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All you need is color : image based spatial gene expression prediction using neural stain learning
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Dawood, Muhammad, Branson, Kim, Rajpoot, Nasir M. and Minhas, Fayyaz ul Amir Afsar (2022) All you need is color : image based spatial gene expression prediction using neural stain learning. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Published in: Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2021 pp. 437-450. ISBN 9783030937324. doi:10.1007/978-3-030-93733-1_32 ISSN 1865-0937.
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Official URL: https://doi.org/10.1007/978-3-030-93733-1_32
Abstract
“Is it possible to predict expression levels of different genes at a given spatial location in the routine histology image of a tumor section by modeling its stain absorption characteristics?” In this work, we propose a “stain-aware” machine learning approach for prediction of spatial transcriptomic gene expression profiles using digital pathology image of a routine Hematoxylin & Eosin (H&E) histology section. Unlike recent deep learning methods which are used for gene expression prediction, our proposed approach termed Neural Stain Learning (NSL) explicitly models the association of stain absorption characteristics of the tissue with gene expression patterns in spatial transcriptomics by learning a problem-specific stain deconvolution matrix in an end-to-end manner. The proposed method with only 11 trainable weight parameters outperforms both classical regression models with cellular composition and morphological features as well as deep learning methods. We have found that the gene expression predictions from the proposed approach show higher correlations with true expression values obtained through sequencing for a larger set of genes in comparison to other approaches.
Item Type: | Conference Item (Paper) | ||||||
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Subjects: | Q Science > QH Natural history > QH426 Genetics | ||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
SWORD Depositor: | Library Publications Router | ||||||
Library of Congress Subject Headings (LCSH): | Gene expression -- Research, Machine learning, Genetic transcription -- Regulation -- Mathematical models, Pathology -- Data processing, Nucleotide sequence -- Data processing | ||||||
Series Name: | Communications in Computer and Information Science | ||||||
Journal or Publication Title: | Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2021 | ||||||
Publisher: | Springer International Publishing | ||||||
ISBN: | 9783030937324 | ||||||
ISSN: | 1865-0937 | ||||||
Official Date: | 18 February 2022 | ||||||
Dates: |
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Page Range: | pp. 437-450 | ||||||
DOI: | 10.1007/978-3-030-93733-1_32 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Date of first compliant deposit: | 30 January 2024 | ||||||
Date of first compliant Open Access: | 30 January 2024 | ||||||
RIOXX Funder/Project Grant: |
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Conference Paper Type: | Paper | ||||||
Title of Event: | Joint European Conference on Machine Learning and Knowledge Discovery in Databases | ||||||
Type of Event: | Conference | ||||||
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