Ajout lecture train

suivi/2025-27/2025-27.qmd

@@ -1,7 +1,7 @@
 ---
 title: "Bilan semaine 27 2025 : 30 juin - 4 juillet"
 categories: [colBiSBM, inférence, GNN]
-date: 2025 07 04
+date: 2025 06 30
 bibliography: references.bib
 ---
 
@@ -54,6 +54,9 @@ Z^0_i \overset{?}{=} & Z^1_i \overset{?}{=}  Z^2_i \overset{?}{=} Z^3_i & \text{
 
 ### Inférence de graphes
 
+- ✅ @Morton2021.11.09.467939 VAE with Multinomial Logistic Normal distribution using Isometric Log Ratio tranform
+Plus rapide que les autres méthodes et performances équivalentes
+
 ## A discuter
 
 ### Inférence

suivi/2025-27/references.bib

@@ -29,21 +29,19 @@
   file       = {/home/louis/snap/zotero-snap/common/Zotero/storage/7LVQPD6D/Nenna - Lecture 1 Monge and Kantorovich problems from primal to dual.pdf}
 }
 
-@article{matchadoNetworkAnalysisMethods2021b,
+@article{Morton2021.11.09.467939,
-  title      = {Network Analysis Methods for Studying Microbial Communities: {{A}} Mini Review},
+  title        = {Scalable Estimation of Microbial Co-Occurrence Networks with {{Variational Autoencoders}}},
-  shorttitle = {Network Analysis Methods for Studying Microbial Communities},
+  author       = {Morton, James T. and Silverman, Justin and Tikhonov, Gleb and Lähdesmäki, Harri and Bonneau, Rich},
-  author     = {Matchado, Monica Steffi and Lauber, Michael and Reitmeier, Sandra and Kacprowski, Tim and Baumbach, Jan and Haller, Dirk and List, Markus},
+  date         = {2021},
-  year       = {2021},
+  journaltitle = {bioRxiv : the preprint server for biology},
-  month      = jan,
+  shortjournal = {bioRxiv},
-  journal    = {Computational and Structural Biotechnology Journal},
+  eprint       = {https://www.biorxiv.org/content/early/2021/11/11/2021.11.09.467939.full.pdf},
-  volume     = {19},
+  publisher    = {Cold Spring Harbor Laboratory},
-  pages      = {2687--2698},
+  doi          = {10.1101/2021.11.09.467939},
-  issn       = {2001-0370},
+  url          = {https://www.biorxiv.org/content/early/2021/11/11/2021.11.09.467939},
-  doi        = {10.1016/j.csbj.2021.05.001},
+  abstract     = {Estimating microbe-microbe interactions is critical for understanding the ecological laws governing microbial communities. Rapidly decreasing sequencing costs have promised new opportunities to estimate microbe-microbe interactions across thousands of uncultured, unknown microbes. However, typical microbiome datasets are very high dimensional and accurate estimation of microbial correlations requires tens of thousands of samples, exceeding the computational capabilities of existing methodologies. Furthermore, the vast majority of microbiome studies collect compositional metagenomics data which enforces a negative bias when computing microbe-microbe correlations. The Multinomial Logistic Normal (MLN) distribution has been shown to be effective at inferring microbe-microbe correlations, however scalable Bayesian inference of these distributions has remained elusive. Here, we show that carefully constructed Variational Autoencoders (VAEs) augmented with the Isometric Log-ratio (ILR) transform can estimate low-rank MLN distributions thousands of times faster than existing methods. These VAEs can be trained on tens of thousands of samples, enabling co-occurrence inference across tens of thousands of microbes without regularization. The latent embedding distances computed from these VAEs are competitive with existing beta-diversity methods across a variety of mouse and human microbiome classification and regression tasks, with notable improvements on longitudinal studies.Competing Interest StatementThe authors have declared no competing interest.},
-  urldate    = {2025-06-16},
+  elocation-id = {2021.11.09.467939},
-  abstract   = {Microorganisms including bacteria, fungi, viruses, protists and archaea live as communities in complex and contiguous environments. They engage in numerous inter- and intra- kingdom interactions which can be inferred from microbiome profiling data. In particular, network-based approaches have proven helpful in deciphering complex microbial interaction patterns. Here we give an overview of state-of-the-art methods to infer intra-kingdom interactions ranging from simple correlation- to complex conditional dependence-based methods. We highlight common biases encountered in microbial profiles and discuss mitigation strategies employed by different tools and their trade-off with increased computational complexity. Finally, we discuss current limitations that motivate further method development to infer inter-kingdom interactions and to robustly and comprehensively characterize microbial environments in the future.},
+  keywords     = {/unread},
-  keywords   = {/unread,Microbial co-occurrence networks,Microbial interactions,Network analysis,Trans-kingdom interactions},
   annotation   = {Read\_Status: New\\
-                Read\_Status\_Date: 2025-06-16T16:18:09.496Z},
+                  Read\_Status\_Date: 2025-06-30T14:17:29.518Z}
-  file       = {/home/louis/snap/zotero-snap/common/Zotero/storage/ZCY74M2I/Matchado et al. - 2021 - Network analysis methods for studying microbial communities A mini review.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/BKZN3MI5/S2001037021001823.html}
 }