Zobrazeno 1 - 10
of 292
pro vyhledávání: '"Eric P Xing"'
Publikováno v:
PLoS Computational Biology, Vol 16, Iss 11, p e1008297 (2020)
In eukaryotes, polyadenylation (poly(A)) is an essential process during mRNA maturation. Identifying the cis-determinants of poly(A) signal (PAS) on the DNA sequence is the key to understand the mechanism of translation regulation and mRNA metabolism
Externí odkaz:
https://doaj.org/article/bab2dcfbd2c042b09ed2b8a7a81f2f08
Autor:
Ankur P Parikh, Ross E Curtis, Irene Kuhn, Sabine Becker-Weimann, Mina Bissell, Eric P Xing, Wei Wu
Publikováno v:
PLoS Computational Biology, Vol 10, Iss 7, p e1003713 (2014)
The HMT3522 progression series of human breast cells have been used to discover how tissue architecture, microenvironment and signaling molecules affect breast cell growth and behaviors. However, much remains to be elucidated about malignant and phen
Externí odkaz:
https://doaj.org/article/44c34867557a4b358d9bcd341fec400c
Autor:
Eric P Xing, Ross E Curtis, Georg Schoenherr, Seunghak Lee, Junming Yin, Kriti Puniyani, Wei Wu, Peter Kinnaird
Publikováno v:
PLoS ONE, Vol 9, Iss 6, p e97524 (2014)
With the continuous improvement in genotyping and molecular phenotyping technology and the decreasing typing cost, it is expected that in a few years, more and more clinical studies of complex diseases will recruit thousands of individuals for pan-om
Externí odkaz:
https://doaj.org/article/4bc064869a404720b64a362d94f3ef7d
Publikováno v:
PLoS ONE, Vol 9, Iss 11, p e113114 (2014)
Computer-mediated communication is driving fundamental changes in the nature of written language. We investigate these changes by statistical analysis of a dataset comprising 107 million Twitter messages (authored by 2.7 million unique user accounts)
Externí odkaz:
https://doaj.org/article/81b27e95f87b48858f2b4d26d5ab9acc
Autor:
Kriti Puniyani, Eric P Xing
Publikováno v:
PLoS Computational Biology, Vol 9, Iss 10, p e1003227 (2013)
Accurate inference of molecular and functional interactions among genes, especially in multicellular organisms such as Drosophila, often requires statistical analysis of correlations not only between the magnitudes of gene expressions, but also betwe
Externí odkaz:
https://doaj.org/article/94ca80c662c048bfbba90da330468ddd
Autor:
Seyoung Kim, Eric P Xing
Publikováno v:
PLoS Genetics, Vol 5, Iss 8, p e1000587 (2009)
Many complex disease syndromes, such as asthma, consist of a large number of highly related, rather than independent, clinical or molecular phenotypes. This raises a new technical challenge in identifying genetic variations associated simultaneously
Externí odkaz:
https://doaj.org/article/1c472cc7a25c4097891cc48d39c081ac
Publikováno v:
PLoS Computational Biology, Vol 4, Iss 6, p e1000090 (2008)
Functional turnover of transcription factor binding sites (TFBSs), such as whole-motif loss or gain, are common events during genome evolution. Conventional probabilistic phylogenetic shadowing methods model the evolution of genomes only at nucleotid
Externí odkaz:
https://doaj.org/article/e18a3700f91143dc970935b61c9f1eba
Autor:
Zhiting Hu, Eric P. Xing
Publikováno v:
Harvard Data Science Review (2022)
Externí odkaz:
https://doaj.org/article/d5cd544a2b4047b7bba918947ab690e2
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 33:7610-7620
Clustering algorithms based on deep neural networks have been widely studied for image analysis. Most existing methods require partial knowledge of the true labels, namely, the number of clusters, which is usually not available in practice. In this a
Publikováno v:
Journal of Computational Biology. 29:1353-1356
We introduce the python software package Kernel Mixed Model (KMM), which allows users to incorporate the network structure into transcriptome-wide association studies (TWASs). Our software is based on the association algorithm KMM, which is a method