Results 191 to 200 of about 2,865,465 (299)
Estimation error in unobserved component models
Examining board: Prof. Luc Bauwens, C.O.R.E. ; Prof. Andrew Harvey, LSE ; Prof. Augustín Maravall, EUI (Supervisor) ; Prof. Grayham Mizon, EUI ; Prof.
openaire +1 more source
Quantum‐Trained AI Enables Inverse Design of Organic Frontier Orbitals at Billion‐Scale
Quantum‐trained AI links molecular substructures to frontier‐orbital energetics and enables interpretable screening across nearly one billion GDB‐13 molecules. By combining fragment‐ and ring‐level insights with donor–acceptor energy alignment against ITIC, the framework narrows an immense chemical space to a small set of promising candidates and ...
Yeongnam Ko, Se Jin Kim, Ki Chul Kim
wiley +1 more source
MAAMOUL: metabolic network-based discovery of microbiome-metabolome shifts in disease. [PDF]
Muller E, Baum S, Borenstein E.
europepmc +1 more source
Single‐cell perturbation responses are predicted across held‐out biological contexts using scPILOT, a query‐conditioned two‐stage latent response‐transfer framework. A shared latent representation supports cell‐level response estimation by latent optimal transport, followed by Leiden‐localized query‐specific transfer and adaptive weighting.
Jialiang Wang +10 more
wiley +1 more source
Interpersonal Communication and Maternal Behavioral Practice: A Case Study of Explanatory Causal Machine Learning in Nepal. [PDF]
Kelcey B, Cunningham K, Frongillo EA.
europepmc +1 more source
Robots and Minimal, Physics‐Informed Features: A Hybrid Framework for Enzyme Catalysis
Robotic experimentation and physics‐informed machine learning combine to predict enzyme substrate scope. With a handful of interpretable features derived from docking and quantum mechanics calculations, our model rivals descriptor‐heavy AI approaches and extrapolates to unseen substrates and enzyme classes.
Natalia Onishchenko +8 more
wiley +1 more source
Impact of environmental covariates summarization on predictive ability in genomic selection. [PDF]
Sagae VS +4 more
europepmc +1 more source
Non‐canonical amino acids (ncAAs) enhance peptide therapeutics but remain difficult to model computationally. SinCAA, a similarity‐enhanced pretraining framework, jointly optimizes contrastive learning guided by a novel conformational similarity metric with masked node reconstruction, capturing both functional relationships and chemical identity of ...
Chencheng Xu +8 more
wiley +1 more source
An operational target trial emulation framework for causal inference using electronic health record data. [PDF]
Wang Y, Li Y, Lin T, Guo Y.
europepmc +1 more source
MethyAnno enables robust and interpretable annotation of single‐cell DNA methylation data by integrating multi‐scale epigenetic information, bidirectional cross‐attention, and prototype‐based metric learning. The framework resolves rare and novel cell types across datasets while revealing cell‐type‐specific epigenetic signatures associated with disease
Yuhang Jia +4 more
wiley +1 more source

