Results 191 to 200 of about 2,865,465 (299)

Estimation error in unobserved component models

open access: yes, 1995
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

open access: yesAdvanced Science, EarlyView.
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

Predicting Single‐Cell Perturbation Responses Across Biological Contexts With a Deep Generative Model Integrating Optimal Transport

open access: yesAdvanced Science, EarlyView.
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

Robots and Minimal, Physics‐Informed Features: A Hybrid Framework for Enzyme Catalysis

open access: yesAdvanced Science, EarlyView.
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

Similarity‐Enhanced Representation Learning of Non‐Canonical Amino Acids for Therapeutic Peptide Modeling

open access: yesAdvanced Science, EarlyView.
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

MethyAnno: An Interpretable Automated Annotation Method Leveraging Multi‐Scale Information and Metric Learning Framework for scDNAm Data

open access: yesAdvanced Science, EarlyView.
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

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