Results 91 to 100 of about 2,644 (209)

Exploring Database Quality Through Shapley Values: Application to Dynamic Soil Parameters Databases

Geotechnics
Geotechnical engineering faces challenges related to data, especially the ones related to dynamic soil behavior (i.e., shear modulus reduction and damping ratio curves with strain), with only a few datasets in open-access format and a slow transition to ...
Julien Borderon, Nathalie Dufour, Julie Régnier   +2 more
doaj   +1 more source

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Advanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

ORTHOGONAL PERMUTATION SAMPLING FOR SHAPLEY VALUES: UNBIASED STRATIFIED ESTIMATORS WITH VARIANCE GUARANTEES

Journal of Intelligent Systems and Applied Data Science
Shapley values for feature attribution suffer from high variance requiring thousands of model evaluations. We introduce Orthogonal Permutation Sampling (OPS), achieving provable variance reduction through: (i) exact position stratification, (ii ...
YASH VARSHNEY, RANAV TYAGI, ANURAG SINHA
doaj   +1 more source

AS‐pHopt: An Optimal pH Prediction Model Enhanced by Active Site of Enzymes

Advanced Intelligent Discovery, EarlyView.
To address the low accuracy of enzyme optimal pH (pHopt) prediction, this study develops active site‐based pHopt (AS‐pHopt), a prediction model enhanced by active site information and pseudo‐label prediction. Integrating key structural and physicochemical features affecting enzyme pHopt, AS‐pHopt uses Evolutionary Scale Modeling (ESM)‐2 with active ...
Wenxiang Song, Xinmin Li, Hongbo Yu, Le Xiong, Guixia Liu, Weihua Li, Yun Tang   +6 more
wiley   +1 more source

Conditional Feature Selection: Evaluating Model Averaging When Selecting Features with Shapley Values

Geomatics
In the field of geomatics, artificial intelligence (AI) and especially machine learning (ML) are rapidly transforming the field of geomatics with respect to collecting, managing, and analyzing spatial data.
Florian Huber, Volker Steinhage
doaj   +1 more source

Deep Learning–Based Extraction of Promising Material Groups and Common Features from High‐Dimensional Data: A Case of Optical Spectra of Inorganic Crystals

Advanced Intelligent Discovery, EarlyView.
We report a novel interpretation method for deep learning models based on feature extraction and clustering. Applying this method to an atomistic line graph neural network (ALIGNN) model trained on optical absorption spectra of 2,681 inorganic compounds obtained from first‐principles calculations, we successfully identify key factors underlying ...
Akira Takahashi, Yu Kumagai, Arata Takamatsu, Fumiyasu Oba   +3 more
wiley   +1 more source

A three-step machine learning strategy for reproducible biomarker screening in ovarian cancer

Machine Learning: Health
Biomarkers have a significant impact in health and medicine, including cancer research, screening, and diagnosis. Traditional statistical tests, however, often fail to provide consistent and reproducible quantitative measures of biomarker effects which ...
W Huang, H Suominen, T Liu, G Rice, C Salomon, A S Barnard   +5 more
doaj   +1 more source

Interpretable Machine Learning for Bandgap Prediction and Descriptor‐Guided Design Rules of Phosphates

Advanced Intelligent Discovery, EarlyView.
An explainable CatBoost model was trained to predict the bandgaps of 474 phosphate crystals based on composition and density descriptors. SHAP analysis identified two key variables—d‐electron‐count dispersion and atomic‐density dispersion—as the primary drivers of the model's predictions.
Wenhu Wang, Abudukadi Tudi, Ran An, Zhihua Yang   +3 more
wiley   +1 more source

GraphX-Net: A Graph Neural Network-Based Shapley Values for Predicting Breast Cancer Occurrence

IEEE Access
Breast cancer is a major health problem worldwide, and an accurate prediction of its recurrence is crucial to early detection of recurrence and personalized treatment.
Abdullah Basaad, Shadi Basurra, Edlira Vakaj, Mohammed Aleskandarany, Mohammed M. Abdelsamea   +4 more
doaj   +1 more source

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source