Data-driven learning of structure augments quantitative prediction of biological responses.

Autor: Yuanchi Ha, Helena R Ma, Feilun Wu, Andrea Weiss, Katherine Duncker, Helen Z Xu, Jia Lu, Max Golovsky, Daniel Reker, Lingchong You

Publikováno v: PLoS Computational Biology, Vol 20, Iss 6, p e1012185 (2024)

Multi-factor screenings are commonly used in diverse applications in medicine and bioengineering, including optimizing combination drug treatments and microbiome engineering. Despite the advances in high-throughput technologies, large-scale experimen

Externí odkaz: https://doaj.org/article/e84a46d7904c4e1988874984de5cb571

Modulation of microbial community dynamics by spatial partitioning

Autor: Feilun Wu, Yuanchi Ha, Andrea Weiss, Meidi Wang, Jeffrey Letourneau, Shangying Wang, Nan Luo, Shuquan Huang, Charlotte T. Lee, Lawrence A. David, Lingchong You

Publikováno v: Nature Chemical Biology. 18:394-402

Microbial communities inhabit spatial architectures that divide a global environment into isolated or semi-isolated local environments, which leads to the partitioning of a microbial community into a collection of local communities. Despite its ubiqu

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::23df3e7ad795dd5258ff79f49f498ab3
https://doi.org/10.1038/s41589-021-00961-w

Distributed information encoding and decoding using self-organized spatial patterns

Autor: Jia Lu, Ryan Tsoi, Nan Luo, Yuanchi Ha, Shangying Wang, Minjun Kwak, Yasa Baig, Nicole Moiseyev, Shari Tian, Alison Zhang, Neil Zhenqiang Gong, Lingchong You

Publikováno v: Patterns. 3:100590

Dynamical systems often generate distinct outputs according to different initial conditions, and one can infer the corresponding input configuration given an output. This property captures the essence of information encoding and decoding. Here, we de

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a39e1358449ed9fb38b76b6b5c162e00
https://doi.org/10.1016/j.patter.2022.100590

Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models

Autor: Colton J. Lloyd, Zachary B. Haiman, Daniel C. Zielinski, Martin J. Lercher, Nathan Mih, Abdelmoneim Amer Desouki, David Heckmann, Yuanchi Ha, Bernhard O. Palsson

Publikováno v: Nature Communications, Vol 9, Iss 1, Pp 1-10 (2018)
Heckmann, D, Lloyd, C J, Mih, N, Ha, Y, Zielinski, D C, Haiman, Z B, Desouki, A A, Lercher, M J & Palsson, B O 2018, ' Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models ', Nature Communications, vol. 9, 5252 . https://doi.org/10.1038/s41467-018-07652-6
Nature communications, vol 9, iss 1
Nature Communications

Knowing the catalytic turnover numbers of enzymes is essential for understanding the growth rate, proteome composition, and physiology of organisms, but experimental data on enzyme turnover numbers is sparse and noisy. Here, we demonstrate that machi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6bfa038068e6cf5b7d4f5904e6b70df5
http://link.springer.com/article/10.1038/s41467-018-07652-6

Finding relevant biomedical datasets: the UC San Diego solution for the bioCADDIE Retrieval Challenge

Autor: Zhanglong Ji, Lucila Ohno-Machado, Yupeng He, Kai Zhang, Qi Li, Wei Wei, Yuanchi Ha

Publikováno v: Database: The Journal of Biological Databases and Curation

The number and diversity of biomedical datasets grew rapidly in the last decade. A large number of datasets are stored in various repositories, with different formats. Existing dataset retrieval systems lack the capability of cross-repository search.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::72a7a662a2e57ce7e5d9bf940f9a13c4
https://doi.org/10.1093/database/bay017