Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Asim Anees"'
Autor:
Rebecca C. Poulos, Peter G. Hains, Rohan Shah, Natasha Lucas, Dylan Xavier, Srikanth S. Manda, Asim Anees, Jennifer M. S. Koh, Sadia Mahboob, Max Wittman, Steven G. Williams, Erin K. Sykes, Michael Hecker, Michael Dausmann, Merridee A. Wouters, Keith Ashman, Jean Yang, Peter J. Wild, Anna deFazio, Rosemary L. Balleine, Brett Tully, Ruedi Aebersold, Terence P. Speed, Yansheng Liu, Roger R. Reddel, Phillip J. Robinson, Qing Zhong
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-13 (2020)
Clinical proteomics critically depends on the ability to acquire highly reproducible data over an extended period of time. Here, the authors assess reproducibility over four months across different mass spectrometers and develop a computational appro
Externí odkaz:
https://doaj.org/article/f31d48fe5cbd4e4fb32261099a109959
Publikováno v:
Remote Sensing, Vol 11, Iss 17, p 2057 (2019)
This paper assesses the performance of DoTRules—a dictionary of trusted rules—as a supervised rule-based ensemble framework based on the mean-shift segmentation for hyperspectral image classification. The proposed ensemble framework consists of m
Externí odkaz:
https://doaj.org/article/44bb886977874555ac356c4047f9c53d
Autor:
Qing Zhong, Sun Rui, Adel T. Aref, Zainab Noor, Asim Anees, Yi Zhu, Natasha Lucas, Rebecca C. Poulos, Mengge Lyu, Tiansheng Zhu, Bo Wang, Guo-Bo Chen, Yingrui Wang, Xuan Ding, Dorothea Rutishauser, Niels J. Rupp, Jan H. Rueschoff, Cédric Poyet, Thomas Hermanns, Christian Fankhauser, María Rodríguez Martínez, Wenguang Shao, Marija Buljan, Janis Frederick Neumann, Andreas Beyer, Peter G. Hains, Roger R. Reddel, Phillip J. Robinson, Ruedi Aebersold, Tiannan Guo, Peter J. Wild
Gleason grading is an important prognostic indicator for prostate adenocarcinoma and is crucial for patient treatment decisions. However, intermediate-risk patients diagnosed in Gleason Grade Groups (GG) 2 and GG3 can harbour either aggressive or non
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d2f4ccbf04dadb90601b2263971c4a9e
https://doi.org/10.1101/2023.03.03.530910
https://doi.org/10.1101/2023.03.03.530910
Autor:
Keith Ashman, Asim Anees, Terence P. Speed, Erin K. Sykes, Roger R. Reddel, Yansheng Liu, Jennifer M. S. Koh, Jean Yang, Merridee A. Wouters, Steven G. Williams, Peter J. Wild, Anna deFazio, Natasha Lucas, Max Wittman, Dylan Xavier, Michael Hecker, Sadia Mahboob, Michael Dausmann, Ruedi Aebersold, Peter G. Hains, Brett Tully, Rohan Shah, Phillip J. Robinson, Qing Zhong, Rosemary L. Balleine, Srikanth S. Manda, Rebecca C. Poulos
Publikováno v:
Nature Communications, 11 (1)
Nature Communications, Vol 11, Iss 1, Pp 1-13 (2020)
Nature Communications
Nature Communications, Vol 11, Iss 1, Pp 1-13 (2020)
Nature Communications
Reproducible research is the bedrock of experimental science. To enable the deployment of large-scale proteomics, we assess the reproducibility of mass spectrometry (MS) over time and across instruments and develop computational methods for improving
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d72057dd71a63badf92c2a0858853f64
https://hdl.handle.net/20.500.11850/430457
https://hdl.handle.net/20.500.11850/430457
Publikováno v:
ISPRS Journal of Photogrammetry and Remote Sensing. 122:167-178
A robust non-parametric framework, based on multiple Radial Basic Function (RBF) kernels, is proposed in this study, for detecting land/forest cover changes using Landsat 7 ETM+ images. One of the widely used frameworks is to find change vectors (dif
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fbb9b398872af821dd2db4270d0516bf
https://doi.org/10.1016/j.isprsjprs.2016.10.011
https://doi.org/10.1016/j.isprsjprs.2016.10.011
Publikováno v:
Remote Sensing
Volume 11
Issue 17
Remote Sensing, Vol 11, Iss 17, p 2057 (2019)
Volume 11
Issue 17
Remote Sensing, Vol 11, Iss 17, p 2057 (2019)
This paper assesses the performance of DoTRules&mdash
a dictionary of trusted rules&mdash
as a supervised rule-based ensemble framework based on the mean-shift segmentation for hyperspectral image classification. The proposed ensemble frame
a dictionary of trusted rules&mdash
as a supervised rule-based ensemble framework based on the mean-shift segmentation for hyperspectral image classification. The proposed ensemble frame
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::758022bfb0f1f58bf938be643adc9ba1
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 9:3359-3371
To improve statistical approaches for near real-time land cover change detection in nonGaussian time-series data, we propose a supervised land cover change detection framework in which a MODIS NDVI time series is modeled as a triply modulated cosine
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7c182cbe69fa58ded0cb8f460fc53cc2
https://doi.org/10.1109/jstars.2015.2428306
https://doi.org/10.1109/jstars.2015.2428306
Autor:
Asim Anees, Jagannath Aryal
Publikováno v:
IEEE Geoscience and Remote Sensing Letters. 11:1717-1721
Beetle infestations have caused significant damage to the pine forest in North America. Early detection of beetle infestation in near real time is crucial, in order to take appropriate steps to control the damage. In this letter, we consider near-rea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c345b1d893f6e99f19eb7d88a031cc7d
https://doi.org/10.1109/lgrs.2014.2306712
https://doi.org/10.1109/lgrs.2014.2306712
Autor:
Jagannath Aryal, Asim Anees
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 7:3713-3723
This paper considers near-real time detection of beetle infestation in North American pine forests using MODIS 8-days 500 m data. Two methods are considered, both using a single time series for detection of beetle infestation by analyzing the statist
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dd59b03ce7653bdb7ee4accc94d91bc0
https://doi.org/10.1109/jstars.2014.2330830
https://doi.org/10.1109/jstars.2014.2330830
Publikováno v:
IGARSS
The paper considers the detection of beetle infestations in North American pine forests using high temporal resolution, coarse spatial resolution MODIS remotely sensed satellite images. Two methods are proposed to detect beetle infestation, both appl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::07881bdb3f65ec45b44b45e41a0b0aa5
https://doi.org/10.1109/igarss.2013.6723540
https://doi.org/10.1109/igarss.2013.6723540