Evaluating deconvolution methods using real bulk RNA-expression data for robust prognostic insights across cancer types

This study broadens deconvolution benchmarking, offering actionable tools for precision oncology and guiding method selection for translational research.

Authors

Institutions

Publication Details

Journal
Genome biology
Published
2026-01-21
DOI
https://doi.org/10.1186/s13059-026-03942-1
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Evaluating deconvolution methods using real bulk RNA-expression data for robust prognostic insights across cancer types

Minghan Li, Yizhou Tang, Yuehfan Lee, Weidong Tian et al.
Genome biology
Single-cell and spatial transcriptomics
article

Evaluating deconvolution methods using real bulk RNA-expression data for robust prognostic insights across cancer types

Minghan Li, Yizhou Tang, Yuehfan Lee, Weidong Tian, Yuqing Su
article en

Abstract

This study broadens deconvolution benchmarking, offering actionable tools for precision oncology and guiding method selection for translational research.

Genome biologyVol. 27(1)
Fudan University (CN), Children's Hospital of Fudan University (CN), State Key Laboratory of Genetic Engineering
Openalex Percentile: Top 82%
Single-cell and spatial transcriptomics
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.