scholarly journals Fiber_distance based unsupervised clustering method of MR tractography data

IBRO Reports ◽  
2019 ◽  
Vol 6 ◽  
pp. S524
Author(s):  
Sang-Han Choi ◽  
Young-Bo Kim ◽  
Zang-Hee Cho
Author(s):  
Manabu Kimura ◽  
◽  
Masashi Sugiyama

Recently, statistical dependence measures such as mutual information and kernelized covariance have been successfully applied to clustering. In this paper, we follow this line of research and propose a novel dependence-maximization clustering method based on least-squares mutual information, which is an estimator of a squared-loss variant of mutual information. A notable advantage of the proposed method over existing approaches is that hyperparameters such as kernel parameters and regularization parameters can be objectively optimized based on cross-validation. Thus, subjective manual-tuning of hyperparameters is not necessary in the proposed method, which is a highly useful property in unsupervised clustering scenarios. Through experiments, we illustrate the usefulness of the proposed approach.


2018 ◽  
Vol 12 (7) ◽  
pp. 989-995 ◽  
Author(s):  
Letizia Vivona ◽  
Donato Cascio ◽  
Vincenzo Taormina ◽  
Giuseppe Raso

2020 ◽  
Author(s):  
Anke Van Dijck ◽  
Susana Barbosa ◽  
Patricia Bermudez-Martin ◽  
Olfa Khalfallah ◽  
Cyprien Gilet ◽  
...  

Abstract Background: Fragile X syndrome (FXS) is the most frequent cause of inherited intellectual disability and the most commonly identified monogenic cause of autism. Recent studies have shown that long-term pathological consequences of FXS are not solely confined to the central nervous system (CNS) but rather extend to other physiological dysfunctions in peripheral organs. To gain insights into possible immune dysfunctions in FXS, we profiled a large panel of immune-related biomarkers in the serum of FXS patients and healthy controls. Methods: We have used a sensitive and robust Electro Chemi Luminescence (ECL)-based immunoassay to measure the levels of 52 cytokines in the serum of n=25 FXS patients and n=29 healthy controls. We then used univariate statistics and multivariate analysis, as well as an advanced unsupervised clustering method, to identify combinations of immune-related biomarkers that could discriminate FXS patients from healthy individuals. Results: While the majority of the tested cytokines were present at similar levels in FXS patients and healthy individuals, nine chemokines, CCL2, CCL3, CCL4, CCL11, CCL13, CCL17, CCL22, CCL26 and CXCL10, were present at much lower levels in FXS patients. Using robust regression, we show that six of these biomarkers (CCL2, CCL3, CCL11, CCL22, CCL26 and CXCL10) were negatively associated with FXS diagnosis. Finally, applying the K-sparse unsupervised clustering method to the biomarker dataset allowed for the identification of two subsets of individuals, which essentially matched the FXS and healthy control categories. Conclusions: Our data show that FXS patients exhibit reduced serum levels of several chemokines. This paves the way for further study of immune phenotypes in FXS patients.


2014 ◽  
Vol 14 (2) ◽  
pp. 18-18 ◽  
Author(s):  
J. Otero-Millan ◽  
J. L. A. Castro ◽  
S. L. Macknik ◽  
S. Martinez-Conde

Author(s):  
Alina Martinez-Oropeza ◽  
Marco Antonio Cruz-Chavez ◽  
Martin H. Cruz-Rosales ◽  
Pedro Moreno Bernal ◽  
Jesus Del Carmen Peralta-Abarca

Author(s):  
Letizia Vivona ◽  
Donato Cascio ◽  
Salvatore Bruno ◽  
Alessandro Fauci ◽  
Vincenzo Taormina ◽  
...  

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