Robust graph structure learning to improve multi-omics cancer subtype classification

Experimental results show that our model achieves competitive performance across eight cancer datasets spanning four omics modalities, with generally improved classification performance and exploratory survival prediction results.

Authors

Institutions

Publication Details

Journal
BMC Bioinformatics
Published
2026-02-25
DOI
https://doi.org/10.1186/s12859-026-06404-4
Primary Topic
Bioinformatics and Genomic Networks
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Robust graph structure learning to improve multi-omics cancer subtype classification

Mengke Guo, Tetsuya Sakurai, Xiucai Ye
BMC Bioinformatics
Bioinformatics and Genomic Networks
article

Robust graph structure learning to improve multi-omics cancer subtype classification

Mengke Guo, Tetsuya Sakurai, Xiucai Ye
article en

Abstract

Experimental results show that our model achieves competitive performance across eight cancer datasets spanning four omics modalities, with generally improved classification performance and exploratory survival prediction results.

BMC Bioinformatics
University of Tsukuba (JP)
Openalex Percentile: Top 42%
Bioinformatics and Genomic Networks
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.