Geometric deep learning enables high-fidelity network imputation for HIV transmission modeling.
Saved in:
| Title: | Geometric deep learning enables high-fidelity network imputation for HIV transmission modeling. |
|---|---|
| Authors: | Clipman SJ; Department of Medicine, Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD, USA. sclipman@jhmi.edu., Wang J; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA., Mehta SH; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA., Mohapatra S; YR Gaitonde Centre for AIDS Research and Education (YRGCARE), Chennai, India., Srikrishnan AK; YR Gaitonde Centre for AIDS Research and Education (YRGCARE), Chennai, India., Kumar MS; YR Gaitonde Centre for AIDS Research and Education (YRGCARE), Chennai, India., Lucas GM; Department of Medicine, Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD, USA., Latkin CA; Department of Health, Behavior, and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA., Solomon SS; Department of Medicine, Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD, USA.; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. |
| Source: | Communications medicine [Commun Med (Lond)] 2026 May 04; Vol. 6 (1). Date of Electronic Publication: 2026 May 04. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Nature Portfolio Country of Publication: England NLM ID: 9918250414506676 Publication Model: Electronic Cited Medium: Internet ISSN: 2730-664X (Electronic) Linking ISSN: 2730664X NLM ISO Abbreviation: Commun Med (Lond) Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
|
Full text is not displayed to guests.
Login for full access.
|
|
Be the first to leave a comment!
