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Theses

Deep learning for computational phenotyping in cell-based assays

Abstract : Computational phenotyping is an emergent set of technologies for systematically studying the role of the genome in eliciting phenotypes, the observable characteristics of an organism and its subsystems. In particular, cell-based assays screen panels of small compound drugs or otherwise modulations of gene expression, and quantify the effects on phenotypic characteristics ranging from viability to cell morphology. High content screening extends the methodologies of cell-based screens to a high content readout based on images, in particular the multiplexed channels of fluorescence microscopy. Screens based on multiple cell lines are apt to differentiating phenotypes across different subtypes of a disease, representing the molecular heterogeneity concerned in the design of precision medicine therapies. These richer biological models underpin a more targeted approach for treating deadly diseases such as cancer. An ongoing challenge for high content screening is therefore the synthesis of the heterogeneous readouts in multi-cell-line screens. Concurrently, deep learning is the established state-of-the-art image analysis and computer vision applications. However, its role in high content screening is only beginning to be realised. This dissertation spans two problem settings in the high content analysis of cancer cell lines. The contributions are the following: (i) a demonstration of the potential for deep learning and generative models in high content screening; (ii) a deep learning-based solution to the problem of heterogeneity in a multi-cell-line drug screen; and (iii) novel applications of image-to-image translation models as an alternative to the expensive fluorescence microscopy currently required for high content screening.
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Submitted on : Thursday, September 3, 2020 - 9:45:28 AM
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  • HAL Id : tel-02928984, version 1

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Joseph Boyd. Deep learning for computational phenotyping in cell-based assays. Bioinformatics [q-bio.QM]. Université Paris sciences et lettres, 2020. English. ⟨NNT : 2020UPSLM008⟩. ⟨tel-02928984⟩

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