ArticleFrontiers in genetics2018
A Novel Protein Subcellular Localization Method With CNN-XGBoost Model for Alzheimer's Disease.
Article in Frontiers in genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- AD-Diff: enhancing Alzheimer's disease prediction accuracy through multimodal fusion.Frontiers in computational neuroscience · 2025Article
- A review of AI-based radiogenomics in neurodegenerative disease.Frontiers in big data · 2025Review
- Optimizing protein sequence classification: integrating deep learning models with Bayesian optimization for enhanced biological analysis.BMC medical informatics and decision making · 2024Article
- A Review for Artificial Intelligence Based Protein Subcellular Localization.Biomolecules · 2024Review
- Predicting cysteine reactivity changes upon phosphorylation using XGBoost.FEBS open bio · 2024Article
- An extended machine learning technique for polycystic ovary syndrome detection using ovary ultrasound image.Scientific reports · 2022Article
- A hybrid machine learning/deep learning COVID-19 severity predictive model from CT images and clinical data.Scientific reports · 2022Article
- nhKcr: a new bioinformatics tool for predicting crotonylation sites on human nonhistone proteins based on deep learning.Briefings in bioinformatics · 2021Article
- A Comparative Analysis of Novel Deep Learning and Ensemble Learning Models to Predict the Allergenicity of Food Proteins.Foods (Basel, Switzerland) · 2021Article
- Identification of Enzymes-specific Protein Domain Based on DDE, and Convolutional Neural Network.Frontiers in genetics · 2021Article
- Computational methods for protein localization prediction.Computational and structural biotechnology journal · 2021Review
- DeepPred-SubMito: A Novel Submitochondrial Localization Predictor Based on Multi-Channel Convolutional Neural Network and Dataset Balancing Treatment.International journal of molecular sciences · 2020Article
- Use of Chou's 5-steps rule to predict the subcellular localization of gram-negative and gram-positive bacterial proteins by multi-label learning based on gene ontology annotation and profile alignment.Journal of integrative bioinformatics · 2020Article
- Article
- Explainable Machine Learning Approach as a Tool to Understand Factors Used to Select the Refractive Surgery Technique on the Expert Level.Translational vision science & technology · 2020Article
Corrections and comments
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Authors and funding
5 authors.
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Abstract
The disorder distribution of protein in the compartment or organelle leads to many human diseases, including neurodegenerative diseases such as Alzheimer's disease. The prediction of protein subcellular localization play important roles in the understanding of the mechanism of protein function, pathogenes and disease therapy. This paper proposes a novel subcellular localization method by integrating the Convolutional Neural Network (CNN) and eXtreme Gradient Boosting (XGBoost), where CNN acts as a feature extractor to automatically obtain features from the original sequence information and a XGBoost classifier as a recognizer to identify the protein subcellular localization based on the output of the CNN. Experiments are implemented on three protein datasets. The results prove that the CNN-XGBoost method performs better than the general protein subcellular localization methods.
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