scholarly journals Machine learning identifies candidates for drug repurposing in Alzheimer’s disease

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Steve Rodriguez ◽  
Clemens Hug ◽  
Petar Todorov ◽  
Nienke Moret ◽  
Sarah A. Boswell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Molecular Mechanisms ◽  
Drug Repurposing ◽  
Braak Stage ◽  
Learning Framework ◽  
Negative Results ◽  
Human Neural Cell ◽  
Ranked List

AbstractClinical trials of novel therapeutics for Alzheimer’s Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA) for another indication is a more rapid and less expensive option. We present DRIAD (Drug Repurposing In AD), a machine learning framework that quantifies potential associations between the pathology of AD severity (the Braak stage) and molecular mechanisms as encoded in lists of gene names. DRIAD is applied to lists of genes arising from perturbations in differentiated human neural cell cultures by 80 FDA-approved and clinically tested drugs, producing a ranked list of possible repurposing candidates. Top-scoring drugs are inspected for common trends among their targets. We propose that the DRIAD method can be used to nominate drugs that, after additional validation and identification of relevant pharmacodynamic biomarker(s), could be readily evaluated in a clinical trial.

scholarly journals Machine Learning Identifies Novel Candidates for Drug Repurposing in Alzheimer’s Disease

2020 ◽  
Author(s):  
Steve Rodriguez ◽  
Clemens Hug ◽  
Petar Todorov ◽  
Nienke Moret ◽  
Sarah A. Boswell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Molecular Mechanisms ◽  
Drug Repurposing ◽  
Molecular Entity ◽  
Braak Stage ◽  
Negative Results ◽  
Innate Immune Signaling ◽  
And Function

AbstractClinical trials of novel therapeutics for Alzheimer’s Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA) for another indication is a more rapid and less expensive option. Repurposing can yield a useful therapeutic and also accelerate proof of concept studies that ultimately lead to a new molecular entity. We present a novel machine learning framework, DRIAD (Drug Repurposing In AD), that quantifies potential associations between the pathology of AD severity (the Braak stage) and molecular mechanisms as encoded in lists of gene names. DRIAD was validated on gene lists known to be associated with AD from other studies and subsequently applied to evaluate lists of genes arising from perturbations in differentiated human neural cell cultures by 80 FDA-approved and clinically tested drugs, producing a ranked list of possible repurposing candidates. Top-scoring drugs were inspected for common trends among their nominal molecular targets and their “off-targets”, revealing a high prevalence of kinases from the Janus (JAK), Unc-51-like (ULK) and NIMA-related (NEK) families. These kinase families are known to modulate pathways related to innate immune signaling, autophagy, and microtubule formation and function, suggesting possible disease-modifying mechanisms of action. We propose that the DRIAD method can be used to nominate drugs that, after additional validation and identification of relevant pharmacodynamic biomarker(s), could be evaluated in a clinical trial.

scholarly journals Epidemiological pathology of Aβ deposition in the ageing brain in CFAS: addition of multiple Aβ-derived measures does not improve dementia assessment using logistic regression and machine learning approaches

2019 ◽  
Vol 7 (1) ◽  
Author(s):  
S. B. Wharton ◽  
◽  
D. Wang ◽  
C. Parikh ◽  
F. E. Matthews ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Logistic Regression ◽  
Neuritic Plaque ◽  
Type I ◽  
Ageing Population ◽  
Braak Stage ◽  
Age Related ◽  
Highly Correlated

AbstractAβ-amyloid deposition is a key feature of Alzheimer’s disease, but Consortium to Establish a Registry for Alzheimer's Disease (CERAD) assessment, based on neuritic plaque density, shows a limited relationships to dementia. Thal phase is based on a neuroanatomical hierarchy of Aβ-deposition, and in combination with Braak neurofibrillary tangle staging also allows derivation of primary age-related tauopathy (PART). We sought to determine whether Thal Aβ phase predicts dementia better than CERAD in a population-representative cohort (n = 186) derived from the Cognitive Function and Ageing Study (CFAS). Cerebral amyloid angiopathy (CAA) was quantitied as the number of neuroanatomical areas involved and cases meeting criteria for PART were defined to determine if they are a distinct pathological group within the ageing population. Agreement with the Thal scheme was excellent. In univariate analysis Thal phase performed less well as a predictor of dementia than CERAD, Braak or CAA. Logistic regression, decision tree and linear discriminant analysis were performed for multivariable analysis, with similar results. Thal phase did not provide a better explanation of dementia than CERAD, and there was no additional benefit to including more than one assessment of Aβ in the model. Number of areas involved by CAA was highly correlated with assessment based on a severity score (p < 0.001). The presence of capillary involvement (CAA type I) was associated with higher Thal phase and Braak stage (p < 0.001). CAA was not associated with microinfarcts (p = 0.1). Cases satisfying pathological criteria for PART were present at a frequency of 10.2% but were not older and did not have a higher likelihood of dementia than a comparison group of individuals with similar Braak stage but with more Aβ. They also did not have higher hippocampal-tau stage, although PART was weakly associated with increased presence of thorn-shaped astrocytes (p = 0.048), suggesting common age-related mechanisms. Thal phase is highly applicable in a population-representative setting and allows definition of pathological subgroups, such as PART. Thal phase, plaque density, and extent and type of CAA measure different aspects of Aβ pathology, but addition of more than one Aβ measure does not improve dementia prediction, probably because these variables are highly correlated. Machine learning predictions reveal the importance of combining neuropathological measurements for the assessment of dementia.

scholarly journals Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data

2021 ◽  
Author(s):  
Ziyi Li ◽  
Xiaoqian Jiang ◽  
Yizhuo Wang ◽  
Yejin Kim
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Biomarker Discovery ◽  
Treatment Strategies ◽  
Drug Repurposing ◽  
Modern Technology ◽  
Disease Classification ◽  
Future Research ◽  
New Biomarkers

Alzheimer's disease (AD) remains a devastating neurodegenerative disease with few preventive or curative treatments available. Modern technology developments of high-throughput omics platforms and imaging equipment provide unprecedented opportunities to study the etiology and progression of this disease. Meanwhile, the vast amount of data from various modalities, such as genetics, proteomics, transcriptomics, and imaging, as well as clinical features impose great challenges in data integration and analysis. Machine learning (ML) methods offer novel techniques to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers. These directions have the potential to help us better manage the disease progression and develop novel treatment strategies. This mini-review paper summarizes different ML methods that have been applied to study AD using single-platform or multi-modal data. We review the current state of ML applications for five key directions of AD research: disease classification, drug repurposing, subtyping, progression prediction, and biomarker discovery. This summary provides insights about the current research status of ML-based AD research and highlights potential directions for future research.

A Parallel Machine Learning Framework for Detecting Alzheimer’s Disease

2021 ◽  
pp. 423-432
Author(s):  
Sean A. Knox ◽  
Tianhua Chen ◽  
Pan Su ◽  
Grigoris Antoniou
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Parallel Machine ◽  
Learning Framework

scholarly journals A MACHINE LEARNING FRAMEWORK FOR ASSESSMENT OF COGNITIVE AND FUNCTIONAL IMPAIRMENTS IN ALZHEIMER’S DISEASE: DATA PREPROCESSING AND ANALYSIS

2020 ◽  
pp. 1-8
Author(s):  
N. Vinutha ◽  
S. Pattar ◽  
S. Sharma ◽  
P. D. Shenoy ◽  
K.R. Venugopal
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Imbalanced Data ◽  
Functional Domain ◽  
Functional Impairments ◽  
Functional Activities ◽  
Learning Framework ◽  
Improved Performance ◽  
Aided Diagnosis

The neuropsychological scores and Functional Activities Questionnaire (FAQ) are significant to measure the cognitive and functional domain of the patients affected by the Alzheimer’s Disease. Further, there are standardized dataset available today that are curated from several centers across the globe that aid in development of Computer Aided Diagnosis tools. However, there are numerous clinical tests to measure these scores that lead to a challenging task for their assessment in diagnosis. Also, the datasets suffer from common missing and imbalanced data issues. In this paper, we propose a machine learning based framework to overcome these issues. Empirical results demonstrate that improved performance of Genetic Algorithm is obtained for the neuropsychological scores after Miss Forest Imputation and for FAQ scores is obtained after subjecting it to the Synthetic Minority Oversampling Technique.

scholarly journals Predictive Modeling of Multiplex Chemical Phenomics for Novel Cells and Patients: Applied to Personalized Alzheimer's Disease Drug Repurposing

2021 ◽  
Author(s):  
Qiao Liu ◽  
Yue Qiu ◽  
Lei Xie
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
State Of The Art ◽  
Drug Repurposing ◽  
Response Curves ◽  
Training Strategy ◽  
Learning Framework ◽  
Teacher Student ◽  
Compound Screening ◽  
Student Training

Chemical phenomics which measures multiplex chemical-induced phenotypic response of cells or patients, particularly dose-dependent transcriptomics and drug-response curves, provides new opportunities for in silico mechanism-driven phenotype-based drug discovery. However, state-of-the-art computational methods only focus on predicting a single phenotypic readout and are less successful in screening compounds for novel cells or individual patients. We designed a new deep learning model, MultiDCP, to enable high-throughput compound screening based on multiplex chemical phenomics for the first time, and further expand the scope of chemical phenomics to unexplored cells and patients. The novelties of MultiDCP lie in a multi-task learning framework with a novel knowledge-driven autoencoder to integrate incoherent labeled and unlabeled omics data, and a teacher-student training strategy to exploit unreliable data. MultiDCP significantly outperforms the state-of-the-art for novel cell lines. The predicted chemical transcriptomics demonstrate a stronger predictive power than noisy experimental data for downstream tasks. We applied MultiDCP to repurpose individualized drugs for Alzheimer's disease, suggesting that MultiDCP is a potentially powerful tool for personalized medicine.

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