Results 141 to 150 of about 317,964 (264)
Causal Graphical Models and Their Applications. [PDF]
Sucar LE, Danks D.
europepmc +1 more source
Directed Graphical Models and Causal Discovery for Zero-Inflated Data. [PDF]
Yu S, Drton M, Shojaie A.
europepmc +1 more source
Time‐resolved X‐ray solution scattering captures how proteins change shape in real time under near‐native conditions. This article presents a practical workflow for light‐triggered TR‐XSS experiments, from data collection to structural refinement. Using a calcium‐transporting membrane protein as an example, the approach can be broadly applied to study ...
Fatemeh Sabzian‐Molaei +3 more
wiley +1 more source
Lipid Class Prediction from MS1 Data using Gaussian Graphical Models. [PDF]
Rix T +7 more
europepmc +1 more source
Amino acids sequence of two different proteins with the same sequence (chameleon sequence—black boxes) represent in 3D structure of the proteins different secondary structures: HHHH—helical and BBB—Beta‐structural. The chains folded in water environment adopt different III‐order structures in which the chameleon fragments appear to adopt similar status
Irena Roterman +4 more
wiley +1 more source
Simultaneous clustering and estimation of networks in multiple graphical models. [PDF]
Li G, Wang M.
europepmc +1 more source
Tree-based Node Aggregation in Sparse Graphical Models. [PDF]
Wilms I, Bien J.
europepmc +1 more source
Development of human monoclonal antibodies against TARM1 by yeast display
Human monoclonal antibodies against TARM1 are generated by yeast display‐guided selection. These antibodies bind to soluble and cell‐surface forms of TARM1. Also, these antibodies exhibit agonistic activity in the NFAT‐GFP reporter assay, indicating that TARM1 signaling can be functionally modulated by antibodies and suggesting TARM1 as a potential ...
Rikio Yabe +5 more
wiley +1 more source
Learning Gaussian Graphical Models from Correlated Data. [PDF]
Song Z +6 more
europepmc +1 more source
This protocol paper outlines methods to establish the success of a time‐resolved serial crystallographic experiment, by means of statistical analysis of timepoint data in reciprocal space and models in real space. We show how to amplify the signal from excited states to visualise structural changes in successful experiments.
Jake Hill +4 more
wiley +1 more source

