaissons-nous quelqu’un  ? Comment le cerveau manipule-t-il les nombres ? Les neurosciences cognitives cherchent à comprendre comment les  pensées et processus mentaux émergent des millions de neurones de notre cerveau. L’accumulation d’images de fonctionnement du cerveau fournit des données uniques pour cette recherche. Mais des grosses données ne suffisent pas à faire avancer la compréhension du cerveau. Gaël Varoquaux nous explique comment reformuler des questions de sciences cognitives de façon formelle, propices à être résolues par un ordinateur; comment cela conduit à développer ...
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Conférences

le (33m0s)

Les données : profiter du passage d'un ensemble de points à un réseau

Associations, gouvernements, industries, milieu académique, journalistes, citoyens : nous sommes tous producteurs et consommateurs de données. La quantité de données à laquelle nous avons accès d’un simple clic est sans précédent. Les moteurs de recherche actuels nous permettent souvent de localiser des documents pertinents par rapport à une requête : comment aller plus loin ? Comment directement répondre à des requêtes plus précises, plus structurées, qu’une recherche par mots-clés ? Comment recouper, combiner des sources différentes ? Comment prendre en compte des ...
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le (5m25s)

1.1. The cell, atom of the living world

Welcome to this introduction to bioinformatics. We will speak of genomes and algorithms. More specifically, we will see how genetic information can be analysed by algorithms. In these five weeks to come, we will see first, what are these genomic texts, we will try to analyse using algorithms and programs. We will then speak of genes and proteins. Proteins being coded by genes. We will study and design algorithms to predict genes on the DNA sequences or genomic texts. We will study, more deeply, an algorithm to compare genomic sequences. And we will use this algorithm to reconstruct phylogenetic trees that is to say the evolution of species over time. During this ...
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le (4m53s)

1.2. At the heart of the cell: the DNA macromolecule

During the last session, we saw how at the heart of the cell there's DNA in the nucleus, sometimes of cells, or directly in the cytoplasm of the bacteria. The DNA is what we call a macromolecule, that is a very long molecule. It's Avery, in 1944, who discovered that the DNA was the support of genetic information. But the scientists who are most well-known for DNA are Francis Crick and James Watson who discovered together, with Maurice Wilkins and Rosalind Franklin, in 1953, the structure of DNA, the famous double helix, the two strands. Here are Crick and Watson explaining on a very crude wire model far away ...
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le (7m22s)

1.3. DNA codes for genetic information

Remember at the heart of any cell,there is this very long molecule which is called a macromolecule for this reason, which is the DNA molecule. Now we will see that DNA molecules support what is called the "genetic information". So, DNAcodes for genetic information. How? If you consider this doublestrand molecule, DNA molecule, you remember that on each strandof the molecule, there is a succession of nucleotides. You can follow these nucleotides and write their name or moreexactly the initial of their name. And you will get what we call the sequence". Look: C, T, A and so on. The process by which you obtain this sequence of characters of ...
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le (5m49s)

1.4. What is an algorithm?

We have seen that a genomic textcan be indeed a very long sequence of characters. And to interpret this sequence of characters, we will need to use computers. Using computers means writing program. Writing program means designing first algorithm. So, let's see what an algorithm is. An algorithm is a series of operationsto be executed by a computer, but maybe also executed by ahuman, for solving a problem.  In the first algorithm we will study in this session and next one, the problem will be to count the number of different of the four different nucleotides which appeared in the sequence. It's a sequence of operations. You may say that in ...
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le (5m11s)

1.5. Counting nucleotides

In this session, don't panic. We will design our first algorithm. This algorithm is forcounting nucleotides. The idea here is that as an input,you have a sequence of nucleotides, of bases, of letters, of characters which ends with a star symbol, here. And, you want to count the number of A,C, G and T, and then the frequencies. To write an algorithm in thispseudo code language, you need first to declare on which objector variables you will work. Here, we declare severalinteger variables. What does it mean integer variables? That is a variable, the value of which can be an integer: 1, 2, 3, minus 9 and so on. So, integer ...
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le (4m29s)

1.6. GC and AT contents of DNA sequence

We have designed our first algorithmfor counting nucleotides. Remember, what we have writtenin pseudo code is first declaration of variables. We have several integer variables that are variables which cantake as a value an integer. One, two, three minus five and so on. We have the sequence of characters we want to interpret, declare as a character string oflengths and define. Then we have the initializationof our different variables. This symbol is a symbol for assignment, it means that zero becomes the value of total nb, nbT and soon and so on and here we say: index takes the value one. It means that we position at the beginning of ...
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le (6m7s)

1.7. DNA walk

We will now design a more graphical algorithm which is called "the DNA walk". We shall see what does it mean "DNA walk". Walk on to DNA. Something like that, yes. But first, just have a look again at the typical, also quite short sequence of DNA, a long text offour letters: A, C, G, T, T and so on. When the first sequence of DNA were obtained, the idea of using computers very quickly emerged but people didn't know exactly what to do with this sequence of characters. Again, there is a meaning behind the sequence because it is genetic information. It means it is the information ...
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le (6m25s)

1.8. Compressing the DNA walk

We have written the algorithm for the circle DNA walk. Just a precision here: the kind of drawing we get has nothing to do with the physical drawing of the DNA molecule. It is a symbolic representation. It is a way of representing the information content of the sequence as a drawing. Remember that the problem of the algorithm we designed is that it supposes the capacity of drawing several millions or billions of segments on the screen. This is not feasible. No screen will be large enough for that. So, how can we deal with this hardware constraint? Compression is the answer. Let's see that in more details. Remember, for each ...
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