Titre : Protéines de filaments intermédiaires

Protéines de filaments intermédiaires : Questions médicales fréquentes

Termes MeSH sélectionnés :

Models, Statistical

Questions fréquentes et termes MeSH associés

Général 1

#1

Erreur lors de la génération.

Veuillez réessayer ultérieurement.
Intermediate Filament Proteins
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Dr Olivier Menir

Contenu validé par Dr Olivier Menir

Expert en Médecine, Optimisation des Parcours de Soins et Révision Médicale


Validation scientifique effectuée le 28/02/2025

Contenu vérifié selon les dernières recommandations médicales

Auteurs principaux

Sarah Köster

9 publications dans cette catégorie

Affiliations :
  • Institute for X-Ray Physics, University of Goettingen, Friedrich-Hund-Platz 1, 37077 Göttingen, Germany.

Sandrine Etienne-Manneville

8 publications dans cette catégorie

Affiliations :
  • Cell Polarity, Migration and Cancer Unit, Institut Pasteur, UMR3691 CNRS, Équipe Labellisée Ligue Contre le Cancer, F-75015 Paris, France. Electronic address: setienne@pasteur.fr.

Charlotta Lorenz

6 publications dans cette catégorie

Affiliations :
  • Institute for X-Ray Physics, University of Göttingen, Friedrich-Hund-Platz 1, 37077 Göttingen, Germany.

Anna V Schepers

5 publications dans cette catégorie

Affiliations :
  • Institute for X-Ray Physics, University of Göttingen, Friedrich-Hund-Platz 1, 37077 Göttingen, Germany.

Rudolf E Leube

4 publications dans cette catégorie

Affiliations :
  • Institute of Molecular and Cellular Anatomy, RWTH Aachen University, Aachen, Germany.

Julia Kraxner

4 publications dans cette catégorie

Affiliations :
  • Max Delbrück Center for Molecular Medicine, Robert-Rössle-Straße 10, D-13125 Berlin, Germany.
  • German Centre for Cardiovascular Research (DZHK), Partner Site Berlin, D-10785 Berlin, Germany.
Publications dans "Protéines de filaments intermédiaires" :

Pierre A Coulombe

3 publications dans cette catégorie

Affiliations :
  • Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI 48109, USA; Department of Dermatology, University of Michigan, Ann Arbor, MI 48109, USA; Rogel Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA. Electronic address: coulombe@umich.edu.
Publications dans "Protéines de filaments intermédiaires" :

Stéphanie Portet

3 publications dans cette catégorie

Affiliations :
  • Department of Mathematics, University of Manitoba, Winnipeg, MB, Canada.

Ohad Medalia

3 publications dans cette catégorie

Affiliations :
  • Department of Biochemistry, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland.
Publications dans "Protéines de filaments intermédiaires" :

Harald Herrmann

3 publications dans cette catégorie

Affiliations :
  • Institute of Neuropathology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.

Puneet Opal

3 publications dans cette catégorie

Affiliations :
  • Ken and Ruth Davee Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
Publications dans "Protéines de filaments intermédiaires" :

Stefan Klumpp

3 publications dans cette catégorie

Affiliations :
  • Institute for Dynamics of Complex Systems, University of Göttingen, Friedrich-Hund-Platz 1, 37077 Göttingen, Germany.
Publications dans "Protéines de filaments intermédiaires" :

Alexander A Dayal

2 publications dans cette catégorie

Affiliations :
  • Institute of Protein Research, Russian Academy of Sciences, Moscow, 119334, Russia.

Alexander A Minin

2 publications dans cette catégorie

Affiliations :
  • Institute of Protein Research, Russian Academy of Sciences, Moscow, 119334, Russia. alexminin@gmail.com.

Hakan Sağsöz

2 publications dans cette catégorie

Affiliations :
  • Department of Histology and Embryology, Faculty of Veterinary Medicine, Dicle University, Diyarbakır, Turkey.
Publications dans "Protéines de filaments intermédiaires" :

J C Dallon

2 publications dans cette catégorie

Affiliations :
  • Department of Mathematics, Brigham Young University, 344 TMCB, Provo, Utah 84602-6539, USA. Electronic address: dallon@math.byu.edu.
Publications dans "Protéines de filaments intermédiaires" :

Cécile Leduc

2 publications dans cette catégorie

Affiliations :
  • Institut Pasteur Paris CNRS UMR3691, Cell Polarity, Migration and Cancer Unit, Equipe Labellisée Ligue Contre le Cancer, 25 rue du Dr Roux, Paris Cedex 15, 75724, France.
Publications dans "Protéines de filaments intermédiaires" :

John E Eriksson

2 publications dans cette catégorie

Affiliations :
  • Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland.
  • Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland.
  • Euro-Bioimaging ERIC, 20520 Turku, Finland.
Publications dans "Protéines de filaments intermédiaires" :

Gerhard Wiche

2 publications dans cette catégorie

Affiliations :
  • Celica BIOMEDICAL, 1000 Ljubljana, Slovenia.
  • Department of Biochemistry and Cell Biology, Max F. Perutz Laboratories, University of Vienna, 1030 Vienna, Austria.
Publications dans "Protéines de filaments intermédiaires" :

Mark Kittisopikul

2 publications dans cette catégorie

Affiliations :
  • Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
Publications dans "Protéines de filaments intermédiaires" :

Sources (10000 au total)

Anatomically Parameterized Statistical Shape Model: Explaining Morphometry Through Statistical Learning.

Statistical shape models (SSMs) are a popular tool to conduct morphological analysis of anatomical structures which is a crucial step in clinical practices. However, shape representations through SSMs... The proposed anatomically parameterized SSM (ANAT[Formula: see text]) is based on learning a linear mapping between shape coefficients (latent space) and selected anatomical parameters (anatomical spa... Anatomical measures of the synthetically generated shapes exhibited realistic statistics. The learned matrices corroborated well with the obtained statistical relationship, while the two SSMs achieved... This study demonstrates the use of anatomical representation for creating anatomically parameterized SSMs and as a result, removes the limited clinical interpretability of standard SSMs.... The proposed models could help analyze differences in relevant bone morphometry between populations, and be integrated in patient-specific pre-surgery planning or in-surgery assessment....

Estimating the Irreducible Uncertainty in Visual Diagnosis: Statistical Modeling of Skill Using Response Models.

For the representative problem of prostate cancer grading, we sought to simultaneously model both the continuous nature of the case spectrum and the decision thresholds of individual pathologists, all... Experts and pathology residents each rated a standardized set of prostate cancer histopathological images on the International Society of Urological Pathologists (ISUP) scale used in clinical practice... The slides were rated by 36 physicians in total: 23 ISUP pathologists and 13 residents. As anticipated, the cases showed a full continuous range of diagnostic severity. Cases ranged along a logit scal... We present a method that allows simultaneous quantification of both the confusability of a particular case and the skill with which raters can distinguish the cases.... The technique generalizes beyond the current example to other clinical situations in which a diagnostician must impose an ordinal rating on a biological spectrum....

Statistical models versus machine learning for competing risks: development and validation of prognostic models.

In health research, several chronic diseases are susceptible to competing risks (CRs). Initially, statistical models (SM) were developed to estimate the cumulative incidence of an event in the presenc... A dataset with 3826 retrospectively collected patients with extremity soft-tissue sarcoma (eSTS) and nine predictors is used to evaluate model-predictive performance in terms of discrimination and cal... Based on the original eSTS data, 100 bootstrapped training datasets are drawn. Performance of the final models is assessed on validation data (left out samples) by employing as measures the Brier scor... Overall, ML techniques are less practical as they require substantial implementation time (data preprocessing, hyperparameter tuning, computational intensity), whereas regression methods can perform w...