Iterative Bleaching Extends Multiplexity (IBEX) Knowledge-Base
Bibliographic record
Abstract
The Iterative Bleaching Extends Multiplexity (IBEX) imaging method is an iterative immunolabeling and chemical bleaching method that enables highly multiplexed imaging of diverse tissues. Development of the IBEX method and related software was led by Dr. Andrea Radtke and Dr. Ziv Yaniv. IBEX and related methods, Ce3D, Ce3D-IBEX, Opal-plex, were originally developed in the laboratory of Dr. Ronald N. Germain, US National Institutes of Health. The IBEX Imaging Community is an international group of scientists committed to sharing knowledge related to multiplexed imaging in a transparent and collaborative manner. This open, global repository is a central resource for reagents, protocols, panels, publications, software, and datasets. In addition to IBEX, we support standard, single cycle multiplexed imaging (Multiplexed 2D imaging), volume imaging of cleared tissues with clearing enhanced 3D (Ce3D), highly multiplexed 3D imaging (Ce3D-IBEX), and extension of the IBEX dye inactivation protocol to the Leica Cell DIVE (Cell DIVE-IBEX). This dataset contains the current state of knowledge with respect to the IBEX microscopy imaging protocol. How to use the Knowledge-Base: Save a copy to your computer. To find a reagent: Open the reagent_resources.csv file found in the data directory. Use a spreadsheet application to filter the columns based on target name, target species, vendor, etc. To view a complete list of fluorescent probes tested by the IBEX imaging community: Open the fluorescent_probes.csv file. This file reports the spectral properties and inactivation conditions of each fluorescent probe. To import publications cited in the Knowledge-Base, import the publications.bib file found in the data directory to your reference manager. To view a local copy of the website: Open the index.md file found in the docs directory using a markdown editor such as the free Visual Studio Code. To view supporting information for a reagent (images, publications, notes): Open a specific target-conjugate-orcid combination under the docs-supporting_material directory structure using a markdown editor. This can also be visualized from the Reagent Resources page and filtered using a catalog number or other unique identifier in your web browser. Join the online IBEX Imaging community and contribute your knowledge. For more details on how to contribute, see these instructions. This research was supported by: The Intramural Research Program of the NIH, National Institute of Allergy and Infectious Diseases and National Cancer Institute, under grants 1ZIAAI001290-02, 1ZIAAI000545-33, 1ZIAAI000758-24, 1ZIAAI000974-16, 1ZIAAI001034-14. The Wellcome Trust, under grant 224586/Z/21/Z. The National Institute of Allergy and Infectious Diseases, NIH, under grant 1ZIAAI001343-01.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.010 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.073 | 0.084 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".