Development of a prostate cancer biochemical recurrence risk signature using machine learning and motor protein-related genes

This study developed a novel MPRG-derived risk signature that effectively predicts BCR in prostate cancer, offering valuable insights for clinical management and personalized treatment strategies.

Authors

Institutions

Publication Details

Journal
PLoS ONE
Published
2025-09-16
DOI
https://doi.org/10.1371/journal.pone.0332747
Citations
1
Primary Topic
Prostate Cancer Treatment and Research
Type
article
Field-Weighted Citation Impact
1.52
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Development of a prostate cancer biochemical recurrence risk signature using machine learning and motor protein-related genes

Guohai Xie, Ziyi Wu, Wendy Kehan Wang
1 citations
PLoS ONE
Prostate Cancer Treatment and Research
1.52
article

Development of a prostate cancer biochemical recurrence risk signature using machine learning and motor protein-related genes

Guohai Xie, Ziyi Wu, Wendy Kehan Wang
article en
1 citations

Abstract

This study developed a novel MPRG-derived risk signature that effectively predicts BCR in prostate cancer, offering valuable insights for clinical management and personalized treatment strategies.

PLoS ONEVol. 20(9)
Ningbo University (CN), Ningbo No. 2 Hospital (CN), Ningbo No.6 Hospital (CN)
Openalex Percentile: Top 14%
Prostate Cancer Treatment and Research
1.52
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.