Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Matthew J, Hirn"'
Autor:
Manik Kuchroo, Marcello DiStasio, Eric Song, Eda Calapkulu, Le Zhang, Maryam Ige, Amar H. Sheth, Abdelilah Majdoubi, Madhvi Menon, Alexander Tong, Abhinav Godavarthi, Yu Xing, Scott Gigante, Holly Steach, Jessie Huang, Guillaume Huguet, Janhavi Narain, Kisung You, George Mourgkos, Rahul M. Dhodapkar, Matthew J. Hirn, Bastian Rieck, Guy Wolf, Smita Krishnaswamy, Brian P. Hafler
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-22 (2023)
Abstract Due to commonalities in pathophysiology, age-related macular degeneration (AMD) represents a uniquely accessible model to investigate therapies for neurodegenerative diseases, leading us to examine whether pathways of disease progression are
Externí odkaz:
https://doaj.org/article/7d518075ed264723a3afcab7d1e39ef3
Autor:
Manik, Kuchroo, Jessie, Huang, Patrick, Wong, Jean-Christophe, Grenier, Dennis, Shung, Alexander, Tong, Carolina, Lucas, Jon, Klein, Daniel B, Burkhardt, Scott, Gigante, Abhinav, Godavarthi, Bastian, Rieck, Benjamin, Israelow, Michael, Simonov, Tianyang, Mao, Ji Eun, Oh, Julio, Silva, Takehiro, Takahashi, Camila D, Odio, Arnau, Casanovas-Massana, John, Fournier, Shelli, Farhadian, Charles S, Dela Cruz, Albert I, Ko, Matthew J, Hirn, F Perry, Wilson, Julie G, Hussin, Guy, Wolf, Akiko, Iwasaki, Yvette, Strong
Publikováno v:
Nat Biotechnol
As the biomedical community produces datasets that are increasingly complex and high dimensional, there is a need for more sophisticated computational tools to extract biological insights. We present Multiscale PHATE, a method that sweeps through all
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dcbc7b5d57e7a395fabb6caf9bc63a6a
https://doi.org/10.1038/s41587-021-01186-x
https://doi.org/10.1038/s41587-021-01186-x
Autor:
Kevin R. Moon, Natalia Ivanova, Zheng Wang, David van Dijk, Scott Gigante, Antonia van den Elzen, William S. Chen, Smita Krishnaswamy, Guy Wolf, Ronald R. Coifman, Matthew J. Hirn, Daniel B. Burkhardt, Kristina Yim
Publikováno v:
Nat Biotechnol
The high-dimensional data created by high-throughput technologies require visualization tools that reveal data structure and patterns in an intuitive form. We present PHATE, a visualization method that captures both local and global nonlinear structu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::73e3810b4f25ab1d3a3810a272ff4713
https://doi.org/10.1038/s41587-019-0336-3
https://doi.org/10.1038/s41587-019-0336-3
Autor:
Tyler Peryea, Ahsan Habib Polash, Alessandra Roncaglioni, Daniel M. Wilson, Warren Casey, Patricia Ruiz, Nathalie Alépée, Sherif Farag, Giovanna J. Lavado, Kimberley M. Zorn, Alexey V. Zakharov, Davide Ballabio, Katrina M. Waters, Risa Sayre, Giuseppe Felice Mangiatordi, Orazio Nicolotti, Nicole Kleinstreuer, Pankaj R. Daga, Sean Ekins, Kamel Mansouri, Liguo Wang, Judy Strickland, Matthew J. Hirn, Sudin Bhattacharya, Dac-Trung Nguyen, Emilio Benfenati, Ignacio J. Tripodi, Amanda K. Parks, Garett Goh, Dennis G. Thomas, Glenn J. Myatt, Prachi Pradeep, Gergely Zahoranszky-Kohalmi, Anton Simeonov, Arthur C. Silva, Grace Patlewicz, Timothy Sheils, Stephen Boyd, Agnes L. Karmaus, Ahmed Sayed, Alex M. Clark, Todd M. Martin, Pavel Karpov, Jeffery M. Gearhart, Robert Rallo, D Allen, Charles Siegel, Zhen Zhang, Zijun Xiao, Alexander Tropsha, Stephen J. Capuzzi, Alexandru Korotcov, Carolina Horta Andrade, Noel Southall, Viviana Consonni, Igor V. Tetko, Jeremy M. Fitzpatrick, Andrew J. Wedlake, Denis Fourches, Zhongyu Wang, Vinicius M. Alves, Eugene N. Muratov, Timothy E. H. Allen, Andrea Mauri, James B. Brown, Alexandre Varnek, Yun Tang, Sanjeeva J. Wijeyesakere, Daniel P. Russo, Cosimo Toma, Christopher M. Grulke, Michael S. Lawless, Domenico Gadaleta, Paritosh Pande, Thomas Hartung, Jonathan M. Goodman, Kristijan Vukovic, Joyce V. Bastos, Daniela Trisciuzzi, Fagen F. Zhang, Domenico Alberga, Thomas Luechtefeld, Dan Marsh, Tyler R. Auernhammer, Shannon M. Bell, Xinhao Li, Brian J. Teppen, F. Lunghini, Sergey Sosnin, Hao Zhu, Feng Gao, Craig Rowlands, Tongan Zhao, R Todeschini, Valery Tkachenko, Francesca Grisoni, Hongbin Yang, Yaroslav Chushak, Maxim V. Fedorov, Heather L. Ciallella, Gilles Marcou
Publikováno v:
Environmental Health Perspectives
Environmental Health Perspectives, National Institute of Environmental Health Sciences, 2021, 129 (4), pp.047013. ⟨10.1289/EHP8495⟩
Environmental Health Perspectives, National Institute of Environmental Health Sciences, 2021, 129 (4), pp.047013. ⟨10.1289/EHP8495⟩
BACKGROUND: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6f10be9a3f401d14e1b5653e1242ab66
https://www.repository.cam.ac.uk/handle/1810/322114
https://www.repository.cam.ac.uk/handle/1810/322114
Publikováno v:
J Comput Chem
This work examines methods for predicting the partition coefficient (log P) for a dataset of small molecules. Here, we use atomic attributes such as radius and partial charge, which are typically used as force field parameters in classical molecular
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3622e4eaa7c0dba6186a791e738ccb1a
https://pubmed.ncbi.nlm.nih.gov/33786857
https://pubmed.ncbi.nlm.nih.gov/33786857
Autor:
Manik Kuchroo, Marcello DiStasio, Eric Song, Eda Calapkulu, Le Zhang, Maryam Ige, Amar H. Sheth, Madhvi Menon, Alexander Tong, Abhinav Godavarthi, Yu Xing, Scott Gigante, Holly Steach, Jessie Huang, Guillaume Huguet, Janhavi Narain, George Mourgkos, Rahul M. Dhodapkar, Matthew J. Hirn, Bastian Rieck, Guy Wolf, Smita Krishnaswamy, Brian P. Hafler
Publikováno v:
SSRN Electronic Journal.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d16c69d1bc9bfc128524430db0a9715b
https://doi.org/10.2139/ssrn.3933294
https://doi.org/10.2139/ssrn.3933294
Autor:
Jialin Liu, Paul Sinz, Kwang Jin Kim, Michael W. Swift, Yue Qi, Xavier Brumwell, Matthew J. Hirn
Publikováno v:
The Journal of chemical physics. 153(8)
The dream of machine learning in materials science is for a model to learn the underlying physics of an atomic system, allowing it to move beyond interpolation of the training set to the prediction of properties that were not present in the original
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c419fa25c5845312094e2fa50895103c
https://pubmed.ncbi.nlm.nih.gov/32872889
https://pubmed.ncbi.nlm.nih.gov/32872889
Autor:
Daniel A. Colón-Ramos, Nathan Brugnone, Mark W. Moyle, Matthew J. Hirn, Guy Wolf, Alex Gonopolskiy, David van Dijk, Kevin R. Moon, Manik Kuchroo, Smita Krishnaswamy
Publikováno v:
Proc IEEE Int Conf Big Data
IEEE BigData
IEEE BigData
Big data often has emergent structure that exists at multiple levels of abstraction, which are useful for characterizing complex interactions and dynamics of the observations. Here, we consider multiple levels of abstraction via a multiresolution geo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ea8416f965a909636a3ae6c202543f05
https://pubmed.ncbi.nlm.nih.gov/32747879
https://pubmed.ncbi.nlm.nih.gov/32747879
Autor:
Anna Little, Matthew J. Hirn
Publikováno v:
Inf inference
We propose a nonlinear, wavelet based signal representation that is translation invariant and robust to both additive noise and random dilations. Motivated by the multi-reference alignment problem and generalizations thereof, we analyze the statistic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3a4d61a56f5eea91d4f8837c5c16a596
http://arxiv.org/abs/1909.11062
http://arxiv.org/abs/1909.11062
Publikováno v:
Wavelets and Sparsity XVIII.
Convolutional neural networks (CNNs) are revolutionizing imaging science for two- and three-dimensional images over Euclidean domains. However, many data sets are intrinsically non-Euclidean and are better modeled through other mathematical structure
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c0cb9f70f48269a81d8020eaf295f02b
https://doi.org/10.1117/12.2529615
https://doi.org/10.1117/12.2529615