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Peter Winter

Verified
Ottawa
As seen in: Medium, MSN, Nature, Wiley Online Library, Yahoo Canada, Oxford University Press (OUP), MDPI, Stuff.co.nz, The Conversation, FOCUS and
Biotechnology writer and analyst; specializes in business and finance developments as well as precision medicine.
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Articles

Evaluating the role of pretraining dataset size and diversity on single-cell foundation model performance

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By Alan DenAdel, Akshaya Thoutam, Anay Gupta, Andrew W. Navia, Nicolo K Fusi, Srivatsan V. Raghavan, Peter Winter Verified, Lorin Crawford
| Nature Verified
Abstract The success of transformer-based foundation models on natural language and images has motivated their use in single-cell biology. Single-cell foundation models have been trained on increasingly larger transcriptomic datasets, scaling from initial studies with 1 million cells to newer atlases with over 100 million cells. Here we investigate the role of pretraining dataset size and diversity on the performance of single-cell foundation models on both zero-shot and fine-tuned tasks.
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Artificial variables help to avoid over-clustering in single-cell RNA sequencing

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By Alan DenAdel, Michelle L. Ramseier, Andrew W. Navia, Alex K. Shalek, Srivatsan V. Raghavan, Peter Winter Verified, Lorin Crawford
| The American Journal of Human Genetics Verified
Keywords clustering double dipping single cell differential expression analysis calibration machine learning Get full text access Log in, subscribe or purchase for full access. References 1. Zheng, G.X.Y. ∙ Terry, J.M. ∙ Belgrader, P. ... Massively parallel digital transcriptional profiling of single cells Nat. Commun. 2017; 8, 14049 2. Macosko, E.Z. ∙ Basu, A. ∙ Satija, R. ... Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets Cell. 2015; 161:1202-1214 3.
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Adapting systems biology to address the complexity of human disease in the single-cell era - Nature Reviews Genetics

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By Martin Villanueva, Peter Winter Verified, David Fischer Verified
| Nature Verified
Abstract Systems biology aims to achieve holistic insights into the molecular workings of cellular systems through iterative loops of measurement, analysis and perturbation. This framework has had remarkable success in unicellular model organisms, and recent experimental and computational advances — from single-cell and spatial profiling to CRISPR genome editing and machine learning — have raised the exciting possibility of leveraging such strategies to prevent, diagnose and treat human diseases.
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