ArticleJournal of computational biology : a journal of computational molecular cell biology2014
Imbalanced class learning in epigenetics.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2014. 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.
- Enhancing smart home environments: a novel pattern recognition approach to ambient acoustic event detection and localization.Frontiers in big data · 2024Article
- A quantum-based oversampling method for classification of highly imbalanced and overlapped data.Experimental biology and medicine (Maywood, N.J.) · 2023Article
- Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.International journal of molecular sciences · 2023Review
- Article
- Machine learning and clinical epigenetics: a review of challenges for diagnosis and classification.Clinical epigenetics · 2020Review
- Predicting gastrointestinal drug effects using contextualized metabolic models.PLoS computational biology · 2019Article
- Integrated genetic and epigenetic prediction of coronary heart disease in the Framingham Heart Study.PloS one · 2018Article
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
In machine learning, one of the important criteria for higher classification accuracy is a balanced dataset. Datasets with a large ratio between minority and majority classes face hindrance in learning using any classifier. Datasets having a magnitude difference in number of instances between the target concept result in an imbalanced class distribution. Such datasets can range from biological data, sensor data, medical diagnostics, or any other domain where labeling any instances of the minority class can be time-consuming or costly or the data may not be easily available. The current study investigates a number of imbalanced class algorithms for solving the imbalanced class distribution present in epigenetic datasets. Epigenetic (DNA methylation) datasets inherently come with few differentially DNA methylated regions (DMR) and with a higher number of non-DMR sites. For this class imbalance problem, a number of algorithms are compared, including the TAN+AdaBoost algorithm. Experiments performed on four epigenetic datasets and several known datasets show that an imbalanced dataset can have similar accuracy as a regular learner on a balanced dataset.
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Registered trials
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 OpenQuestion graph.