GenomeNet/Self-GenomeNet
A method that utilizes unlabeled genomic data to address the challenge of limited data availability through self-training
This tool helps biologists and geneticists classify genomic sequences, even when they have limited labeled data for their specific problem. You provide a small amount of labeled genomic data along with a larger pool of unlabeled genomic data. It outputs a classification model that can accurately identify features or types within new genomic sequences, like distinguishing between different virus types or classifying genetic regulatory elements.
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Use this if you need to classify genomic sequences and have a wealth of unlabeled data, but only a small, expensive-to-obtain set of labeled examples.
Not ideal if you already have a large, well-labeled dataset for your specific classification task, as traditional supervised methods might suffice.
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Language
R
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Last pushed
Nov 20, 2023
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