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Yinan Ni on Muck Rack

Yinan Ni

As seen in: Nature, Science Magazine, BioRxiv, Journal of Financial Economic Policy
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Articles

[2601.20389] Graph-Structured Deep Learning Framework for Multi-task Contention Identification with High-dimensional Metrics

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By Yinan Ni, Yuqi Tang, Zhimin Qiu
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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Graph-Transformer Reconstruction Learning for Unsupervised Anomaly Detection in Dependency-Coupled Systems

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By Chong Zhang, Chihui Shao, Junjie Jiang, Yinan Ni
| preprints.org
To address the practical challenges of diverse anomaly patterns, strongly coupled dependencies, and high labeling costs in large-scale complex infrastructures, this paper presents an unsupervised anomaly detection method that integrates graph neural networks with Transformer models. The approach learns normal system behavior and identifies deviations without relying on anomaly labels.
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LLPS REDIFINE allows the biophysical characterization of multicomponent condensates without tags or labels - Nature Communications

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By Mihajlo Novakovic, Yinan Ni, Leonidas Emmanouilidis, Nina C. Kathe
| Nature Verified
Abstract Liquid-liquid phase separation (LLPS) phenomenon plays a vital role in multiple cell biology processes, providing a mechanism to concentrate biomolecules and promote cellular reactions locally. Despite its significance in biology, there is a lack of conventional techniques suitable for studying biphasic samples in their biologically relevant form.
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