ArticleiScience2022
layerUMAP: A tool for visualizing and understanding deep learning models in biological sequence classification using UMAP.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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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.
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Who cites it
7 citing papers in PubMed.
- Multimodal deep learning for allergenic proteins prediction.BMC biology · 2025Article
- Assessing and improving reliability of neighbor embedding methods: a map-continuity perspective.Nature communications · 2025Article
- Overview and Prospects of DNA Sequence Visualization.International journal of molecular sciences · 2025Review
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- EnsembleDL-ATG: Identifying autophagy proteins by integrating their sequence and evolutionary information using an ensemble deep learning framework.Computational and structural biotechnology journal · 2023Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite the impressive success of deep learning techniques in various types of classification and prediction tasks, interpreting these models and explaining their predictions are still major challenges. In this article, we present an easy-to-use command line tool capable of visualizing and analyzing alternative representations of biological observations learned by deep learning models. This new tool, namely, layerUMAP, integrates autoBioSeqpy software and the UMAP library to address learned high-level representations. An important advantage of the tool is that it provides an interactive option that enables users to visualize the outputs of hidden layers along the depth of the model. We use two different classes of examples to illustrate the potential power of layerUMAP, and the results demonstrate that layerUMAP can provide insightful visual feedback about models and further guide us to develop better models.
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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.