Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection

🏆 Top 20📊 OpenAlex

Most Cited Paper

in Cancer Genomics and Diagnostics

18 Months Post-Publication

Class of 2026

“Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection”

AuthorsHongwei Liu et al.

Siyi Wanggou, Xuejun Li

JournalComputational and Structural Biotechnology Journal

Central South University

Citations83

DOI10.1016/j.csbj.2024.06.035

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Topics

Mime · machine-learning framework · transcriptional data · clinical characteristics prediction · feature selection · PIEZO1-associated signatures · prognosis prediction · immunotherapy response prediction · SDC1 · glioma · Next-generation sequencing · Tumor heterogeneity · R package · integration model · Gene signature · patient outcome prediction

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Pepkio Research Index (PRI). Topics and Trends in Most Cited Cancer Genomics and Diagnostics Papers, Class of 2026. https://pri.pepkio.com/top-papers/cancer-genomics-and-diagnostics/2026. Accessed 2026-08-20.

Methodology
Zheng Su, Tinsley Li, Thematic Shifts in Early-High-Impact Cancer Genomics and Diagnostics Research: A Bibliometric and Semantic Analysis. bioRxiv 2026.07.04.736459; doi: https://doi.org/10.64898/2026.07.04.736459