scholarly journals S100B gene polymorphisms are associated with the S100B level and Alzheimer’s disease risk by altering the miRNA binding capacity

Aging ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 13954-13967
Author(s):  
Jiafeng Wang ◽  
Yulan Zhou ◽  
Yixia Yang ◽  
Xiang Gao ◽  
Zhibin Liu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gene Polymorphisms ◽  
Disease Risk ◽  
Binding Capacity ◽  
S100b Level

scholarly journals MAPT H1 Haplotype is Associated with Late-Onset Alzheimer’s Disease Risk in APOE ɛ4 Noncarriers: Results from the Dementia Genetics Spanish Consortium

2015 ◽  
Vol 49 (2) ◽  
pp. 343-352 ◽  
Author(s):  
Pau Pastor ◽  
Fermín Moreno ◽  
Jordi Clarimón ◽  
Agustín Ruiz ◽  
Onofre Combarros ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Risk ◽  
Late Onset

scholarly journals A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shaker El-Sappagh ◽  
Jose M. Alonso ◽  
S. M. Riazul Islam ◽  
Ahmad M. Sultan ◽  
Kyung Sup Kwak
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cross Validation ◽  
Disease Risk ◽  
Binary Classification ◽  
Fuzzy Rule ◽  
Large Set ◽  
Detection Model ◽  
Multi Class Classification ◽  
Clinical Measures

AbstractAlzheimer’s disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, research studies often have little effect on clinical practice mainly due to the following reasons: (1) Most studies depend mainly on a single modality, especially neuroimaging; (2) diagnosis and progression detection are usually studied separately as two independent problems; and (3) current studies concentrate mainly on optimizing the performance of complex machine learning models, while disregarding their explainability. As a result, physicians struggle to interpret these models, and feel it is hard to trust them. In this paper, we carefully develop an accurate and interpretable AD diagnosis and progression detection model. This model provides physicians with accurate decisions along with a set of explanations for every decision. Specifically, the model integrates 11 modalities of 1048 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) real-world dataset: 294 cognitively normal, 254 stable mild cognitive impairment (MCI), 232 progressive MCI, and 268 AD. It is actually a two-layer model with random forest (RF) as classifier algorithm. In the first layer, the model carries out a multi-class classification for the early diagnosis of AD patients. In the second layer, the model applies binary classification to detect possible MCI-to-AD progression within three years from a baseline diagnosis. The performance of the model is optimized with key markers selected from a large set of biological and clinical measures. Regarding explainability, we provide, for each layer, global and instance-based explanations of the RF classifier by using the SHapley Additive exPlanations (SHAP) feature attribution framework. In addition, we implement 22 explainers based on decision trees and fuzzy rule-based systems to provide complementary justifications for every RF decision in each layer. Furthermore, these explanations are represented in natural language form to help physicians understand the predictions. The designed model achieves a cross-validation accuracy of 93.95% and an F1-score of 93.94% in the first layer, while it achieves a cross-validation accuracy of 87.08% and an F1-Score of 87.09% in the second layer. The resulting system is not only accurate, but also trustworthy, accountable, and medically applicable, thanks to the provided explanations which are broadly consistent with each other and with the AD medical literature. The proposed system can help to enhance the clinical understanding of AD diagnosis and progression processes by providing detailed insights into the effect of different modalities on the disease risk.

scholarly journals The BIN1 rs744373 Alzheimer's disease risk SNP is associated with faster Aβ‐associated tau accumulation and cognitive decline

2021 ◽  
Author(s):  
Nicolai Franzmeier ◽  
Rik Ossenkoppele ◽  
Matthias Brendel ◽  
Anna Rubinski ◽  
Ruben Smith ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
Disease Risk

scholarly journals Caspase-1 genetic variation is not associated with Alzheimer's disease risk

2010 ◽  
Vol 11 (1) ◽  
Author(s):  
José Luis Vázquez-Higuera ◽  
Eloy Rodríguez-Rodríguez ◽  
Pascual Sánchez-Juan ◽  
Ignacio Mateo ◽  
Ana Pozueta ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Genetic Variation ◽  
Disease Risk ◽  
Caspase 1
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