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- W4387467474 abstract "<p class=first dir=auto id=d11455383e50>Multiomics, from single-cell to spatial omics, have recently emerged as a new combinatorial modality where multiple layers of biological information can provide deeper and richer pathophysiological insights. Yet, challenges from selecting an appropriate sample, to technical considerations, to readout interpretation; become more and more relevant, given the ever-increasing wealth of the data we generate. Another yet field of Science, representation learning, has gained in recent years a lot of attention in laboratory experimental and clinical science with variable levels of success and utility. Despite the persistent euphoria and hope around machine and deep learning in Bioscience, high-quality datasets still require to be tailored to the biological question, to form the basis of a robust trainable system. However, deciding which are the appropriate technologies for the task, how much data, the scope, the focus and the composition of the data pool, frequently enough, doesn't have a straightforward answer. In our lab, we are currently developing the experimental design, to shape data-producing pipelines for both model animals and airway/lung patient samples, to serve as an abundant source of cell identities and states (healthy vs respiratory disease states) for multi-modal algorithmic design, with tailor-made datasets." @default.
- W4387467474 created "2023-10-10" @default.
- W4387467474 creator A5093031119 @default.
- W4387467474 date "2023-01-01" @default.
- W4387467474 modified "2023-10-11" @default.
- W4387467474 title "Challenges and considerations of designing multi-modal data workflows for trainable omics models" @default.
- W4387467474 doi "https://doi.org/10.14293/gof.23.30" @default.
- W4387467474 hasPublicationYear "2023" @default.
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