Christopher J. Langmead

TL;DR: A new approach to learning statistical models from multiple sequence alignments (MSA) of proteins, called GREMLIN (Generative REgularized ModeLs of proteINs), learns an undirected probabilistic graphical model of the amino acid composition within the MSA, which encodes both the position‐specific conservation statistics and the correlated mutation statistics between sequential and long‐range pairs of residues.

TL;DR: The existing scoring systems seem to have reached their maximal efficacy in predicting persistent organ failure in acute pancreatitis, and 12 predictive rules that combined these scores to optimize predictive accuracy are developed.

TL;DR: This work presents the first algorithm for performing statistical Model Checking using Bayesian Sequential Hypothesis Testing, and shows that this Bayesian approach outperforms current statistical Model checking techniques, which rely on tests from Classical statistics, by requiring fewer system simulations.

TL;DR: The PDAC-specific biomarker panels identified in this investigation warrant additional clinical validation to determine their role in screening targeted high-risk populations.

TL;DR: An algorithm, called BioLab, for verifying temporal properties of rule-based models of cellular signalling networks, encoded in the BioNetGen language, and properties are expressed as formulae in probabilistic bounded linear temporal logic.