ArticleFrontiers in genetics2019
Predicting Parkinson's Disease Genes Based on Node2vec and Autoencoder.
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 40 papers, 1 of them a synthesis that pooled it.
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Who cites it
40 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- MELGene: knowledge-enhanced multimodel ensemble learning for disease-gene association prediction.Briefings in bioinformatics · 2026Article
- Decoding the anti-thrombotic effects of leonurine: a multimodal approach combining TCM repositioning and mTOR signaling.Chinese medicine · 2025Article
- DDK-Linker: a network-based strategy identifies disease signals by linking high-throughput omics datasets to disease knowledge.Briefings in bioinformatics · 2024Article
- A network embedding approach to identify active modules in biological interaction networks.Life science alliance · 2023Article
- Expanding potential targets of herbal chemicals by node2vec based on herb-drug interactions.Chinese medicine · 2023Article
- Ranking Plant Network Nodes Based on Their Centrality Measures.Entropy (Basel, Switzerland) · 2023Article
- Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.Molecular diversity · 2023Review
- Accurately modeling biased random walks on weighted networks using node2vec.Bioinformatics (Oxford, England) · 2023Article
- Article
- Early and High-Accuracy Diagnosis of Parkinson's Disease: Outcomes of a New Model.Computational and mathematical methods in medicine · 2023Article
- The module triad: a novel network biology approach to utilize patients' multi-omics data for target discovery in ulcerative colitis.Scientific reports · 2022Article
- Predicting Parkinson disease related genes based on PyFeat and gradient boosted decision tree.Scientific reports · 2022Review
- Genetics in parkinson's disease: From better disease understanding to machine learning based precision medicine.Frontiers in molecular medicine · 2022Review
- WINNER: A network biology tool for biomolecular characterization and prioritization.Frontiers in big data · 2022Article
- A comprehensive survey on computational learning methods for analysis of gene expression data.Frontiers in molecular biosciences · 2022Review
- Angiogenesis goes computational - The future way forward to discover new angiogenic targets?Computational and structural biotechnology journal · 2022Review
- AIME: Autoencoder-based integrative multi-omics data embedding that allows for confounder adjustments.PLoS computational biology · 2022Article
- Identifying genes targeted by disease-associated non-coding SNPs with a protein knowledge graph.PloS one · 2022Article
- Benchmarking network-based gene prioritization methods for cerebral small vessel disease.Briefings in bioinformatics · 2021Article
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3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying genes associated with Parkinson's disease plays an extremely important role in the diagnosis and treatment of Parkinson's disease. In recent years, based on the guilt-by-association hypothesis, many methods have been proposed to predict disease-related genes, but few of these methods are designed or used for Parkinson's disease gene prediction. In this paper, we propose a novel prediction method for Parkinson's disease gene prediction, named N2A-SVM. N2A-SVM includes three parts: extracting features of genes based on network, reducing the dimension using deep neural network, and predicting Parkinson's disease genes using a machine learning method. The evaluation test shows that N2A-SVM performs better than existing methods. Furthermore, we evaluate the significance of each step in the N2A-SVM algorithm and the influence of the hyper-parameters on the result. In addition, we train N2A-SVM on the recent dataset and used it to predict Parkinson's disease genes. The predicted top-rank genes can be verified based on literature study.
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