scholarly journals A SURVEY ON THE DIAGNOSIS OF ALZHEIMER’S DISEASE USING MICROARRAY DATA

2016 ◽  
Author(s):  
Jegatheeswari S. ◽  
Vishnu Priya J P ◽  
Dr. Devaki P
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Microarray Data

scholarly journals Predicting Clinical Dementia Rating Using Blood RNA Levels

2019 ◽  
Author(s):  
Justin B. Miller ◽  
John S.K. Kauwe ◽  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Microarray Data ◽  
Predictive Accuracy ◽  
Cognitive Status ◽  
Clinical Dementia Rating ◽  
P Values ◽  
Intracellular Channel ◽  
Rna Levels

Structured AbstractINTRODUCTIONThe Clinical Dementia Rating (CDR) is commonly used to assess cognitive decline in Alzheimer’s disease patients.METHODSWe divided 741 participants with blood microarray data in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) into three groups based on their most recent CDR assessment: cognitive normal (CDR=0), mild dementia (CDR=0.5), and probable AD (CDR≥1.0). We then used machine learning to predict cognitive status using only blood RNA levels.RESULTSOne chloride intracellular channel 1 (CLIC1) probe was significant. By combining nonsignificant probes with p-values less than 0.1, we averaged 87.87 (s = 1.02)% predictive accuracy in classifying the three groups, compared to a 55.46% baseline for this study.DISCUSSIONWe identified one significant probe in CLIC1. However, CLIC1 levels alone were not sufficient to determine dementia status. We propose that combining individually suggestive, but nonsignificant, blood RNA levels can significantly improve diagnostic accuracy.

Gene Expression Biomarkers in the Brain of a Mouse Model for Alzheimer's Disease: Mining of Microarray Data by Logic Classification and Feature Selection

2011 ◽  
Vol 24 (4) ◽  
pp. 721-738 ◽  
Author(s):  
Ivan Arisi ◽  
Mara D'Onofrio ◽  
Rossella Brandi ◽  
Armando Felsani ◽  
Simona Capsoni ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Selection ◽  
Mouse Model ◽  
Microarray Data ◽  
The Brain

Gene Selection from Microarray Data for Alzheimer's Disease Using Random Forest

2020 ◽  
pp. 1391-1404
Author(s):  
Kazutaka Nishiwaki ◽  
Katsutoshi Kanamori ◽  
Hayato Ohwada
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Random Forest ◽  
Microarray Data ◽  
Gene Selection ◽  
Microarray Gene Expression Data ◽  
High Dimensional ◽  
Microarray Gene Expression ◽  
Disease Related Genes ◽  
Microarray Gene

A significant amount of microarray gene expression data is available on the Internet, and researchers are allowed to analyze such data freely. However, microarray data includes thousands of genes, and analysis using conventional techniques is too difficult. Therefore, selecting informative gene(s) from high-dimensional data is very important. In this study, the authors propose a gene selection method using random forest as a machine learning technique. They applied this method to microarray data on Alzheimer's disease and conducted an experiment to rank genes. The authors' results indicated some genes that have been investigated for their relevance to Alzheimer's disease, proving that their proposed cognitive method was successful in finding disease-related genes using microarray data.

Gene Selection from Microarray Data for Alzheimer's Disease Using Random Forest

2017 ◽  
Vol 9 (2) ◽  
pp. 14-30 ◽  
Author(s):  
Kazutaka Nishiwaki ◽  
Katsutoshi Kanamori ◽  
Hayato Ohwada
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Random Forest ◽  
Microarray Data ◽  
Gene Selection ◽  
Microarray Gene Expression Data ◽  
Gene Selection Method ◽  
Microarray Gene Expression ◽  
Learning Technique ◽  
Disease Related Genes

A significant amount of microarray gene expression data is available on the Internet, and researchers are allowed to analyze such data freely. However, microarray data includes thousands of genes, and analysis using conventional techniques is too difficult. Therefore, selecting informative gene(s) from high-dimensional data is very important. In this study, the authors propose a gene selection method using random forest as a machine learning technique. They applied this method to microarray data on Alzheimer's disease and conducted an experiment to rank genes. The authors' results indicated some genes that have been investigated for their relevance to Alzheimer's disease, proving that their proposed cognitive method was successful in finding disease-related genes using microarray data.

scholarly journals Texture and Gene Expression Analysis of the MRI Brain in Detection of Alzheimer’s Disease

2018 ◽  
Vol 8 (2) ◽  
pp. 111-120 ◽  
Author(s):  
Alhadi Bustamam ◽  
Devvi Sarwinda ◽  
Gianinna Ardenaswari
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Texture Analysis ◽  
Microarray Data ◽  
Human Memory ◽  
Nerve Tissue ◽  
Whole Brain ◽  
The Brain

Abstract Alzheimer’s disease is a type of dementia that can cause problems with human memory, thinking and behavior. This disease causes cell death and nerve tissue damage in the brain. The brain damage can be detected using brain volume, whole brain form, and genetic testing. In this research, we propose texture analysis of the brain and genomic analysis to detect Alzheimer’s disease. 3D MRI images were chosen to analyze the texture of the brain, and microarray data were chosen to analyze gene expression. We classified Alzheimer’s disease into three types: Alzheimer’s, Mild Cognitive Impairment (MCI), and Normal. In this study, texture analysis was carried out by using the Advanced Local Binary Pattern (ALBP) and the Gray Level Co-occurrence Matrix (GLCM). We also propose the bi-clustering method to analyze microarray data. The experimental results from texture analysis show that ALBP had better performance than GLCM in classification of Alzheimer’s disease. The ALBP method achieved an average value of accuracy of between 75% - 100% for binary classification of the whole brain data. Furthermore, Biclustering method with microarray data shows good performance gene expression, where this information show influence Alzheimer’s disease with total of bi-cluster is 6.

Cognitive control constrains memory attributions

2019 ◽  
Vol 42 ◽  
Author(s):  
Colleen M. Kelley ◽  
Larry L. Jacoby
Keyword(s):  
Alzheimer’S Disease ◽  
Older Adults ◽  
Alzheimer's Disease ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Cognitive Control

Abstract Cognitive control constrains retrieval processing and so restricts what comes to mind as input to the attribution system. We review evidence that older adults, patients with Alzheimer's disease, and people with traumatic brain injury exert less cognitive control during retrieval, and so are susceptible to memory misattributions in the form of dramatic levels of false remembering.

Formation of paired helical filaments in Alzheimer's disease

1984 ◽  
Vol 42 ◽  
pp. 302-303
Author(s):  
J. Metuzals ◽  
D. F. Clapin ◽  
V. Montpetit
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Frontal Lobe ◽  
Giant Axon ◽  
Paired Helical Filaments ◽  
Normal Brain ◽  
Protein Assemblies ◽  
Electron Microscopic ◽  
Helical Symmetry ◽  
Structural Levels

Information on the conformation of paired helical filaments (PHF) and the neurofilamentous (NF) network is essential for an understanding of the mechanisms involved in the formation of the primary lesions of Alzheimer's disease (AD): tangles and plaques. The structural and chemical relationships between the NF and the PHF have to be clarified in order to discover the etiological factors of this disease. We are investigating by stereo electron microscopic and biochemical techniques frontal lobe biopsies from patients with AD and squid giant axon preparations. The helical nature of the lesion in AD is related to pathological alterations of basic properties of the nervous system due to the helical symmetry that exists at all hierarchic structural levels in the normal brain. Because of this helical symmetry of NF protein assemblies and PHF, the employment of structure reconstruction techniques to determine the conformation, particularly the handedness of these structures, is most promising. Figs. 1-3 are frontal lobe biopsies.

Computer-aided methods for 3-D visualization of serial sections and thick biological specimens

1992 ◽  
Vol 50 (2) ◽  
pp. 1060-1061
Author(s):  
Mark Ellisman ◽  
Maryann Martone ◽  
Gabriel Soto ◽  
Eleizer Masliah ◽  
David Hessler ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Three Dimensional ◽  
Neuritic Plaque ◽  
Dimensional Structure ◽  
Data Sets ◽  
Molecular Physiology ◽  
Research Activities ◽  
Computer Aided ◽  
Dimensional Reconstruction

Structurally-oriented biologists examine cells, tissues, organelles and macromolecules in order to gain insight into cellular and molecular physiology by relating structure to function. The understanding of these structures can be greatly enhanced by the use of techniques for the visualization and quantitative analysis of three-dimensional structure. Three projects from current research activities will be presented in order to illustrate both the present capabilities of computer aided techniques as well as their limitations and future possibilities.The first project concerns the three-dimensional reconstruction of the neuritic plaques found in the brains of patients with Alzheimer's disease. We have developed a software package “Synu” for investigation of 3D data sets which has been used in conjunction with laser confocal light microscopy to study the structure of the neuritic plaque. Tissue sections of autopsy samples from patients with Alzheimer's disease were double-labeled for tau, a cytoskeletal marker for abnormal neurites, and synaptophysin, a marker of presynaptic terminals.

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