Yan Li
Princeton University
7 Papers
13 Citations
Yan Li is an academic researcher from Princeton University. The author has contributed to research in topics: Lasso (statistics) & RNA. The author has an hindex of 3, co-authored 6 publications.
Chat about Author
Papers
High-throughput in vivo mapping of RNA accessible interfaces to identify functional sRNA binding sites
Mia K. Mihailovic,Jorge Vazquez-Anderson,Yan Li,Victoria Fry,Praveen Vimalathas,Daniel Herrera,Richard A. Lease,Warren B. Powell,Lydia M. Contreras +8 more
TL;DR: An intracellular method that quantifies antisense hybridization efficacy of any number of RNA regions simultaneously via a transcriptional elongation output, measurable via RNA-seq is introduced.
Design of Stimuli‐Responsive Peptides and Proteins
TL;DR: In this paper , a review of five intensively studied types of stimuli-responsive peptides and proteins, their design principles and applications, including temperature, pH, ions, enzymes, magnetic field, redox, etc., are explored.
28
•Posted Content
The Knowledge Gradient Policy Using A Sparse Additive Belief Model
Yan Li,Han Liu,Warren B. Powell +2 more
TL;DR: A knowledge gradient policy for sparse linear models (KGSpLin) with group Lasso penalty is derived, which efficiently learns the correct set of nonzero parameters even when the model is imbedded with hundreds of dummy parameters.
Quantifying Experimental Characterization Choices in Optimal Learning and Materials Design
Kristofer G. Reyes,Si Chen,Yan Li,Warren B. Powell +3 more
- 27 Feb 2015
TL;DR: This work considers the choices and subsequent costs associated with ensemble averaging and extrapolating experimental measurements in the context of optimizing material properties using Optimal Learning (OL), and demonstrates how these two general techniques lead to a trade-off between measurement error and experimental costs.
3
A Knowledge Gradient Policy for Sequencing Experiments to Identify the Structure of RNA Molecules Using a Sparse Additive Belief Model
Yan Li,Kristofer G. Reyes,Jorge Vazquez-Anderson,Yingfei Wang,Lydia M. Contreras,Warren B. Powell +5 more
TL;DR: In this article, a sparse knowledge gradient (SpKG) algorithm was proposed to adaptively select the targeted regions within a large RNA molecule to identify which regions are most amenable to interactions with other regions.
3