scholarly journals Identification of genomic biomarkers with machine learning for early and differential diagnosis of late‐onset Alzheimer’s disease (LOAD)

2020 ◽  
Vol 16 (S2) ◽  
Author(s):  
Onur Erdoğan ◽  
Mert Esme ◽  
Cafer Balci ◽  
Sevda Rafatov ◽  
Mustafa Cankurtaran ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Differential Diagnosis ◽  
Late Onset ◽  
Genomic Biomarkers

scholarly journals A Comprehensive Machine Learning Framework for the Exact Prediction of the Age of Onset in Familial and Sporadic Alzheimer’s Disease

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 887
Author(s):  
Jorge I. Vélez ◽  
Luiggi A. Samper ◽  
Mauricio Arcos-Holzinger ◽  
Lady G. Espinosa ◽  
Mario A. Isaza-Ruget ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Mean Squared Error ◽  
Age Of Onset ◽  
Late Onset ◽  
Training Data ◽  
Therapeutic Interventions ◽  
Data Set ◽  
Unseen Data

Machine learning (ML) algorithms are widely used to develop predictive frameworks. Accurate prediction of Alzheimer’s disease (AD) age of onset (ADAOO) is crucial to investigate potential treatments, follow-up, and therapeutic interventions. Although genetic and non-genetic factors affecting ADAOO were elucidated by other research groups and ours, the comprehensive and sequential application of ML to provide an exact estimation of the actual ADAOO, instead of a high-confidence-interval ADAOO that may fall, remains to be explored. Here, we assessed the performance of ML algorithms for predicting ADAOO using two AD cohorts with early-onset familial AD and with late-onset sporadic AD, combining genetic and demographic variables. Performance of ML algorithms was assessed using the root mean squared error (RMSE), the R-squared (R2), and the mean absolute error (MAE) with a 10-fold cross-validation procedure. For predicting ADAOO in familial AD, boosting-based ML algorithms performed the best. In the sporadic cohort, boosting-based ML algorithms performed best in the training data set, while regularization methods best performed for unseen data. ML algorithms represent a feasible alternative to accurately predict ADAOO with little human intervention. Future studies may include predicting the speed of cognitive decline in our cohorts using ML.

scholarly journals Machine learning methods applied to genotyping data capture interactions between single nucleotide variants in late onset Alzheimer's disease

2021 ◽  
Author(s):  
Magdalena Arnal Segura ◽  
Dietmar Fernandez ◽  
Claudia Giambartolomei ◽  
Giorgio Bini ◽  
Eleftherios Samaras ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Late Onset ◽  
Hot Spot ◽  
Association Studies ◽  
Machine Learning Algorithms ◽  
Genome Wide Association Studies ◽  
Single Nucleotide Variants ◽  
Single Nucleotide

INTRODUCTION Genome-wide association studies (GWAS) in late onset Alzheimer's disease (LOAD) provide lists of individual genetic determinants. However, GWAS are not good at capturing the synergistic effects among multiple genetic variants and lack good specificity. METHODS We applied tree-based machine learning algorithms (MLs) to discriminate LOAD (> 700 individuals) and age-matched unaffected subjects using single nucleotide variants (SNVs) from AD studies, obtaining specific genomic profiles with the prioritized SNVs. RESULTS The MLs prioritized a set of SNVs located in close proximity genes PVRL2, TOMM40, APOE and APOC1. The captured genomic profiles in this region showed a clear interaction between rs405509 and rs1160985. Additionally, rs405509 located in APOE promoter interacts with rs429358 among others, seemingly neutralizing their predisposing effect. Interactions are characterized by their association with specific comorbidities and the presence of eQTL and sQTLs. DISCUSSION Our approach efficiently discriminates LOAD from controls, capturing genomic profiles defined by interactions among SNVs in a hot-spot region.

scholarly journals Identifying and ranking potential driver genes of Alzheimer’s disease using multiview evidence aggregation

2019 ◽  
Vol 35 (14) ◽  
pp. i568-i576 ◽  
Author(s):  
Sumit Mukherjee ◽  
Thanneer M Perumal ◽  
Kenneth Daily ◽  
Solveig K Sieberts ◽  
Larsson Omberg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Genome Wide Association Study ◽  
Learning Algorithm ◽  
Late Onset ◽  
External Validation ◽  
Driver Genes ◽  
Feature Sets ◽  
Ranking Scheme

Abstract Motivation Late onset Alzheimer’s disease is currently a disease with no known effective treatment options. To better understand disease, new multi-omic data-sets have recently been generated with the goal of identifying molecular causes of disease. However, most analytic studies using these datasets focus on uni-modal analysis of the data. Here, we propose a data driven approach to integrate multiple data types and analytic outcomes to aggregate evidences to support the hypothesis that a gene is a genetic driver of the disease. The main algorithmic contributions of our article are: (i) a general machine learning framework to learn the key characteristics of a few known driver genes from multiple feature sets and identifying other potential driver genes which have similar feature representations, and (ii) A flexible ranking scheme with the ability to integrate external validation in the form of Genome Wide Association Study summary statistics. While we currently focus on demonstrating the effectiveness of the approach using different analytic outcomes from RNA-Seq studies, this method is easily generalizable to other data modalities and analysis types. Results We demonstrate the utility of our machine learning algorithm on two benchmark multiview datasets by significantly outperforming the baseline approaches in predicting missing labels. We then use the algorithm to predict and rank potential drivers of Alzheimer’s. We show that our ranked genes show a significant enrichment for single nucleotide polymorphisms associated with Alzheimer’s and are enriched in pathways that have been previously associated with the disease. Availability and implementation Source code and link to all feature sets is available at https://github.com/Sage-Bionetworks/EvidenceAggregatedDriverRanking.

scholarly journals Late-onset attention-deficit/hyperactivity disorder as a differential diagnosis of dementia: a case report

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Hiroyuki Sasaki ◽  
Tadashi Jono ◽  
Ryuji Fukuhara ◽  
Seiji Yuki ◽  
Tomohisa Ishikawa ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Attention Deficit Hyperactivity Disorder ◽  
Differential Diagnosis ◽  
Attention Deficit ◽  
Early Onset ◽  
Late Onset ◽  
Her Family ◽  
Hyperactivity Disorder ◽  
Early Onset Alzheimer’S Disease

Abstract Background Although adult attention-deficit/hyperactivity disorder has recently gained increased attention, few reports on attention-deficit/hyperactivity disorder in the pre-elderly or elderly have been published. Here, we present the case of a patient with attention-deficit/hyperactivity disorder who gradually developed dementia-like symptoms as she aged, which initially made her condition difficult to distinguish from early onset Alzheimer’s disease. This report illustrates that some types of attention-deficit/hyperactivity disorder may be misdiagnosed as dementia. Case presentation The patient was a 58-year-old woman. Although she presented with a tendency for inattentiveness and forgetfulness since childhood, she did not have a history of psychiatric disorders prior to consultation. Around the age of 52 years, her inattentiveness and forgetfulness gradually progressed, and at 57 years of age, she became inattentive and forgetful that it interfered with her work and daily life. For example, she forgot meetings with important clients and transferred money to the wrong bank account; these failures resulted in poor management of her company. At home, she experienced increasing difficulties with remembering prior commitments with her family and misplacing items, which her family members noticed. With the encouragement of her family and employees, who worried that she was suffering from dementia, she visited our memory clinic, whereby she was suspected of having early onset Alzheimer’s disease. However, neuropsychological tests and brain imaging evaluations did not reveal any significant abnormalities. After dismissing various possible diagnoses, including dementia, other organic diseases, mood disorders, and delirium, we diagnosed her with attention-deficit/hyperactivity disorder. Treatment with 18 mg of methylphenidate was initiated, and significant improvements in her symptoms were observed within a few days; for example, she stopped losing her things, was able to concentrate for long durations, and could complete more tasks than she could before treatment. Since initiating treatment, she has returned to work and has been able to perform her daily activities without difficulty. Conclusions This case supports that some patients with late-onset attention-deficit/hyperactivity disorder may gradually develop dementia-like symptoms during the pre-elderly and elderly stages of life. Therefore, clinicians should consider late-onset attention-deficit/hyperactivity disorder as a differential diagnosis of some types of dementias.

scholarly journals Benchmarking machine learning models for late-onset alzheimer’s disease prediction from genomic data

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Javier De Velasco Oriol ◽  
Edgar E. Vallejo ◽  
Karol Estrada ◽  
José Gerardo Taméz Peña ◽  
The Alzheimer’s Disease Neuroimaging Initiative
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Late Onset ◽  
Classification Performance ◽  
Learning Models ◽  
Machine Learning Methods ◽  
Machine Learning Model ◽  
Cognitive Therapies ◽  
Machine Learning Models

Abstract Background Late-Onset Alzheimer’s Disease (LOAD) is a leading form of dementia. There is no effective cure for LOAD, leaving the treatment efforts to depend on preventive cognitive therapies, which stand to benefit from the timely estimation of the risk of developing the disease. Fortunately, a growing number of Machine Learning methods that are well positioned to address this challenge are becoming available. Results We conducted systematic comparisons of representative Machine Learning models for predicting LOAD from genetic variation data provided by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Our experimental results demonstrate that the classification performance of the best models tested yielded ∼72% of area under the ROC curve. Conclusions Machine learning models are promising alternatives for estimating the genetic risk of LOAD. Systematic machine learning model selection also provides the opportunity to identify new genetic markers potentially associated with the disease.

Picture Recognition Errors in the Differential Diagnosis of Alzheimer’s Disease

2019 ◽  
Vol 30 (3) ◽  
pp. 157-168
Author(s):  
Helmut Hildebrandt ◽  
Jana Schill ◽  
Jana Bördgen ◽  
Andreas Kastrup ◽  
Paul Eling
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Differential Diagnosis ◽  
Clinical Diagnosis ◽  
Response Bias ◽  
Recognition Task ◽  
Discriminatory Power ◽  
False Alarms ◽  
Picture Recognition ◽  
Recognition Errors

Abstract. This article explores the possibility of differentiating between patients suffering from Alzheimer’s disease (AD) and patients with other kinds of dementia by focusing on false alarms (FAs) on a picture recognition task (PRT). In Study 1, we compared AD and non-AD patients on the PRT and found that FAs discriminate well between these groups. Study 2 served to improve the discriminatory power of the FA score on the picture recognition task by adding associated pairs. Here, too, the FA score differentiated well between AD and non-AD patients, though the discriminatory power did not improve. The findings suggest that AD patients show a liberal response bias. Taken together, these studies suggest that FAs in picture recognition are of major importance for the clinical diagnosis of AD.

Familial Patterns of Risk in Very Late-Onset Alzheimer's Disease

2003 ◽  
Author(s):  
J. M. Silverman ◽  
C. J. Smith ◽  
D. B. Marin ◽  
R. C. Mohs ◽  
C. B. Propper
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Late Onset ◽  
Familial Patterns

Exploring the Role of Aggregated Proteomes in the Pathogenesis of Alzheimer’s Disease

2020 ◽  
Vol 21 (12) ◽  
pp. 1164-1173
Author(s):  
Siju Ellickal Narayanan ◽  
Nikhila Sekhar ◽  
Rajalakshmi Ganesan Rajamma ◽  
Akash Marathakam ◽  
Abdullah Al Mamun ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Amyloid Precursor Protein ◽  
Early Onset ◽  
Late Onset ◽  
Precursor Protein ◽  
Brain Disorder ◽  
Amyloid Aβ ◽  
T Tau

: Alzheimer’s disease (AD) is a progressive brain disorder and one of the most common causes of dementia and death. AD can be of two types; early-onset and late-onset, where late-onset AD occurs sporadically while early-onset AD results from a mutation in any of the three genes that include amyloid precursor protein (APP), presenilin 1 (PSEN 1) and presenilin 2 (PSEN 2). Biologically, AD is defined by the presence of the distinct neuropathological profile that consists of the extracellular β-amyloid (Aβ) deposition in the form of diffuse neuritic plaques, intraneuronal neurofibrillary tangles (NFTs) and neuropil threads; in dystrophic neuritis, consisting of aggregated hyperphosphorylated tau protein. Elevated levels of (Aβ), total tau (t-tau) and phosphorylated tau (ptau) in cerebrospinal fluid (CSF) have become an important biomarker for the identification of this neurodegenerative disease. The aggregation of Aβ peptide derived from amyloid precursor protein initiates a series of events that involve inflammation, tau hyperphosphorylation and its deposition, in addition to synaptic dysfunction and neurodegeneration, ultimately resulting in dementia. The current review focuses on the role of proteomes in the pathogenesis of AD.

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