Zobrazeno 1 - 10
of 160
pro vyhledávání: '"Joshua T. Vogelstein"'
Autor:
Tianyi Chen, Youngser Park, Ali Saad-Eldin, Zachary Lubberts, Avanti Athreya, Benjamin D. Pedigo, Joshua T. Vogelstein, Francesca Puppo, Gabriel A. Silva, Alysson R. Muotri, Weiwei Yang, Christopher M. White, Carey E. Priebe
Publikováno v:
Applied Network Science, Vol 8, Iss 1, Pp 1-13 (2023)
Abstract Recent advancements have been made in the development of cell-based in-vitro neuronal networks, or organoids. In order to better understand the network structure of these organoids, a super-selective algorithm has been proposed for inferring
Externí odkaz:
https://doaj.org/article/dd431d1a9d754a39a8e3c7919c7480ff
Publikováno v:
Mathematics, Vol 12, Iss 5, p 746 (2024)
We propose and study a data-driven method that can interpolate between a classical and a modern approach to classification for a class of linear models. The class is the convex combinations of an average of the source task classifiers and a classifie
Externí odkaz:
https://doaj.org/article/e87d663eaf48436f850d704983653221
Publikováno v:
Network Neuroscience, Vol 7, Iss 2, Pp 522-538 (2023)
AbstractGraph matching algorithms attempt to find the best correspondence between the nodes of two networks. These techniques have been used to match individual neurons in nanoscale connectomes—in particular, to find pairings of neurons across hemi
Externí odkaz:
https://doaj.org/article/99b045b559b24fd58f00939d8e5499e9
Autor:
Itzy E. Morales Pantoja, Lena Smirnova, Alysson R. Muotri, Karl J. Wahlin, Jeffrey Kahn, J. Lomax Boyd, David H. Gracias, Timothy D. Harris, Tzahi Cohen-Karni, Brian S. Caffo, Alexander S. Szalay, Fang Han, Donald J. Zack, Ralph Etienne-Cummings, Akwasi Akwaboah, July Carolina Romero, Dowlette-Mary Alam El Din, Jesse D. Plotkin, Barton L. Paulhamus, Erik C. Johnson, Frederic Gilbert, J. Lowry Curley, Ben Cappiello, Jens C. Schwamborn, Eric J. Hill, Paul Roach, Daniel Tornero, Caroline Krall, Rheinallt Parri, Fenna Sillé, Andre Levchenko, Rabih E. Jabbour, Brett J. Kagan, Cynthia A. Berlinicke, Qi Huang, Alexandra Maertens, Kathrin Herrmann, Katya Tsaioun, Raha Dastgheyb, Christa Whelan Habela, Joshua T. Vogelstein, Thomas Hartung
Publikováno v:
Frontiers in Artificial Intelligence, Vol 6 (2023)
The brain is arguably the most powerful computation system known. It is extremely efficient in processing large amounts of information and can discern signals from noise, adapt, and filter faulty information all while running on only 20 watts of powe
Externí odkaz:
https://doaj.org/article/aebc0f0cd878418ebed9205a612b1932
Publikováno v:
Communications Biology, Vol 5, Iss 1, Pp 1-11 (2022)
ViterBrain is an automated probabilistic reconstruction method that can reconstruct neuronal geometry and processes from microscopy images with code available in the open-source Python package, brainlit.
Externí odkaz:
https://doaj.org/article/ab6f315413cb476e9daa9967e7bdf0c3
Autor:
Joshua T. Vogelstein, Eric W. Bridgeford, Minh Tang, Da Zheng, Christopher Douville, Randal Burns, Mauro Maggioni
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-9 (2021)
Biomedical measurements usually generate high-dimensional data where individual samples are classified in several categories. Vogelstein et al. propose a supervised dimensionality reduction method which estimates the low-dimensional data projection f
Externí odkaz:
https://doaj.org/article/4e96486444d04876a7a760151e1835c7
Autor:
Ross M. Lawrence, Eric W. Bridgeford, Patrick E. Myers, Ganesh C. Arvapalli, Sandhya C. Ramachandran, Derek A. Pisner, Paige F. Frank, Allison D. Lemmer, Aki Nikolaidis, Joshua T. Vogelstein
Publikováno v:
Scientific Data, Vol 8, Iss 1, Pp 1-9 (2021)
Abstract Using brain atlases to localize regions of interest is a requirement for making neuroscientifically valid statistical inferences. These atlases, represented in volumetric or surface coordinate spaces, can describe brain topology from a varie
Externí odkaz:
https://doaj.org/article/0a03d07c74214af68dc50816185fd573
Autor:
Ketan Mehta, Rebecca F. Goldin, David Marchette, Joshua T. Vogelstein, Carey E. Priebe, Giorgio A. Ascoli
Publikováno v:
Network Neuroscience, Vol 5, Iss 3, Pp 689-710 (2021)
AbstractThis work presents a novel strategy for classifying neurons, represented by nodes of a directed graph, based on their circuitry (edge connectivity). We assume a stochastic block model (SBM) in which neurons belong together if they connect to
Externí odkaz:
https://doaj.org/article/be9035929960411f89afef38f09a14f1
Autor:
Shilong Li, Tomi Jun, Jonathan Tyler, Emilio Schadt, Yu-Han Kao, Zichen Wang, Maximilian F. Konig, Chetan Bettegowda, Joshua T. Vogelstein, Nickolas Papadopoulos, Ramon E. Parsons, Rong Chen, Eric E. Schadt, Li Li, William K. Oh
Publikováno v:
Frontiers in Medicine, Vol 9 (2022)
Apha-1-adrenergic receptor antagonists (α1-blockers) can suppress pro-inflammatory cytokines, thereby potentially improving outcomes among patients with COVID-19. Accordingly, we evaluated the association between α1-blocker exposure (before or duri
Externí odkaz:
https://doaj.org/article/1575e73295f84b33af980c1f36a8943b
Autor:
Marc-Andre Schulz, B. T. Thomas Yeo, Joshua T. Vogelstein, Janaina Mourao-Miranada, Jakob N. Kather, Konrad Kording, Blake Richards, Danilo Bzdok
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-15 (2020)
Schulz et al. systematically benchmark performance scaling with increasingly sophisticated prediction algorithms and with increasing sample size in reference machine-learning and biomedical datasets. Complicated nonlinear intervariable relationships
Externí odkaz:
https://doaj.org/article/b1ee0d1de11c40d8869725556c90d89f