Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Ding, Daisy"'
We propose Cooperative Component Analysis (CoCA), a new method for unsupervised multi-view analysis: it identifies the component that simultaneously captures significant within-view variance and exhibits strong cross-view correlation. The challenge o
Externí odkaz:
http://arxiv.org/abs/2407.16870
Autor:
Liang, Weixin, Zhang, Yuhui, Cao, Hancheng, Wang, Binglu, Ding, Daisy, Yang, Xinyu, Vodrahalli, Kailas, He, Siyu, Smith, Daniel, Yin, Yian, McFarland, Daniel, Zou, James
Expert feedback lays the foundation of rigorous research. However, the rapid growth of scholarly production and intricate knowledge specialization challenge the conventional scientific feedback mechanisms. High-quality peer reviews are increasingly d
Externí odkaz:
http://arxiv.org/abs/2310.01783
Multiomics data fusion integrates diverse data modalities, ranging from transcriptomics to proteomics, to gain a comprehensive understanding of biological systems and enhance predictions on outcomes of interest related to disease phenotypes and treat
Externí odkaz:
http://arxiv.org/abs/2308.01458
While RNA technologies hold immense therapeutic potential in a range of applications from vaccination to gene editing, the broad implementation of these technologies is hindered by the challenge of delivering these agents effectively. Lipid nanoparti
Externí odkaz:
http://arxiv.org/abs/2308.01402
We propose a new method for supervised learning with multiple sets of features ("views"). The multiview problem is especially important in biology and medicine, where "-omics" data such as genomics, proteomics and radiomics are measured on a common s
Externí odkaz:
http://arxiv.org/abs/2112.12337
Machine learning with missing data has been approached in two different ways, including feature imputation where missing feature values are estimated based on observed values, and label prediction where downstream labels are learned directly from inc
Externí odkaz:
http://arxiv.org/abs/2010.16418
Autor:
Fleming, Scott L., Jeyapragasan, Kuhan, Duan, Tony, Ding, Daisy, Gombar, Saurabh, Shah, Nigam, Brunskill, Emma
There is an emerging trend in the reinforcement learning for healthcare literature. In order to prepare longitudinal, irregularly sampled, clinical datasets for reinforcement learning algorithms, many researchers will resample the time series data to
Externí odkaz:
http://arxiv.org/abs/1911.07084
Autor:
Duan, Tony, Avati, Anand, Ding, Daisy Yi, Thai, Khanh K., Basu, Sanjay, Ng, Andrew Y., Schuler, Alejandro
We present Natural Gradient Boosting (NGBoost), an algorithm for generic probabilistic prediction via gradient boosting. Typical regression models return a point estimate, conditional on covariates, but probabilistic regression models output a full p
Externí odkaz:
http://arxiv.org/abs/1910.03225
The use of machine learning systems to support decision making in healthcare raises questions as to what extent these systems may introduce or exacerbate disparities in care for historically underrepresented and mistreated groups, due to biases impli
Externí odkaz:
http://arxiv.org/abs/1907.06260
The Impression section of a radiology report summarizes crucial radiology findings in natural language and plays a central role in communicating these findings to physicians. However, the process of generating impressions by summarizing findings is t
Externí odkaz:
http://arxiv.org/abs/1809.04698