ArticleFrontiers in genetics2019
k-Skip-n-Gram-RF: A Random Forest Based Method for Alzheimer's Disease Protein Identification.
Article in Frontiers in genetics, 2019. 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.
- A review on advancements in feature selection and feature extraction for high-dimensional NGS data analysis.Functional & integrative genomics · 2024Review
- Lipoproteins and metabolites in diagnosing and predicting Alzheimer's disease using machine learning.Lipids in health and disease · 2024Article
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- ASPIRER: a new computational approach for identifying non-classical secreted proteins based on deep learning.Briefings in bioinformatics · 2022Article
- HSM6AP: a high-precision predictor for the HomoRNA biology · 2021Article
- Artificial Intelligence, Machine Learning and Deep Learning in Ion Channel Bioinformatics.Membranes · 2021Review
- sgRNACNN: identifying sgRNA on-target activity in four crops using ensembles of convolutional neural networks.Plant molecular biology · 2021Article
- Identification of the key target profiles underlying the drugs of narrow therapeutic index for treating cancer and cardiovascular disease.Computational and structural biotechnology journal · 2021Article
- Identification of Methicillin-Resistant Staphylococcus Aureus From Methicillin-Sensitive Staphylococcus Aureus and Molecular Characterization in Quanzhou, China.Frontiers in cell and developmental biology · 2021Article
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- Application of Machine Learning for Drug-Target Interaction Prediction.Frontiers in genetics · 2021Review
- Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine Learning Methods.BioMed research international · 2021Article
- Genomic Variation Prediction: A Summary From Different Views.Frontiers in cell and developmental biology · 2021Review
- Computational Prediction of Protein Arginine Methylation Based on Composition-Transition-Distribution Features.ACS omega · 2020Article
- Prediction of m5C Modifications in RNA Sequences by Combining Multiple Sequence Features.Molecular therapy. Nucleic acids · 2020Article
- Is There Any Sequence Feature in the RNA Pseudouridine Modification Prediction Problem?Molecular therapy. Nucleic acids · 2020Article
- Identifying Protein Biomarkers in Blood for Alzheimer's Disease.Frontiers in cell and developmental biology · 2020Article
- ApoPred: Identification of Apolipoproteins and Their Subfamilies With Multifarious Features.Frontiers in cell and developmental biology · 2020Article
- PSBP-SVM: A Machine Learning-Based Computational Identifier for Predicting Polystyrene Binding Peptides.Frontiers in bioengineering and biotechnology · 2020Article
- BOW-GBDT: A GBDT Classifier Combining With Artificial Neural Network for Identifying GPCR-Drug Interaction Based on Wordbook Learning From Sequences.Frontiers in cell and developmental biology · 2020Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
In this paper, a computational method based on machine learning technique for identifying Alzheimer's disease genes is proposed. Compared with most existing machine learning based methods, existing methods predict Alzheimer's disease genes by using structural magnetic resonance imaging (MRI) technique. Most methods have attained acceptable results, but the cost is expensive and time consuming. Thus, we proposed a computational method for identifying Alzheimer disease genes by use of the sequence information of proteins, and classify the feature vectors by random forest. In the proposed method, the gene protein information is extracted by adaptive k-skip-n-gram features. The proposed method can attain the accuracy to 85.5% on the selected UniProt dataset, which has been demonstrated by the experimental results.
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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 Socratic graph.