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Merih Deniz Toruner on Muck Rack

Merih Deniz Toruner

As seen in: Illumination, PLOS, MDPI, BioRxiv
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Articles

Machine learning on multiple epigenetic features reveals H3K27Ac as a driver of gene expression prediction across patients with glioblastoma

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By Yusuke Suita, Yuan Pu, Merih Deniz Toruner
| PLOS
We test several regression-based ML and DL models to examine the novel application of our specific combination of epigenetic markers to predict gene expression in a cross-patient setting for CIPHER. Given the two patients (GSC1 and GSC2), we seek to use ML to extract a common pattern seen across patients, as GSCs are highly heterogeneous from one patient to the next.
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Tumor Landscape Analysis: An Ecologically Informed Framework to Understand Tumor Microenvironments

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By Luis Cisneros, Merih Deniz Toruner, Martin E Fernandez-Zapico, Carlo C. Maley
| BioRxiv
Abstract Tumor microenvironments (TMEs) are spatially complex and dynamic systems shaped by evolutionary pressures, tissue architecture, and cellular interactions. To capture this complexity, we developed the Tumor Landscape Analysis (TLA) pipeline, a computational framework applying principles from landscape ecology and spatial statistics to quantitatively characterize tumor spatial heterogeneity.
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Artificial Intelligence-Driven Wireless Sensing for Health Management

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By Merih Deniz Toruner, Victoria Shi, John Sollee, Wen-Chi Hsu
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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