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Your search keyword '"Yang, Yoona"' showing total 33 results
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33 results on '"Yang, Yoona"'

1. AKT and EZH2 inhibitors kill TNBCs by hijacking mechanisms of involution

Author

Schade, Amy E., Perurena, Naiara, Yang, Yoona, Rodriguez, Carrie L., Krishnan, Anjana, Gardner, Alycia, Loi, Patrick, Xu, Yilin, Nguyen, Van T. M., Mastellone, G. M., Pilla, Natalie F., Watanabe, Marina, Ota, Keiichi, Davis, Rachel A., Mattioli, Kaia, Xiang, Dongxi, Zoeller, Jason J., Lin, Jia-Ren, Morganti, Stefania, Garrido-Castro, Ana C., Tolaney, Sara M., Li, Zhe, Barbie, David A., Sorger, Peter K., Helin, Kristian, Santagata, Sandro, Knott, Simon R. V., and Cichowski, Karen

Published
2024
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2. Force Training Neural Network Potential Energy Surface Models

Author

Devereux, Christian, Yang, Yoona, Martí, Carles, Zádor, Judit, Eldred, Michael S., and Najm, Habib N.

Subjects
Physics - Chemical Physics
Abstract

Machine learned chemical potentials have shown great promise as alternatives to conventional computational chemistry methods to represent the potential energy of a given atomic or molecular system as a function of its geometry. However, such potentials are only as good as the data they are trained on, and building a comprehensive training set can be a costly process. Therefore, it is important to extract as much information from training data as possible without further increasing the computational cost. One way to accomplish this is by training on molecular forces in addition to energies. This allows for three additional labels per atom within the molecule. Here we develop a neural network potential energy surface for studying a hydrogen transfer reaction between two conformers of C5H5. We show that, for a much smaller training set, force training can greatly improve the accuracy of the model compared to only training to energies. We also demonstrate the importance of choosing the proper force to energy weight ratio for the loss function to minimize the model test error.

Published
2023

3. The molecular consequences of androgen activity in the human breast

Author

Raths, Florian, Karimzadeh, Mehran, Ing, Nathan, Martinez, Andrew, Yang, Yoona, Qu, Ying, Lee, Tian-Yu, Mulligan, Brianna, Devkota, Suzanne, Tilley, Wayne T, Hickey, Theresa E, Wang, Bo, Giuliano, Armando E, Bose, Shikha, Goodarzi, Hani, Ray, Edward C, Cui, Xiaojiang, and Knott, Simon RV

Subjects
Biochemistry and Cell Biology, Biomedical and Clinical Sciences, Biological Sciences, Breast Cancer, Genetics, Cancer, Sexual and Gender Minorities (SGM/LGBT*), Aetiology, 2.1 Biological and endogenous factors, androgen regulation of human breast homeostasis, breast microenvironment, cellular metabolism, chromatin regulation, hormone receptor, lactation, multiplexed immunohistochemistry by co-detection by indexing, single-cell ATAC sequencing, single-cell RNA sequencing, transcriptional control
Abstract

Estrogen and progesterone have been extensively studied in the mammary gland, but the molecular effects of androgen remain largely unexplored. Transgender men are recorded as female at birth but identify as male and may undergo gender-affirming androgen therapy to align their physical characteristics and gender identity. Here we perform single-cell-resolution transcriptome, chromatin, and spatial profiling of breast tissues from transgender men following androgen therapy. We find canonical androgen receptor gene targets are upregulated in cells expressing the androgen receptor and that paracrine signaling likely drives sex-relevant androgenic effects in other cell types. We also observe involution of the epithelium and a spatial reconfiguration of immune, fibroblast, and vascular cells, and identify a gene regulatory network associated with androgen-induced fat loss. This work elucidates the molecular consequences of androgen activity in the human breast at single-cell resolution.

Published
2023

4. Force training neural network potential energy surface models.

Author

Devereux, Christian, Yang, Yoona, Martí, Carles, Zádor, Judit, Eldred, Michael S., and Najm, Habib N.

Subjects
*NUCLEAR energy, *FORCE & energy, *POTENTIAL energy surfaces, *HYDROGEN transfer reactions, *COMPUTATIONAL chemistry
Abstract

Machine learned chemical potentials have shown great promise as alternatives to conventional computational chemistry methods to represent the potential energy of a given atomic or molecular system as a function of its geometry. However, such potentials are only as good as the data they are trained on, and building a comprehensive training set can be a costly process. Therefore, it is important to extract as much information from training data as possible without further increasing the computational cost. One way to accomplish this is by training on molecular forces in addition to energies. This allows for three additional labels per atom within the molecule. Here we develop a neural network potential energy surface for studying a hydrogen transfer reaction between two isomers of C5H5${\mathrm{C}}_5{\mathrm{H}}_5$. We show that, for a much smaller training set, force training not only improves the accuracy of the model compared to only training on energies, but also provides more accurate and smoother first and second derivatives that are crucial to run dynamics and extract vibrational frequencies in the context of transition‐state theory. We also demonstrate the importance of choosing the proper force to energy weight ratio for the loss function to minimize the model test error. [ABSTRACT FROM AUTHOR]

Published
2025
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10. Machine Learning for Carbon Nanotube Optical Sensors

Author

Heller, Daniel A., primary, Yaari, Zvi A, additional, Kim, Mijin, additional, Yang, Yoona, additional, Chen, Chen, additional, Antman-Passig, Merav, additional, Wang, Peng, additional, Wang, YuHuang, additional, Jagota, Anand, additional, and Zheng, Ming, additional

Published
2022
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16. A perception-based nanosensor platform to detect cancer biomarkers

Author

Yaari, Zvi, primary, Yang, Yoona, additional, Apfelbaum, Elana, additional, Cupo, Christian, additional, Settle, Alex H., additional, Cullen, Quinlan, additional, Cai, Winson, additional, Roche, Kara Long, additional, Levine, Douglas A., additional, Fleisher, Martin, additional, Ramanathan, Lakshmi, additional, Zheng, Ming, additional, Jagota, Anand, additional, and Heller, Daniel A., additional

Published
2021
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22. Organic Color Center Platform for Cancer Diagnosis

Author

Kim, Mijin, primary, Yang, Yoona, additional, Wang, Peng, additional, Chen, Chen, additional, Antman-Passig, Merav, additional, Wun, Christopher, additional, Luo, Hong-bin, additional, Wang, YuHuang, additional, Jagota, Anand, additional, Zheng, Ming, additional, and Heller, Daniel A., additional

Published
2021
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23. Machine-Perception Nanosensor Platform to Detect Cancer Biomarkers

Author

Yaari, Zvi, primary, Yang, Yoona, additional, Apfelbaum, Elana, additional, Settle, Alex, additional, Cullen, Quinlan, additional, Cai, Winson, additional, Roche, Kara Long, additional, Levine, Douglas A., additional, Fleisher, Martin, additional, Ramanathan, Lakshmi, additional, Zheng, Ming, additional, Jagota, Anand, additional, and Heller, Daniel A., additional

Published
2021
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