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. 2023 Oct 16;21:728. doi: 10.1186/s12967-023-04576-8

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 1 — Schematic overview of the targeted panel development strategy. This figure presents our novel workflow for candidate gene prioritization (C), within a broader omics data-driven strategy for developing targeted “transcriptome fingerprinting assays” (TFAs). The first component involves data-driven construction of a collection of co-expressed blood transcriptional modules (A). This “fixed transcriptional repertoire” provides a stable framework over time for data analysis and interpretation. The BloodGen3 repertoire consists of 382 modules in 38 aggregates representing 14,168 transcripts, constructed and characterized as described in the Methods and a prior publication [17]. Using BloodGen3 in multiple studies provided insight into the potential biological and clinical relevance of its modular signatures (B). One signature, corresponding to the module aggregate A37, was associated with circulating erythroid cells, vaccine responses, and respiratory viral infection severity [15, 16], leading to its prioritization for inclusion in a generic Immune Profiling TFA panel (ImmP-TFA). Modules within this aggregate were selected to pilot the novel workflow for the prioritization of candidate gene pools (C). In doing so, we investigated the versatility of large LLMs for a range of tasks, from scoring candidates to the selection of top candidates for more comprehensive characterization in a separate workflow (D) [30, 31]