PubMed
open access
CC BY 4.0
LGTM: Gaussian process modulated neural topic modeling for longitudinal microbiome.
Longitudinal microbiome data are key to understanding the dynamics of microbial communities and their relationships with the host and environment. However, analysis of such data is challenging due to high dimensionality, compositionality, irregular sampling and temporal dependencies on external cova
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Drivers of distance decay of host-associated microbiomes vary by host sociality and microbial association strength#964957
- A spatiotemporal U-Net++ deep learning framework for dengue risk mapping in Colombia#651855
- A spatiotemporal U-Net++ deep learning framework for dengue risk mapping in Colombia.#431486
- Phylogenetic coherence in microbiome composition across environmental gradients#356348
- Performance of IBD machine learning classifiers varies across microbiome training data independent of geographic diversity#296494
Record · ID 1221482
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive