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In traditional laser scanning confocal spectral microscopy, fluorophore information is acquired by recording emission spectra with a single combination of discrete excitation wavelengths. However, organic fluorophores possess characteristic excitation spectra in addition to their unique emission spectral signatures. In this paper, we propose a generalized multi-view machine learning approach that leverages both excitation and emission spectra to significantly improve the accuracy in differentiating multiple highly overlapping fluorophores in a single image. By recording emission spectra of the same field with multiple combinations of excitation wavelengths, we obtain data representing different views of the underlying fluorophore distribution in the sample. We then propose a multi-view machine learning framework that allows for the flexible incorporation of noise information and abundance constraints, enabling the extraction of spectral signatures from reference images and efficient recovery of corresponding abundances in unknown mixed images. Numerical experiments on simulated image data demonstrate the method\u2019s efficacy in improving accuracy, allowing for the discrimination of 100 fluorophores with highly overlapping spectra. Furthermore, validation on images of mixtures of fluorescently labeled Escherichia coli highlights the power of the proposed multi-view strategy in discriminating fluorophores with spectral overlap in real biological images.<\/jats:p>","DOI":"10.1093\/bib\/bbaf005","type":"journal-article","created":{"date-parts":[[2025,1,2]],"date-time":"2025-01-02T15:17:03Z","timestamp":1735831023000},"source":"Crossref","is-referenced-by-count":4,"title":["A framework of multi-view machine learning for biological spectral unmixing of fluorophores with overlapping excitation and emission spectra"],"prefix":"10.1093","volume":"26","author":[{"given":"Ruogu","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Biology, University at Albany, SUNY , 1400 Washington Ave, Albany, NY 12222 ,","place":["United States"]},{"name":"RNA Institute, University at Albany, SUNY , 1400 Washington Ave, Albany, NY 12222 ,","place":["United States"]}]},{"given":"Yunlong","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, University at Albany, SUNY , 1400 Washington Ave, Albany, NY 12222 ,","place":["United States"]}]},{"given":"Alex M","family":"Valm","sequence":"additional","affiliation":[{"name":"Department of Biology, University at Albany, SUNY , 1400 Washington Ave, Albany, NY 12222 ,","place":["United States"]},{"name":"RNA Institute, University at Albany, SUNY , 1400 Washington Ave, Albany, NY 12222 ,","place":["United States"]}]}],"member":"286","published-online":{"date-parts":[[2025,1,13]]},"reference":[{"key":"2025011313440073300_ref1","first-page":"1","article-title":"Alternating direction algorithms for constrained sparse regression: application to hyperspectral unmixing","volume-title":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote 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