ArticleMolecules (Basel, Switzerland)2018
An Efficient Classifier for Alzheimer's Disease Genes Identification.
Article in Molecules (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed, 107 citations in OpenAlex.
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- A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis.Bioengineering (Basel, Switzerland) · 2024Review
- Applications of artificial intelligence in dementia research.Cambridge prisms. Precision medicine · 2023Review
- Wavelet-Based Fractal Analysis of rs-fMRI for Classification of Alzheimer's Disease.Sensors (Basel, Switzerland) · 2022Article
- HSM6AP: a high-precision predictor for the HomoRNA biology · 2021Article
- sgRNACNN: identifying sgRNA on-target activity in four crops using ensembles of convolutional neural networks.Plant molecular biology · 2021Article
- Application of Machine Learning for Drug-Target Interaction Prediction.Frontiers in genetics · 2021Review
- Early MCI-to-AD Conversion Prediction Using Future Value Forecasting of Multimodal Features.Computational intelligence and neuroscience · 2021Article
- Altered Regional Cerebral Blood Flow and Brain Function Across the Alzheimer's Disease Spectrum: A Potential Biomarker.Frontiers in aging neuroscience · 2021Article
- Article
- Identification of Methicillin-Resistant Staphylococcus Aureus From Methicillin-Sensitive Staphylococcus Aureus and Molecular Characterization in Quanzhou, China.Frontiers in cell and developmental biology · 2021Article
- Unearthing of Key Genes Driving the Pathogenesis of Alzheimer's Disease via Bioinformatics.Frontiers in genetics · 2021Article
- The Application of Artificial Intelligence in the Genetic Study of Alzheimer's Disease.Aging and disease · 2020Review
- NonClasGP-Pred: robust and efficient prediction of non-classically secreted proteins by integrating subset-specific optimal models of imbalanced data.Microbial genomics · 2020Article
- Computational Prediction of Protein Arginine Methylation Based on Composition-Transition-Distribution Features.ACS omega · 2020Article
- Is There Any Sequence Feature in the RNA Pseudouridine Modification Prediction Problem?Molecular therapy. Nucleic acids · 2020Article
- ApoPred: Identification of Apolipoproteins and Their Subfamilies With Multifarious Features.Frontiers in cell and developmental biology · 2020Article
- Identification and Classification of Enhancers Using Dimension Reduction Technique and Recurrent Neural Network.Computational and mathematical methods in medicine · 2020Article
- A Method for Identifying Vesicle Transport Proteins Based on LibSVM and MRMD.Computational and mathematical methods in medicine · 2020Article
- Prediction of Anticancer Peptides Using a Low-Dimensional Feature Model.Frontiers in bioengineering and biotechnology · 2020Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) is considered to one of 10 key diseases leading to death in humans. AD is considered the main cause of brain degeneration, and will lead to dementia. It is beneficial for affected patients to be diagnosed with the disease at an early stage so that efforts to manage the patient can begin as soon as possible. Most existing protocols diagnose AD by way of magnetic resonance imaging (MRI). However, because the size of the images produced is large, existing techniques that employ MRI technology are expensive and time-consuming to perform. With this in mind, in the current study, AD is predicted instead by the use of a support vector machine (SVM) method based on gene-coding protein sequence information. In our proposed method, the frequency of two consecutive amino acids is used to describe the sequence information. The accuracy of the proposed method for identifying AD is 85.7%, which is demonstrated by the obtained experimental results. The experimental results also show that the sequence information of gene-coding proteins can be used to predict AD.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.