An Aromatic-Vinylene Structural Correlate of CO₂ Permeability in Glassy Polymer Membranes Identified by Interpretable Machine Learning

Data and code for: Frequent Mental Distress Across Texas Census Tracts: Social-Environmental Co-Exposure, Spatial Dependence, and Interpretable Machine Learning

kwadwo Frimpong

This repository contains the processed analytic dataset and analysis code supporting the study "Environmental Co-Exposure, Green Space, and Frequent Mental Distress in Texas Census Tracts: An Interpretable Machine Learning and Spatial Analysis." The dataset includes tract-level f…Read more

Interpretable machine-learning risk stratification at diagnosis for 3-year mortality in de novo metastatic prostate cancer (SEER): reproducibility code

Xin Wang, Guanglei Yao, Wei Ding

This archive contains the analysis code, the predictor dictionary, and the retrained primary model objects underlying the manuscript "Interpretable machine-learning risk stratification at the time of diagnosis for 3-year mortality in de novo metastatic prostate cancer: developmen…Read more

Interpretable Machine Learning Recovers Transferable Gamma-Ray Attenuation Laws from A Priori Material Descriptors: Code and Data

Nassar N. Asemi, Abdullah Al Mazrooei, Hanan Akhdar

Code and dataset accompanying the manuscript "Interpretable Machine Learning Recovers Transferable Gamma-Ray Attenuation Laws from A Priori Material Descriptors." Includes the symbolic-regression scripts (PySR) for discovering closed-form mass-attenuation laws of lead-free PEI/me…Read more

Towards Early and Accurate Disease Detection Through Multimodal Predictive Modeling: Fusion of Electronic Health Records, Medical Imaging, And Omics Data Using Interpretable Machine Learning.

Muhammad Ahsan Hayat, Jahangir Baig, Shayan Ahmed, Ahmed Faraz Ayubi

Early detection of disease is a cornerstone for improving patient outcomes, reducing costs, and enabling preventative interventions. Traditional predictive models often rely on a single type of data (e.g., imaging, clinical labs, or genomics). However, human health is inherently…Read more

Also available via: European Organization for Nuclear Research

Discriminating global ore deposit genetic types using chalcopyrite trace elements: Insights from interpretable machine learning

H. Li, Ming-Yu Cao, Ben Qin, Peng-Fei Wang, Le Wang

"Supplementary Table.xlsx" Trace element data of chalcopyrite sulfides and other supplementary tables related to the manuscript. "Code files" Modeling and application code related to the manuscript. Please refer to the manuscript and README.txt for details.

Before the Model: Why Datasets and Data Representation Define What Machine Learning Can Learn

Jean Franck Loa Rojas

Machine learning systems do not learn reality directly; they learn from the representations preserved in their datasets. This structured narrative review examines how dataset purpose, coverage, integrity, labeling, independence, reproducibility, governance, and continuity determi…Read more