بیماری آلزایمر ارثی اوتوزومی غالب
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بیماری آلزایمر ارثی اوتوزومی غالب

عنوان فارسی مقاله: بیماری آلزایمر ارثی اوتوزومی غالب: تجزیه و تحلیل زیر گروه های ژنتیکی توسط یادگیری ماشین
عنوان انگلیسی مقاله: Autosomal Dominantly Inherited Alzheimer Disease: Analysis of genetic subgroups by Machine Learning
مجله/کنفرانس: ادغام اطلاعات – Information Fusion
رشته های تحصیلی مرتبط: پزشکی، زیست شناسی
گرایش های تحصیلی مرتبط: مغز و اعصاب، ژنتیک پزشکی، ژنتیک
کلمات کلیدی فارسی: بیماری آلزایمر ارثی غالب، شبکه آلزایمر ارثی غالب، بیماری آلزایمر، تصویربرداری عصبی، یادگیری ماشین
کلمات کلیدی انگلیسی: Dominantly-Inherited Alzheimer’s Disease (DIAD), DIAN, Alzheimer’s Disease (AD), Neuroimaging, Machine Learning
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.inffus.2020.01.001
دانشگاه: Department of Signal Theory, Telematics and Communications, University of Granada, Granada (Spain
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2020
ایمپکت فاکتور: 13.201 در سال 2019
شاخص H_index: 85 در سال 2020
شاخص SJR: 2.238 در سال 2019
شناسه ISSN: 1566-2535
شاخص Quartile (چارک): Q1 در سال 2019
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 57
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: بله
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: دارد
کد محصول: E14245
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


۱٫ Introduction


۲٫ Material & methods


۳٫ Results


۴٫ Discussion & conclusions


CRediT authorship contribution statement


Declaration of Competing Interest


Acknowledgments


Appendix A. Supplementary materials


Research Data


References

نمونه متن انگلیسی مقاله

Abstract


Despite subjects with Dominantly-Inherited Alzheimer’s Disease (DIAD) represent less than 1% of all Alzheimer’s Disease (AD) cases, the Dominantly Inherited Alzheimer Network (DIAN) initiative constitutes a strong impact in the understanding of AD disease course with special emphasis on the presyptomatic disease phase. Until now, the 3 genes involved in DIAD pathogenesis (PSEN1, PSEN2 and APP) have been commonly merged into one group (Mutation Carriers, MC) and studied using conventional statistical analysis. Comparisons between groups using null-hypothesis testing or longitudinal regression procedures, such as the linear-mixed-effects models, have been assessed in the extant literature. Within this context, the work presented here performs a comparison between different groups of subjects by considering the 3 genes, either jointly or separately, and using tools based on Machine Learning (ML). This involves a feature selection step which makes use of ANOVA followed by Principal Component Analysis (PCA) to determine which features would be realiable for further comparison purposes. Then, the selected predictors are classified using a Support-Vector-Machine (SVM) in a nested k-Fold cross-validation resulting in maximum classification rates of 72–۷۴% using PiB PET features, specially when comparing asymptomatic Non-Carriers (NC) subjects with asymptomatic PSEN1 Mutation-Carriers (PSEN1-MC). Results obtained from these experiments led to the idea that PSEN1-MC might be considered as a mixture of two different subgroups including: a first group whose patterns were very close to NC subjects, and a second group much more different in terms of imaging patterns. Thus, using a k-Means clustering algorithm it was determined both subgroups and a new classification scenario was conducted to validate this process. The comparison between each subgroup vs. NC subjects resulted in classification rates around 80% underscoring the importance of considering DIAN as an heterogeneous entity.


Introduction


Alzheimer’s Disease (AD) is neuropathologically defined by the presence of amyloid-β (Aβ)-plaques and by neurofibrillary tangles associated with a suggestive clinical phenotype [1, 2, 3]. Clinically AD is characterized by a progressive loss of memory and other neuropsychiatric changes such as decline in executive functioning and behavioral changes [4, 5]. Since the development of a theoretical model of biomarker changes for AD [6], multiple longitudinal studies about AD have tried to find the exact triggers that could explain the prognosis and evolution of the disease. Clinicopathologic evidence suggests that pathological changes leading to AD such as deposition of Aβ-plaques begin many years prior to onset of cognitive symptoms [7, 8, 9, 10], but it still awaits for further empirical validation. In addition to this, as some more recent works point out, the nature of AD might be mistakenly described until now as different genetic alterations, which are causing the same disease, are expressing themselves through different triggers [11, 12, 13, 14, 3, 15]. Dominantly Inherited Alzheimers Disease (DIAD) only represent about 1% of all AD cases, but it has a marked importance for AD research [16]. This type of AD is caused by known mutations in the Amyloid Precursor Protein (APP) [17], Presenilin-1 (PSEN1) [18, 3] (most frequently found), or Presenilin 2 (PSEN2) [19] genes. DIAD is quite similar to the more common Late Onset AD (LOAD) in many features including clinical presentation and disease course [20, 21, 22, 23, 3, 24]. In this sense, the main difference between DIAD and LOAD is in the age at onset, family history and co-pathologies [25].

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