ArticlePloS one2019
A novel one-class classification approach to accurately predict disease-gene association in acute myeloid leukemia cancer.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Multiclass leukemia cell classification using hybrid deep learning and machine learning with CNN-based feature extraction.Scientific reports · 2025Article
- Disease candidate genes prediction using positive labeled and unlabeled instances.BMC medical genomics · 2025Article
- Learning peptide properties with positive examples only.Digital discovery · 2024Article
- Distance-based novelty detection model for identifying individuals at risk of developing Alzheimer's disease.Frontiers in aging neuroscience · 2024Article
- Optimization of diagnosis and treatment of hematological diseases via artificial intelligence.Frontiers in medicine · 2024Review
- A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects.Journal of medical Internet research · 2022Review
- Machine learning prediction and tau-based screening identifies potential Alzheimer's disease genes relevant to immunity.Communications biology · 2022Article
- Application of machine learning in the management of acute myeloid leukemia: current practice and future prospects.Blood advances · 2020Review
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Authors and funding
3 authors.
Funding
Abstract
Disease causing gene identification is considered as an important step towards drug design and drug discovery. In disease gene identification and classification, the main aim is to identify disease genes while identifying non-disease genes are of less or no significant. Hence, this task can be defined as a one-class classification problem. Existing machine learning methods typically take into consideration known disease genes as positive training set and unknown genes as negative samples to build a binary-class classification model. Here we propose a new One-class Classification Support Vector Machines (OCSVM) method to precisely classify candidate disease genes. Our aim is to build a model that concentrate its focus on detecting known disease-causing gene to increase sensitivity and precision. We investigate the impact of our proposed model using a benchmark consisting of the gene expression dataset for Acute Myeloid Leukemia (AML) cancer. Compared with the traditional methods, our experimental result shows the superiority of our proposed method in terms of precision, recall, and F-measure to detect disease causing genes for AML. OCSVM codes and our extracted AML benchmark are publicly available at: https://github.com/imandehzangi/OCSVM.
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