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International Mouse Phenotyping Consortium: Investigating gene function and providing insights into human disease.

Wilson, R.; Bülbül Atac, T.; Cheng, T. K.; Frost, A.; Günes, O.; Kan, M.; Keskivali-Bond, P.; Lopez Gomez, F.; McLaughlin, J.; Mucha, J.; Munava, T.; Oliveira, C.; Pava, D.; Pena Estrada, J. F.; Selkirk, E.; Vardal, B.; Wells, S.; Cacheiro, P.; Smedley, D.; Parkinson, H.

2025-10-09 genetics
10.1101/2025.10.09.681205 bioRxiv
Show abstract

The International Mouse Phenotyping Consortium (IMPC; https://www.mousephenotype.org/) web portal contains phenotype data for mouse protein-coding genes derived from analysis of data obtained in a systematic and high-throughput fashion from knock-out lines produced by IMPC. The project has produced >1,400 mouse models of human disease that recapitulate phenotypes observed in patients. Over 8000 papers rely on data or reagents generated by IMPC, demonstrating the impact of the project on the research and clinical communities, and IMPC data is incorporated into other resources, such as MGI, Open Targets and UniProt. Data release (DR23.0, 2025) contains > 100 million data points from 9,277 genes and identified 113,803 significant phenotypes. To manage efficient access to this quantity of high dimensional data the IMPC web portal has been rebuilt using a cloud native architecture. The modern user interface retains the look and feel of the original portal with improvements identified through a usability study. New data visualisation and training materials for large scale data access through the API have also been developed to make the resource easier to use. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/681205v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@199243borg.highwire.dtl.DTLVardef@119c12forg.highwire.dtl.DTLVardef@1da1a27org.highwire.dtl.DTLVardef@1eb141d_HPS_FORMAT_FIGEXP M_FIG C_FIG

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The top 1 journal accounts for 50% of the predicted probability mass.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.