3d infomax encoding for drugs (InfoMax Inc)
Structured Review
![Ablation study: performance measures ± standard error of the mean. ‘ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\checkmark$\end{document} ’ indicates included, ‘×’ indicates excluded model components; the first two rows are the complete CANDELA model with the two different pre-training stragies. Models marked ‘ † ’ are significantly worse than CANDELA using only one pre-training task. Models marked ‘*’ are significantly better (pairwise t -test, α = 0.05, corrected for multiple testing using Holm–Šídák). Bold values indicate the best configuration](https://pub-med-central-html-table-images-cdn.bioz.com/pub_med_central_ids_ending_with_5499/pmc11055499/pmc11055499__tbl2__3d_ascii32_infomax__infomax_ascii32_inc.jpg)
3d Infomax Encoding For Drugs, supplied by InfoMax Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/3d+infomax+encoding+for+drugs/pmc11055499-181-20-21?v=InfoMax+Inc
Average 90 stars, based on 1 article reviews
Images
1) Product Images from "Cancer drug sensitivity estimation using modular deep Graph Neural Networks"
Article Title: Cancer drug sensitivity estimation using modular deep Graph Neural Networks
Journal: NAR Genomics and Bioinformatics
doi: 10.1093/nargab/lqae043
Figure Legend Snippet: Ablation study: performance measures ± standard error of the mean. ‘ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\checkmark$\end{document} ’ indicates included, ‘×’ indicates excluded model components; the first two rows are the complete CANDELA model with the two different pre-training stragies. Models marked ‘ † ’ are significantly worse than CANDELA using only one pre-training task. Models marked ‘*’ are significantly better (pairwise t -test, α = 0.05, corrected for multiple testing using Holm–Šídák). Bold values indicate the best configuration
Techniques Used: