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Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
Published on: March 28, 2025
Graph-based signal integration for high-throughput phenotyping
Jorge R Herskovic1, Devika Subramanian, Trevor Cohen
1School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA. jorge.r.herskovic@uth.tmc.edu
Background:
Electronic Health Records aggregated in Clinical Data Warehouses (CDWs) promise to revolutionize Comparative Effectiveness Research and suggest new avenues of research. However, the effectiveness of CDWs is diminished by the lack of properly labeled data. We present a novel approach that integrates knowledge from the CDW, the biomedical literature, and the Unified Medical Language System (UMLS) to perform high-throughput phenotyping. In this paper, we automatically construct a graphical knowledge model and then use it to phenotype breast cancer patients. We compare the performance of this approach to using MetaMap when labeling records.
Results:
MetaMap's overall accuracy at identifying breast cancer patients was 51.1% (n=428); recall=85.4%, precision=26.2%, and F1=40.1%. Our unsupervised graph-based high-throughput phenotyping had accuracy of 84.1%; recall=46.3%, precision=61.2%, and F1=52.8%.
Conclusions:
We conclude that our approach is a promising alternative for unsupervised high-throughput phenotyping.
