DeepInsight-FS: Selecting features for non-image data using convolutional neural network
DeepInsight-FS: Selecting features for non-image data using convolutional neural network
Abstract Identifying smaller element or gene subsets from biological or other data types is an essential step in discovering underlying mechanisms. Statistical machine learning methods have played a key role in revealing gene subsets. However, growing data complexity is pushing the limits of these techniques. A review of the recent literature shows that arranging elements by similarity in image-form for a convolutional neural network (CNN) improves classification performance over treating them individually. Expanding on this, here we show a pipeline, DeepInsight-FS, to uncover gene subsets of clinical relevance. DeepInsight-FS converts non-image samples into image-form and performs element selection via CNN. To our knowledge, this is the first approach to employ CNN for element or gene selection on non-image data. A real world application of DeepInsight-FS to publicly available cancer data identified gene sets with significant overlap to several cancer-associated pathways suggesting the potential of this method to discover biomedically meaningful connections.
Tsunoda Tatsuhiko、Vans Edwin、Sharma Alok、Boroevich Keith A、Lysenko Artem
Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences||Laboratory for Medical Science Mathematics, Department of Biological Sciences, Graduate School of Science, The University of Tokyo||Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University||CREST, JSTSchool of Engineering & Physics, University of the South PacificLaboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences||Institute for Integrated and Intelligent Systems, Griffith University||School of Engineering & Physics, University of the South PacificLaboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical SciencesLaboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences
生物科学研究方法、生物科学研究技术生物科学理论、生物科学方法分子生物学
Tsunoda Tatsuhiko,Vans Edwin,Sharma Alok,Boroevich Keith A,Lysenko Artem.DeepInsight-FS: Selecting features for non-image data using convolutional neural network[EB/OL].(2025-03-28)[2025-05-02].https://www.biorxiv.org/content/10.1101/2020.09.17.301515.点此复制
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