Revolutionizing Biomarker Discovery: Unlock the Power of Scailyte’s AI platform

Revolutionizing Biomarker Discovery: Unlock the Power of Scailyte's AI platform

Dive into the forefront of scientific advancement with Scailyte’s groundbreaking AI platform, ScaiVision. Discover how our algorithm’s unmatched robustness and accuracy are reshaping biomarker identification. From accurate signatures to unwavering reliability, ScaiVision is leading the way. Ready to witness the future of data-driven discovery? Click below to watch the video and stay tuned for more exciting insights!

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Discovering next-generation biomarkers through integration of single-cell biology and artificial intelligence

Single-cell technologies have enabled the generation of vast amounts of data from human tissues with unprecedented resolution. While numerous cell atlas efforts have made strides toward describing and cataloging cellular complexity, the full potential of single-cell data in biomarker discovery and clinical applications has yet to be unlocked, in part due to the challenges in analyzing such high-dimensional, complex data. Scailyte has developed a novel approach combining single-cell analysis, integration of clinical data, and a tailor-made supervised machine learning platform, which allows for targeted and sensitive biomarker discovery. Here we highlight several discovery projects across different disease areas, including oncology and cell therapy.

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Identification of ALP+/CD73+ defining markers for enhanced osteogenic potential in human adipose-derived mesenchymal stromal cells by mass cytometry.

Adipose-derived mesenchymal stem cells represent a relatively novel cell therapeutic modality for promoting bone healing, however they remain relatively uncharacterized on a subpopulation level. Using the forefather algorithm of ScaiVision, CellCNN, the authors were able to exploit CyTOF data to identify an ALP and CD73-double positive population as having a high bone differentiation potential. They could also show that these cells could not only be used to identify promising cell lines for use in bone regeneration, but their levels could also be used as a quality control during manufacturing.

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High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy

Although it has driven a paradigm shift in how cancer is treated, targeted immunotherapy is oftentimes plagued by low response rates, highlighting the urgent need for biomarkers predictive of response. Here, CellCNN was used to further characterize and confirm the identity of a specific subpopulation of monocytes as being strongly associated with response to anti-PD-1 immunotherapy in stage IV melanoma, paving the road for the selection of tailored treatments for patients.

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ScaiDigest August 2023

ScaiDigest August 2023

Sukalp Muzumdar, our esteemed expert data scientist, has delved into cutting-edge research once again in the latest edition of ScaiDigest!

In the August edition, Sukalp explores the critical aspect of model interpretability in the application of artificial intelligence in the biological sciences.

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