Identification of plasma protein biomarkers for endometriosis and the development of statistical models for disease diagnosis

Authors

Institutions

Publication Details

Journal
Human Reproduction
Published
2024-12-24
DOI
https://doi.org/10.1093/humrep/deae278
Citations
12
Primary Topic
Endometriosis Research and Treatment
Type
article
Field-Weighted Citation Impact
6.44

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Identification of plasma protein biomarkers for endometriosis and the development of statistical models for disease diagnosis

Scott Bringans, Connor Laming, Martin Healey, Deem ISMAIL et al.
12 citations
Human Reproduction
Endometriosis Research and Treatment
6.44
article

Identification of plasma protein biomarkers for endometriosis and the development of statistical models for disease diagnosis

Scott Bringans, Connor Laming, Martin Healey, Deem ISMAIL, Elizna M. Schoeman, Berin A. Boughton, Peter A. W. Rogers, Sarah J. Holdsworth‐Carson, Richard Lipscombe, Jun Ito, Mufaidha RAJU, Tammy M. Casey, Hyunjung Jade Lim, Puay Hoon Tan, Kirsten E. Peters, Christina E. Andronis, Marisa N. Duong, Jane E. Girling, Liqing Chen, Martin K. Mead
article en
12 citations

Abstract

No abstract available for this paper.
Human ReproductionVol. 40(2)
Royal Women's Hospital (AU), The University of Melbourne (AU), La Trobe University (AU), Epworth Hospital (AU), Abterra Biosciences (United States) (US), Endometriosis UK (GB), University of Otago (NZ)
Medical Research Council, National Health and Medical Research Council
Openalex Percentile: Top 3%
Endometriosis Research and Treatment
6.44
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.