Results 141 to 150 of about 45,420 (307)
Advanced Science, EarlyView.Antimicrobial resistance caused by Gram‐negative bacteria remains difficult to overcome due to the protective outer membrane. To address this challenge, a multi‐condition constrained generative AI framework, GenMTAMP is proposed for de novo membrane‐targeting antimicrobial peptide design by integrating physicochemical and spatial structure descriptors.
Jingxiao Yu +5 more
wiley +1 more source
Nonparametric time trends in optimal design of experiments. [PDF]
When performing an experiment, the observed responses are often influenced by a temporal trend due to aging of material, learning effects, equipment wear-out, warm-up effects, etc. The construction of run orders that are optimally balanced for time trend
Tack, Lieven, Vandebroek, Martina
core
Adaptive Kernel Smoothing Regression for Spatio-Temporal Environmental Datasets
, 2012 A Method for Performing Kernel Smoothing Regression in an Incremental, Adaptive Manner is Described. a Simple and Fast Combination of Incremental Vector Quantization with Kernel Smoothing Regression using Adaptive Bandwidth is Shown to Be Effective for ...
Montesino Pouzols, Federico +1 more
core +1 more source
Advanced Science, EarlyView.This study proposed a unified sequence‐based framework for protein binding site prediction, which adopted a tri‐track semantic multi‐source feature fusion strategy to effectively capture diverse macromolecular interaction sites and further improved the accuracy of antibody‐antigen interaction prediction.
Dongliang Hou +8 more
wiley +1 more source
AI‐Physics‐Experiment Trinity for Integrated Protein Dynamics Modeling
Advanced Science, EarlyView.This review unites experiments, physics‐based simulations, and AI as a synergistic triad for protein dynamics modeling. It highlights integrative strategies, resolves sampling and forcefield bottlenecks, and outlines challenges and future directions for accurate, interpretable conformational ensemble prediction.
Chen Shi +4 more
wiley +1 more source
Inference on semiparametric models with discrete regressors [PDF]
, 1993 We study statistical properties of coefficient estimates of the partially linear regression model when some or all regressors, in the unknown part of the model, are discrete. The method does not require smoothing in the discrete variables.
Delgado, Miguel A. +3 more
core
Advanced Science, EarlyView.SKALE 2.0 maps disease‐associated protein aggregation as a phase‐resolved structural process, linking mutation‐induced geometric perturbations to nucleation, elongation, and suppressor design. Across neurodegenerative proteins, the framework reveals cryptic aggregation vulnerabilities, separates phase‐concordant and phase‐switching mutations, and ...
Jia Shen Sio +6 more
wiley +1 more source
Transformation kernel density estimation of actuarial loss functions
A transformation kernel density estimator that is suitable for heavy-tailed distributions is discussed. Using a truncated Beta transformation, the choice of the bandwidth parameter becomes straightforward.
Montserrat Guillen (Universitat de Barcelona) +2 more
core
An adaptive kernel smoothing method for classifying Austrosimulium tillyardianum (Diptera: Simuliidae) larval instars. [PDF]
J Insect Sci, 2015 Cen G +6 more
europepmc +1 more source
Advanced Science, EarlyView.A gap‐free genome assembly and multi‐omics comparison of the terrestrial slug Laevichaulis alte with an aquatic relative reveal that expansion of the VEGF family orchestrates mucus production, lipid metabolism, and immune defense—highlighting key molecular innovations for conquering life on land.
Gang Wang +19 more
wiley +1 more source