Results 151 to 160 of about 167,650 (307)
Advanced Science, EarlyView.An interpretable machine learning framework integrating SHAP and PDP analysis identifies critical design descriptors from 139 physicochemical features for Nb─Si alloys. The framework achieves <7% prediction error and guides the discovery of Nb38.5Ti38.5Si3Zr18V2 alloy with 22.791 MPa·m1/2 fracture toughness, breaking the 20 MPa·m1/2 barrier.
Dezhi Chen +7 more
wiley +1 more source
Topology‐Aware Deep Learning on Higher‐Order Structures for Drug Response Prediction
Advanced Science, EarlyView.We present TopDr, a topology‐aware deep learning framework that encodes both drugs and cell lines as multiscale simplicial complexes, capturing interactions at the 0‐, 1‐, and 2‐simplex levels. By jointly integrating local higher‐order neighborhoods and global topological structures, TopDr generates enriched representations for sensitivity prediction ...
Cong Shen +3 more
wiley +1 more source
Comparison of three methods’ RMSE on Flixster.
, 2016 Comparison of three methods’ RMSE on Flixster.
Weijie Cheng (548016) +4 more
core +1 more source
Advanced Science, EarlyView.A new data‐efficient framework combining DFT calculations, a neural network model, and automated graph analysis of catalytic reaction networks is proposed and applied to CO2 hydrogenation on transition metal nanoparticles. The analysis shows how efficient C2 oxygenate production requires a balance between CHx formation, C–C coupling, protonation, and ...
Mikhail V. Polynski, Sergey M. Kozlov
wiley +1 more source
A Strategy for Using Bias and RMSE as Outcomes in Monte Carlo Studies in Statistics
Journal of Modern Applied Statistical Methods, 2019 To help ensure important patterns of bias and accuracy are detected in Monte Carlo studies in statistics this paper proposes conditioning bias and root mean square error (RMSE) measures on estimated Type I and Type II error rates. A small Monte Carlo study is used to illustrate this argument.
openaire +2 more sources
MAE and RMSE of the dataset [37].
, 2019 MAE and RMSE of the dataset [37].
Tariq Bashir (127259) +4 more
core +1 more source
Discriminator‐Guided Inverse Folding for Multi‐Property Protein Design
Advanced Science, EarlyView.Discriminator‐Guided Inverse Folding (DGIF) integrates multiple property predictors trained from single‐property datasets to guide protein sequence generation from a backbone structure. DGIF enables simultaneous improvement of thermostability and solubility without requiring multi‐property annotated datasets and generates designs that move toward the ...
Yuchuan Zheng +7 more
wiley +1 more source
Heatmap with RMSE scores for different methods and case studies.
, 2017 The color scheme represents RMSE scores normalized by case-study in order to emphasize differences between methods. The color scale moves from green (low RMSE) to blue (high RMSE). The numeric values of the RMSE scores for each method/case-study are also
Alejandro F. Villaverde (313992) +4 more
core +1 more source
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
On the Mathematical Relationship Between RMSE and NSE Across Evaluation Scenarios
Model evaluation metrics play a crucial role in hydrology, where accurate prediction of continuous variables such as streamflow and rainfall–runoff is essential for sustainable water resources management and climate resilience. Among these metrics, the Nash–Sutcliffe efficiency (NSE) is the most widely adopted, while the Root Mean Squared Error (RMSE ...
openaire +1 more source