scholarly journals Identification of marker genes in Alzheimer's disease using a machine-learning model

2021 ◽  
Vol 17 (2) ◽  
pp. 363-368
Author(s):  
Inamul Hasan Madar ◽  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Marker Gene ◽  
The Elderly ◽  
Tissue Expression ◽  
Marker Genes ◽  
Data Sets ◽  
Machine Learning Classification ◽  
Machine Learning Model

Alzheimer's Disease (AD) is one of the most common causes of dementia, mostly affecting the elderly population. Currently, there is no proper diagnostic tool or method available for the detection of AD. The present study used two distinct data sets of AD genes, which could be potential biomarkers in the diagnosis. The differentially expressed genes (DEGs) curated from both datasets were used for machine learning classification, tissue expression annotation and co-expression analysis. Further, CNPY3, GPR84, HIST1H2AB, HIST1H2AE, IFNAR1, LMO3, MYO18A, N4BP2L1, PML, SLC4A4, ST8SIA4, TLE1 and N4BP2L1 were identified as highly significant DEGs and exhibited co-expression with other query genes. Moreover, a tissue expression study found that these genes are also expressed in the brain tissue. In addition to the earlier studies for marker gene identification, we have considered a different set of machine learning classifiers to improve the accuracy rate from the analysis. Amongst all the six classification algorithms, J48 emerged as the best classifier, which could be used for differentiating healthy and diseased samples. SMO/SVM and Logit Boost further followed J48 to achieve the classification accuracy.

scholarly journals Rapid Biomarker Screening of Alzheimer's Disease by Machine Learning and Graphene-Assisted Raman Spectroscopy

2021 ◽  
Author(s):  
Ziyang Wang ◽  
Jiarong Ye ◽  
Li Ding ◽  
Tomotaroh Granzier-Nakajima ◽  
Shubhang Sharma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Raman Spectroscopy ◽  
Brain Slices ◽  
Rapid Screening ◽  
Monolayer Graphene ◽  
Accurate Identification ◽  
Machine Learning Classification ◽  
The Brain

As the most common cause of dementia, Alzheimer's disease (AD) faces challenges in terms of understanding of pathogenesis, developing early diagnosis and developing effective treatment. Rapid and accurate identification of AD biomarkers in the brain will be critical to provide novel insights of AD. To this end, in the current work, we developed a system that can enable a rapid screening of AD biomarkers by employing Raman spectroscopy and machine learning analyses in AD transgenic animal brains. Specifically, we collected Raman spectra on slices of mouse brains with and without AD, and used machine learning to classify AD and non-AD spectra. By contacting monolayer graphene with the brain slices, we achieved significantly increased accuracy from 77% to 98% in machine learning classification. Further, we identified the Raman signature bands that are most important in classifying AD and non-AD samples. Based on these, we managed to identify AD-related biomolecules, which have been confirmed by biochemical studies. Our Raman-machine learning integrated method is promising to greatly accelerate the study of AD and can be potentially extended to human samples and various other diseases.

scholarly journals An interpretable machine learning model for diagnosis of Alzheimer's disease

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6543 ◽  
Author(s):  
Diptesh Das ◽  
Junichi Ito ◽  
Tadashi Kadowaki ◽  
Koji Tsuda
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Medical Diagnosis ◽  
Cost Effective ◽  
Learning Model ◽  
Patient Specific ◽  
Interpretable Machine Learning ◽  
Machine Learning Model ◽  
Effective Diagnosis

We present an interpretable machine learning model for medical diagnosis called sparse high-order interaction model with rejection option (SHIMR). A decision tree explains to a patient the diagnosis with a long rule (i.e., conjunction of many intervals), while SHIMR employs a weighted sum of short rules. Using proteomics data of 151 subjects in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, SHIMR is shown to be as accurate as other non-interpretable methods (Sensitivity, SN = 0.84 ± 0.1, Specificity, SP = 0.69 ± 0.15 and Area Under the Curve, AUC = 0.86 ± 0.09). For clinical usage, SHIMR has a function to abstain from making any diagnosis when it is not confident enough, so that a medical doctor can choose more accurate but invasive and/or more costly pathologies. The incorporation of a rejection option complements SHIMR in designing a multistage cost-effective diagnosis framework. Using a baseline concentration of cerebrospinal fluid (CSF) and plasma proteins from a common cohort of 141 subjects, SHIMR is shown to be effective in designing a patient-specific cost-effective Alzheimer’s disease (AD) pathology. Thus, interpretability, reliability and having the potential to design a patient-specific multistage cost-effective diagnosis framework can make SHIMR serve as an indispensable tool in the era of precision medicine that can cater to the demand of both doctors and patients, and reduce the overwhelming financial burden of medical diagnosis.

scholarly journals Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 778
Author(s):  
Nitsa J. Herzog ◽  
George D. Magoulas
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Structural Changes ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Progressive Dementia ◽  
Machine Learning Classification

Early identification of degenerative processes in the human brain is considered essential for providing proper care and treatment. This may involve detecting structural and functional cerebral changes such as changes in the degree of asymmetry between the left and right hemispheres. Changes can be detected by computational algorithms and used for the early diagnosis of dementia and its stages (amnestic early mild cognitive impairment (EMCI), Alzheimer’s Disease (AD)), and can help to monitor the progress of the disease. In this vein, the paper proposes a data processing pipeline that can be implemented on commodity hardware. It uses features of brain asymmetries, extracted from MRI of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, for the analysis of structural changes, and machine learning classification of the pathology. The experiments provide promising results, distinguishing between subjects with normal cognition (NC) and patients with early or progressive dementia. Supervised machine learning algorithms and convolutional neural networks tested are reaching an accuracy of 92.5% and 75.0% for NC vs. EMCI, and 93.0% and 90.5% for NC vs. AD, respectively. The proposed pipeline offers a promising low-cost alternative for the classification of dementia and can be potentially useful to other brain degenerative disorders that are accompanied by changes in the brain asymmetries.

scholarly journals Distinctive Oculomotor Behaviors in Alzheimer's Disease and Frontotemporal Dementia

2021 ◽  
Vol 12 ◽  
Author(s):  
Carmen Lage ◽  
Sara López-García ◽  
Alexandre Bejanin ◽  
Martha Kazimierczak ◽  
Ignacio Aracil-Bolaños ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Eye Tracking ◽  
Frontotemporal Dementia ◽  
Brain Regions ◽  
Middle Temporal Gyrus ◽  
Classification Algorithms ◽  
Learning Approaches ◽  
Machine Learning Classification

Oculomotor behavior can provide insight into the integrity of widespread cortical networks, which may contribute to the differential diagnosis between Alzheimer's disease and frontotemporal dementia. Three groups of patients with Alzheimer's disease, behavioral variant of frontotemporal dementia (bvFTD) and semantic variant of primary progressive aphasia (svPPA) and a sample of cognitively unimpaired elders underwent an eye-tracking evaluation. All participants in the discovery sample, including controls, had a biomarker-supported diagnosis. Oculomotor correlates of neuropsychology and brain metabolism evaluated with 18F-FDG PET were explored. Machine-learning classification algorithms were trained for the differentiation between Alzheimer's disease, bvFTD and controls. A total of 93 subjects (33 Alzheimer's disease, 24 bvFTD, seven svPPA, and 29 controls) were included in the study. Alzheimer's disease was the most impaired group in all tests and displayed specific abnormalities in some visually-guided saccade parameters, as pursuit error and horizontal prosaccade latency, which are theoretically closely linked to posterior brain regions. BvFTD patients showed deficits especially in the most cognitively demanding tasks, the antisaccade and memory saccade tests, which require a fine control from frontal lobe regions. SvPPA patients performed similarly to controls in most parameters except for a lower number of correct memory saccades. Pursuit error was significantly correlated with cognitive measures of constructional praxis and executive function and metabolism in right posterior middle temporal gyrus. The classification algorithms yielded an area under the curve of 97.5% for the differentiation of Alzheimer's disease vs. controls, 96.7% for bvFTD vs. controls, and 92.5% for Alzheimer's disease vs. bvFTD. In conclusion, patients with Alzheimer's disease, bvFTD and svPPA exhibit differentiating oculomotor patterns which reflect the characteristic neuroanatomical distribution of pathology of each disease, and therefore its assessment can be useful in their diagnostic work-up. Machine learning approaches can facilitate the applicability of eye-tracking in clinical practice.

scholarly journals Machine learning for comprehensive forecasting of Alzheimer’s Disease progression

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Charles K. Fisher ◽  
◽  
Aaron M. Smith ◽  
Jonathan R. Walsh ◽  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Progression ◽  
Synthetic Data ◽  
Patient Characteristics ◽  
Patient Data ◽  
Word Recall ◽  
Boltzmann Machine ◽  
Machine Learning Model

Abstract Most approaches to machine learning from electronic health data can only predict a single endpoint. The ability to simultaneously simulate dozens of patient characteristics is a crucial step towards personalized medicine for Alzheimer’s Disease. Here, we use an unsupervised machine learning model called a Conditional Restricted Boltzmann Machine (CRBM) to simulate detailed patient trajectories. We use data comprising 18-month trajectories of 44 clinical variables from 1909 patients with Mild Cognitive Impairment or Alzheimer’s Disease to train a model for personalized forecasting of disease progression. We simulate synthetic patient data including the evolution of each sub-component of cognitive exams, laboratory tests, and their associations with baseline clinical characteristics. Synthetic patient data generated by the CRBM accurately reflect the means, standard deviations, and correlations of each variable over time to the extent that synthetic data cannot be distinguished from actual data by a logistic regression. Moreover, our unsupervised model predicts changes in total ADAS-Cog scores with the same accuracy as specifically trained supervised models, additionally capturing the correlation structure in the components of ADAS-Cog, and identifies sub-components associated with word recall as predictive of progression.

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