Supplementary Table 1. Clinical metadata Supplementary Table 2. 3D stacked serial sections overview Supplementary Table 3. 2D gene discovery sections overview Supplementary Table 4.CODEX panel information Supplementary Table 5. Xenium panel information Supplementary Table 6. HT704B1 distant region path metadata Supplementary Table 7: Section retention for 3D cohort Supplementary Table 8. Model architecture, hyperparameters, and training details for H&E protein prediction Supplementary Table 9. 3D serial section ROI summary metadata Supplementary Table 10. 3D serial section ROI path metadata Supplementary Table 11. 2D marker gene discovery: section-level feature metrics Supplementary Table 12. 2D marker gene discovery: visium barcode annotations Supplementary Table 13. 3D ROI manual marker gene discovery: feature expression for annotated regions Supplementary Table 14. 3D ROI manual marker gene discovery: feature fold change for annotated regions Supplementary Table 15: Data associated with breast functional assays Supplementary Table 16: Data associated with prostate functional assays.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
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This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
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This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.
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This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.
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It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or sequence constraints.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
