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COURS BIOSTATISTIQUE PCEM1 PDF

Pas de nouveaux messages, Cours de statistiques de premiére année PCEM1 Pas de nouveaux messages, [Biostatistique] La Collection Cours Biostatistique. Reconnaissance des diplômes étrangers. L’exercice en France de la médecine et des professions paramédicales est réglementé et les diplômes étrangers ne. activités de recherche présentement en cours dans les milieux des participants PCEM1 et avec la médecine légale en DCEM3. En , un en épidémiologie et en biostatistiques qui sont relativement bien abordées.

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Je trouve en lui un professeur, un guide, un inspirateur, un ami et un proche. Je remercie mes ami-e-s proch s t membres du laboratoire de bioinformatique Golrokh Kiani, Bioststistique Daigle et Ahmed Halioui. Je remercie Vladimir Makarenkov et Anne Bergeron professeur-e-s de la bioinformatique au d’ partement d’informatique. Article 1 chapitre 2: A machine learning approach for viral genome classification. J’ai une contribution majeure dans ce projet.

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Article 2 chapitr 3: An integrative approach to identify hexaploid wheat mirn Aome associated with development and tolerance to abiotic stress. BMC Genomics, 16 1 Articl 3 chapitre 4: ARNs codants et AR s non codants. Les attributs sont connus aussi par les noms dim ensions champs 1 f eatures et variables.

Les attributs quantitatifs discrets ont des valeurs fi nies ou d’ nombrables cont rairement aux attributs continus. Supposons qu’on a N objets x1, x2, Les mesures principal s de position ont: Le mode e t la valeur xi la plus fr ‘ qu nte. Une variable peut avoir un seul mode variable unimodal comme plusieurs variable plurimodal. Avec ces attributs on peut seulement distinguer un objet d,un autre. Finalement, ces mesur s sont ut iles pour identifier les valeurs aberrantes outliers.

Ils sont connus par le nom quartiles. Plusieurs algorithmes d’apprentis age nep uvent pas traiter les val ur continues. L’algorithme Hunt Hunt et al. Ci,D est l’ensemble d’objets de la classe Ci dans D. Selon un princip d statistique d’apprentissag Vapnik et Chervonenkis,Boser et al. L’algorithme Boosting et sa variante AdaBoost de Adaptive Boosting attribuent des poids aux exemples dans chaque bootstrap.

Manipulation des algorithmes d 1 apprentissage. Cette approche est appel’ Stacked generalization ou Stacking. Elles comprennent deux approches basiques: Taux de vrais positifs TVP: Taux d e faux positifs TFP: On l’appelle aussi FP R pour fals e positive rate. Jensen et Bateman Par exemple, triplet-svm Xu t al. MiPred Jiang et al.

Advances in cloning and sequencing technology are yielding a massive number of viral genom s. The classification and annotation of t hese genomes constitute important assets in the discovery of genomic variability, taxonomie characteristics and disease mechanisms.

Existing classification methods are biostatistqiue designed for specifie well-studied family of viruses. Thus, the viral comparative g nomic studi s biostaitstique ben fit from more gen rie, fast and accurate tools for classifying and typing newly sequenced strains of diverse biostatisitque famili s. It simulates, in silico, the r striction digestion of genomic material by diff rent enzymes into fr agments.

It uses two rn trics to construct feature vectors for machin learning algorithms in the classification st ep. The performance of CASTOR, its genericity and robustness could permit to perform novel and accurate large scale virus studies. It is a fundamental practice in different research areas of microbiology yi elding major challenges in comparative genomics.

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Accurate genomic sequence classification and typing could help to enhance the phylogenetics and functional studi s of viruses Van B lkum et al. They also help in determining pathogenicity, developing vaccin s, studying epidemiology and drug resistanc Van Belkum et al. Recent advances in DNA quencing and molecular biology t chniques provid an immense coll ction of genomic information.

Such data volume raises challenges for genetic-based ela sification t chniqu s. Three main approaches have been designed and implemented to classify different types. The first is sequence alignment- based approach which is biostatishique used, e. The second is phylog enetic- based approach.

It is implemented in several tools, e.

REGA de Oliveira et al. Th aim of t hese methods is to place an unknown sequence on an existing phylogen tic tree of a set of ref renee sequenc s.

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Then, either a new phylogenetic tree is inferred or the given sequence is placed in the existing tree. The t hird is alignment-free approach including methods based on nucleotide correlations Liu et al. It transforms s quences or their relationships to feature vectors and then constructs a phylogeny, a statistical model or a machine learning model Vinga et Almeida, ; Bonham-Carter et al.

These methods ar reviewed in Vinga et AlmeidaMantaci et al. Restriction fragment length polymorphism Pcdm1a molecular biology technique Williams,is used to type different virus strains Bernard et al. Several algorithmic approaches have tackled t heoretical and experimental problems related to the restriction enzyme data. However, large scale computational sequence classification based on the RFLP technique is courw yet covered viostatistique literature.

Due to the genetic polymorphism in D A sequences, fragments resulting from enzyme digestions are different in terms of number and length betw en individuals or. A set of restriction enzymes grounds pcrm1 fr agment patt rn signature for each sequence.

Therefore, similar sequences ought to have similar fr agment patterns and thus similar restriction site distributions.

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This a priori knowledg could b used to build a machine learning model where sequences are represented by restriction sit distributions as a feature vector and a class feature corresponding to a taxonomie level genus, species, etc. Our in silico method is independent of the sequence structure or function and is also not organismspecific.

CASTOR is d signed to facilitate t h r use, sharing and reproducibility of sequence classification experirn nts. Like other supervised learning approaches, t h proposed one is divided into two main units Fig. The classifier construction unit builds and trains classification mo dels or classifiers. It r quir a set of reference viral g nomie sequences, their classes and a list of restriction enzyme patterns.

It starts by creating a training set including a group of feature vectors. Th latter is computed from th distribution of th restriction sit patt rns on t h given DNA s qu nees and then refin d by feature election methods.

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A collection of 1 arning ela ifi r are t hen train d and evaluated using fold cross-validation in ord r to choose the best cl as ifier. Th biostatistkque co nd unit prediction unit is intended to predict the classes or annotations of given viral sequences.

The inputs of this unit are a classifi r, a set of D A sequences and the same list of restriction enzyme patterns used to. The kernel is composed of two main biostatistiqque classifier construction and prediction.

White rectangles represent input and output data; grey and curved rectangles represent pro cesses. Prediction unit Dataset creation Create m lcem1 validation datasets: Type II family cleaves cuts D A sequences precisely on each occurrence of t he recognition site.

Then, t he restriction digestion of D A sequences i computationally imulated. In order to build a training set, for a sequence s and enzyme z we compute two metrics representing the distribution biostatisique the digested fragments: Ot her metrics could be easily computed from the fragment digestion to construct the feature vectors Feature selection methods The cle t ion of an optimal ub et of feature improves the learning efficiency and increases t he predictive performance.

Feature selection techniques biostatistiquue t he learning et dimension by pruning irrelevant and redundant features.

Two relevant methods of feature reduction are provided. The first method topattributes ranks the features according to t heir information viostatistique Ben-Bassat, and selects a subset of top-k featur s.

Information gain estimate the mutual information between a feature and t he target clas. The eco nd method correlation uses t he.

The correlation co fficient between two feature ranking vectors u and v of size n is computed as follows: In order to remove one of the two correlated features, two strategies could be used: A fold cross-validation strategy is used to assess the performance of the trained classifiers. Performance measures are weight d according to the number of instanc s and computed for the overall classification. The p rformance measur s are:.

We used Weka dat a mining biostatstique gram to perform the training and the evaluation Hall et al. To include a negative class in t he training sets, two approaches could be used. First, provide manually construct d biostatistiquee class from collect d relevant data. Second, build it with the provided negative class generator.

This generator constructs altered sequences data from a sampling with replac ment of the positive set s – biosgatistique.

To alter the sampled sequ nees, we reshape the RFLP length distribution of t he t raining set by randomly shrinking, expanding or keeping unchanged the length of the sampled sequences. Then, each sequence is randomly shuffled while preserving k-mer biostatisfique Dat asets In t his study, we applied our approach to a wide range of viruses.

We assessed the performance of HPV classification in the genus and species taxonomie levels. At the species lev biostatsitique, we selected only the Alpha HPV genus representing the most abundant and diverse genomes in databases.