CCL4 as a potential immune-metabolic biomarker in acute myocardial infarction via machine learning and single-cell transcriptomics

Data and Code for: Graph convolutional network model of CD4+ T cells provides an optimal single-cell clock for human age prediction

Qingqing Hao, Jun Zhang, M H Zhao, Min Wang, Fanglin Guan, Jiangwei Yan

OverviewThis repository contains the code and processed datasets for the manuscript: “Graph convolutional network model of CD4+ T cells provides an optimal single-cell clock for human age prediction”.This study demonstrates that utilizing single-cell Graph Convolutional Networks…Read more

PULSE-72: Single-Cell Pulse and Drive-Cycle Dataset for ARGUS (Mixed-Training Deep Learning for Equivalent Circuit Parameter Identification in Lithium-Ion Batteries)

A. P. Druzhinin

The dataset was acquired from tests of a commercial LG INR18650 MJ1 lithium-ion cell (nominal capacity 3500 mAh).For equivalent-circuit parameterization, we used voltage responses to rectangular galvanostatic current pulses with durations from 9 to 144 s and amplitudes of approxi…Read more

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

Reproducibility archive for validation-first machine learning interatomic potentials for local lithium energetics in graphene-based battery motifs.

Yuhan Sun, Xiao Huang

Chemistry-stratified reliability audit of selective abstention for universal machine-learning interatomic potentials

Zeyu Fu

Version 0.3.0 provides the sanitized, hermetic code and frozen result records supporting the chemistry-stratified selective-abstention analysis. It includes the 32-UIP roster analysis, prototype-blocked confidence intervals, mechanism and sensitivity controls, manuscript sources,…Read more

Research data supporting "Design Space of Self--Consistent Electrostatic Machine Learning Interatomic Potentials".

William J. Baldwin, Gábor Cśanyi, Ilyes Batatia, Martin Vondrák, Johannes T. Margraf