Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Athanasios N. Nikolakopoulos"'
Autor:
Zicheng Wang, Yunong Xia, Lauren Mills, Athanasios N. Nikolakopoulos, Nicole Maeser, Scott M. Dehm, Jason M. Sheltzer, Ruping Sun
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-16 (2024)
Abstract The timing and fitness effect of somatic copy number alterations (SCNA) in cancer evolution remains poorly understood. Here we present a framework to determine the timing of a clonal SCNA that encompasses multiple gains. This involves calcul
Externí odkaz:
https://doaj.org/article/a2aa4e2c332c42c18d976ced25f4b625
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2018, Iss 1, Pp 1-17 (2018)
Abstract The present work introduces the hybrid consensus alternating direction method of multipliers (H-CADMM), a novel framework for optimization over networks which unifies existing distributed optimization approaches, including the centralized an
Externí odkaz:
https://doaj.org/article/4e760d8da72647b393e5bd3eca5bf801
Publikováno v:
PLoS Computational Biology, Vol 17, Iss 3, p e1008838 (2021)
Can metastatic-primary (M-P) genomic divergence measured from next generation sequencing reveal the natural history of metastatic dissemination? This remains an open question of utmost importance in facilitating a deeper understanding of metastatic p
Externí odkaz:
https://doaj.org/article/5683d767de31489bb32ef80f146c651e
Autor:
Zicheng Wang, Yunong Xia, Lauren Mills, Athanasios N. Nikolakopoulos, Nicole Maeser, Jason M. Sheltzer, Ruping Sun
Charting the evolutionary history of rampant somatic copy number alterations (SCNA) is an indispensable step toward a deeper understanding of their roles in tumor development. However, the existing SCNA timing analysis is limited to low copy number s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6ffe15147d128ec384e3686ddd4f5ae3
https://doi.org/10.1101/2022.06.14.495959
https://doi.org/10.1101/2022.06.14.495959
Publikováno v:
ACM Transactions on Knowledge Discovery from Data. 14:1-26
Item-based models are among the most popular collaborative filtering approaches for building recommender systems. Random walks can provide a powerful tool for harvesting the rich network of interactions captured within these models. They can exploit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4eb22ab2aa0ef59003ba1424fcc8228f
https://doi.org/10.1145/3406241
https://doi.org/10.1145/3406241
Publikováno v:
Recommender Systems Handbook ISBN: 9781071621967
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::604be330ebb0edcca2c7628b023ba705
https://doi.org/10.1007/978-1-0716-2197-4_2
https://doi.org/10.1007/978-1-0716-2197-4_2
Publikováno v:
Knowledge and Information Systems. 58:59-81
We introduce EigenRec; a versatile and efficient Latent-Factor framework for Top-N Recommendations that includes the well-known PureSVD algorithm as a special case. EigenRec builds a low dimensional model of an inter-item proximity matrix that combin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::728925c984a62a67a08b58089377d7cd
https://doi.org/10.1007/s10115-018-1197-7
https://doi.org/10.1007/s10115-018-1197-7
Publikováno v:
RecSys
This paper introduces PerDif; a novel framework for learning personalized diffusions over item-to-item graphs for top-n recommendation. PerDif learns the teleportation probabilities of a time-inhomogeneous random walk with restarts capturing a user-s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::382a32c61cfcc72fd77cac2bc8ad63ea
https://doi.org/10.1145/3298689.3346985
https://doi.org/10.1145/3298689.3346985
Publikováno v:
WSDM
Random walks can provide a powerful tool for harvesting the rich network of interactions captured within item-based models for top-n recommendation. They can exploit indirect relations between the items, mitigate the effects of sparsity, ensure wider
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ed3aa35ac05821c6846277c8bc28e653
https://doi.org/10.1145/3289600.3291016
https://doi.org/10.1145/3289600.3291016
Publikováno v:
IEEE BigData
Diffusion-based classifiers such as those relying on the Personalized PageRank and the Heat kernel, enjoy remarkable classification accuracy at modest computational requirements. Their performance however is affected by the extent to which the chosen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9b64ebc90bbc15ad4c8029f515d20a10
https://doi.org/10.1109/bigdata.2018.8622130
https://doi.org/10.1109/bigdata.2018.8622130