Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Rajmonda S. Caceres"'
Autor:
Lin Li, Esther Gupta, John Spaeth, Leslie Shing, Rafael Jaimes, Emily Engelhart, Randolph Lopez, Rajmonda S. Caceres, Tristan Bepler, Matthew E. Walsh
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-12 (2023)
Abstract Therapeutic antibodies are an important and rapidly growing drug modality. However, the design and discovery of early-stage antibody therapeutics remain a time and cost-intensive endeavor. Here we present an end-to-end Bayesian, language mod
Externí odkaz:
https://doaj.org/article/4b310d77320b4b48bba9cd5498b95743
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
Abstract COVID-19 epidemics have varied dramatically in nature across the United States, where some counties have clear peaks in infections, and others have had a multitude of unpredictable and non-distinct peaks. Our lack of understanding of how the
Externí odkaz:
https://doaj.org/article/4af6e01c390d4777963f86cf0d7b3f10
Publikováno v:
Physical Review X, Vol 7, Iss 3, p 031056 (2017)
Applied network science often involves preprocessing network data before applying a network-analysis method, and there is typically a theoretical disconnect between these steps. For example, it is common to aggregate time-varying network data into wi
Externí odkaz:
https://doaj.org/article/6351e7c8c81d4a1b813a0bd5c48f5178
Autor:
Mohammed Eslami, Aaron Adler, Rajmonda S. Caceres, Joshua G. Dunn, Nancy Kelley-Loughnane, Vanessa A. Varaljay, Hector Garcia Martin
Publikováno v:
Communications of the ACM. 65:88-97
The opportunities and challenges of adapting and applying AI principles to synbio.
Publikováno v:
Scientific Reports
Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
COVID-19 epidemics have varied dramatically in nature across the United States, where some counties have clear peaks in infections, and others have had a multitude of unpredictable and non-distinct peaks. Our lack of understanding of how the pandemic
Publikováno v:
Applied Network Science, Vol 6, Iss 1, Pp 1-20 (2021)
Complex networks are often either too large for full exploration, partially accessible, or partially observed. Downstream learning tasks on these incomplete networks can produce low quality results. In addition, reducing the incompleteness of the net
Publikováno v:
Complex Networks and Their Applications VIII ISBN: 9783030366865
COMPLEX NETWORKS (1)
COMPLEX NETWORKS (1)
Complex networks are often either too large for full exploration, partially accessible, or partially observed. Downstream learning tasks on these incomplete networks can produce low quality results. In addition, reducing the incompleteness of the net
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0a944e5457d71eb0168f958bc2fabd49
https://doi.org/10.1007/978-3-030-36687-2_75
https://doi.org/10.1007/978-3-030-36687-2_75
Autor:
Christine Klymko, Yinghui Wu, Lawrence B. Holder, Rajmonda S. Caceres, Ravi Kumar, Maleq Khan, Nitesh V. Chawla, Jason Riedy, David F. Gleich, Tina Eliassi-Rad, Aditya Prakash
Publikováno v:
ACM SIGKDD Explorations Newsletter. 18:39-45
We report on the Second Workshop on Mining Networks and Graphs held at the 2015 SIAM International Conference on Data Mining. This half-day workshop consisted of a keynote talk, four technical paper presentations, one demonstration, and a panel on fu
Publikováno v:
American Physical Society
Physical Review X, Vol 7, Iss 3, p 031056 (2017)
Physical review. X
Physical Review X, Vol 7, Iss 3, p 031056 (2017)
Physical review. X
Applied network science often involves preprocessing network data before applying a network-analysis method, and there is typically a theoretical disconnect between these steps. For example, it is common to aggregate time-varying network data into wi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2af4a0db35732e3c3ea6fa291dd3ab29
http://hdl.handle.net/1721.1/114446
http://hdl.handle.net/1721.1/114446
Publikováno v:
ICASSP
A new approach for targeted graph sampling is proposed in which graph sampling and classification occur together, and content-based homophily is exploited to achieve improved classification performance. The application of network discovery of relevan