76 research outputs found

    NetTurnP – Neural Network Prediction of Beta-turns by Use of Evolutionary Information and Predicted Protein Sequence Features

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    UNLABELLED: ÎČ-turns are the most common type of non-repetitive structures, and constitute on average 25% of the amino acids in proteins. The formation of ÎČ-turns plays an important role in protein folding, protein stability and molecular recognition processes. In this work we present the neural network method NetTurnP, for prediction of two-class ÎČ-turns and prediction of the individual ÎČ-turn types, by use of evolutionary information and predicted protein sequence features. It has been evaluated against a commonly used dataset BT426, and achieves a Matthews correlation coefficient of 0.50, which is the highest reported performance on a two-class prediction of ÎČ-turn and not-ÎČ-turn. Furthermore NetTurnP shows improved performance on some of the specific ÎČ-turn types. In the present work, neural network methods have been trained to predict ÎČ-turn or not and individual ÎČ-turn types from the primary amino acid sequence. The individual ÎČ-turn types I, I', II, II', VIII, VIa1, VIa2, VIba and IV have been predicted based on classifications by PROMOTIF, and the two-class prediction of ÎČ-turn or not is a superset comprised of all ÎČ-turn types. The performance is evaluated using a golden set of non-homologous sequences known as BT426. Our two-class prediction method achieves a performance of: MCC=0.50, Qtotal=82.1%, sensitivity=75.6%, PPV=68.8% and AUC=0.864. We have compared our performance to eleven other prediction methods that obtain Matthews correlation coefficients in the range of 0.17-0.47. For the type specific ÎČ-turn predictions, only type I and II can be predicted with reasonable Matthews correlation coefficients, where we obtain performance values of 0.36 and 0.31, respectively. CONCLUSION: The NetTurnP method has been implemented as a webserver, which is freely available at http://www.cbs.dtu.dk/services/NetTurnP/. NetTurnP is the only available webserver that allows submission of multiple sequences

    Figures du victimaire face à la pandémie

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    Dans la prĂ©sente communication, j’aborde le concept du victimaire et en fait des figures « en cinq peaux » par la notion centrale du « pharmakos » Ă©clairĂ©e par la lumiĂšre des thĂ©ories DĂ©ridiennes et Stiegleriennes dĂ©constructivistes. Il s’agit d’interroger la dimension versatile de crĂ©ation du « pharmakos » comme stratĂ©gies des gouvernances et de rĂ©flĂ©chir aux processus constitutifs des figures victimaires pour s’en dĂ©prendre
In this communication, I approach the concept of the victim and make figures of it into five skins by the central notion of "pharmakos" illuminated by the light of DĂ©ridiennes and Stiegleriennes deconstructivist theories. I wonder about the versatile dimension of creation of "pharmakos" as governance strategies. I reflect on the constitutive processes of victim figures to get rid of them and get rid of them by themselves

    ''DĂ©corporĂ©itĂ© du bouc – Ă©missaire apsychique, dĂ©solĂ© et dĂ©socialisĂ©''.

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    Du concept du victimaire

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    Axiomatique victimaire : une pratique du soi victimaire ?

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    « LĂ  oĂč l’ñme prĂ©tend s’uniïŹer, lĂ  oĂč le Moi s’invente une identitĂ© ou une cohĂ©rence, le gĂ©nĂ©alogiste part Ă  la recherche du commencement des commencements innombrables qui laissent ce soupçon de couleur, cette marque presque effacĂ©e qui ne saurait tromper un Ɠil un peu historique ; l’analyse de la provenance permet de dissocier le Moi et de faire pulluler, aux lieux et places de sa synthĂšse vide, mille Ă©vĂ©nements maintenant perdus » (Foucault, 1971, p. 141).IntroductionL’objet de cet article n’

    Axiomique victimaire. Une pratique du soi victimaire  ?

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