ArticleFrontiers in genetics2020
Identification of Orphan Genes in Unbalanced Datasets Based on Ensemble Learning.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Construction of an Overexpression Library for Chinese Cabbage Orphan Genes inPlants (Basel, Switzerland) · 2025Article
- A review of machine learning methods for imbalanced data challenges in chemistry.Chemical science · 2025Review
- Machine learning-based prediction model for the efficacy and safety of statins.Frontiers in pharmacology · 2024Article
- Enhancing DDoS detection in SDIoT through effective feature selection with SMOTE-ENN.PloS one · 2024Article
- Assessment of Soybean Lodging Using UAV Imagery and Machine Learning.Plants (Basel, Switzerland) · 2023Article
- The Lost and Found: Unraveling the Functions of Orphan Genes.Journal of developmental biology · 2023Review
- An Improved CatBoost-Based Classification Model for Ecological Suitability of Blueberries.Sensors (Basel, Switzerland) · 2023Article
- ORFanID: A web-based search engine for the discovery and identification of orphan and taxonomically restricted genes.PloS one · 2023Article
- Random forest algorithm for predicting postoperative delirium in older patients.Frontiers in neurology · 2023Article
- Research Advances and Prospects of Orphan Genes in Plants.Frontiers in plant science · 2022Review
- Relevant Characteristics Analysis Using Natural Language Processing and Machine Learning Based on Phenotypes and T-Cell Subsets in Systemic Lupus Erythematosus Patients With Anxiety.Frontiers in psychiatry · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Orphan genes are associated with regulatory patterns, but experimental methods for identifying orphan genes are both time-consuming and expensive. Designing an accurate and robust classification model to detect orphan and non-orphan genes in unbalanced distribution datasets poses a particularly huge challenge. Synthetic minority over-sampling algorithms (SMOTE) are selected in a preliminary step to deal with unbalanced gene datasets. To identify orphan genes in balanced and unbalanced
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Registered trials
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