OSCR

MCVAE-based multi-omic anomaly detection in Fragile X Syndrome.

Code ↔ Paper

14 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 14 matches
  1. [1] § Results › Signaling pathways linked to translatomic anomalies ↔ utils/enrichment.Rmd, lines 6–78 · score 0.97 · Receptor Tyrosine Kinases, VPS4B, growth factor mediated, PRKAR2A, Signal Transduction, brain function
  2. [2] § Materials and methods › Data collection and pre-processing ↔ pre-processing/DESeq.Rmd, lines 160–213 · score 0.86 · DESeq2, Rlog transformation, external validation, pre processing, integer, genotype
  3. [3] § Results › Translatomic and transcriptomic anomalies align with regulatory hubs potentially underlying indirect FXS effects ↔ utils/enrichment.Rmd, lines 6–78 · score 0.80 · chromatin binding, RNA binding, transcription regulators, TDRD3, FIZ1, SMG5
  4. [4] § Materials and methods › Benchmarking ↔ main.py, lines 211–287 · score 0.67 · OmiEmbed, DeepIMV, MCVAE training, pipeline, Enrichment, Benchmarking
  5. [5] § Materials and methods › Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO) › Leave-one-out cross-validation (LOOCV) to compare benchmarked methods ↔ utils/DIABLO.py, lines 310–368 · score 0.65 · WeightedPredict.class, F1 score, recall, precision, accuracy, DIABLO
  6. [6] § Materials and methods › Data collection and pre-processing ↔ pre-processing/DESeq.Rmd, lines 160–213 · score 0.64 · DESeq2, Rlog transformation, pre processing, covariances, batch, validation
  7. [7] § Results › Benchmarking ↔ main.py, lines 211–287 · score 0.57 · OmiEmbed, DeepIMV, benchmarked, CCA, PLS, linear
  8. [8] § Results › MCVAE identifies a multi-omic anomaly signature separating WT and Fmr1-KO samples ↔ utils/PCA.py, lines 275–336 · score 0.56 · neural stem cells, adult, hippocampus, cortex, GSE112502, Fmr1
  9. [9] § Materials and methods › Anomaly detection ↔ models/VAE/error_analysis_vae.py, lines 20–93 · score 0.56 · Brunner Munzel, Reconstruction error, model
  10. [10] § Materials and methods › MCVAE model training ↔ models/MCVAE/MCVAE.py, lines 52–129 · score 0.55 · Kullback Leibler, dimensional, latent, layers, MCVAE, model
  11. [11] § Materials and methods › Anomaly detection ↔ models/Linear/CCA.py, lines 216–272 · score 0.55 · Brunner Munzel, statsmodels
  12. [12] § Results › Involvement of translatomic anomalies in brain physiology, FXS, and other neurodevelopmental disorders ↔ utils/PCA.py, lines 275–336 · score 0.52 · neural stem cells, Fmr1 KO, embryonic, cortex, brain
  13. [13] § Materials and methods › Annotation analysis of anomalies ↔ utils/enrichment.Rmd, lines 185–260 · score 0.52 · chromatin binding, Tmem47, DNA, STAG2, RNA, Stox2
  14. [14] § Materials and methods › Enrichment score for consensus FMRP mRNA targets ↔ utils/CLIP_enrichment.py, lines 119–201 · score 0.50 · enrichment score, overlapping, CLIP, genes

Paper

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The authors' code

R Markdown · 265 lines · 8.5 KB · no license · 3 matches

  1. ---
  2. title: "R Notebook"
  3. output: html_notebook
  4. ---
  6. This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code.
  8. Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ctrl+Shift+Enter*.
  12. ```{r}
  13. library(dplyr)
  14. library(tidyr)
  15. library(ggplot2)
  16. library(readr)
  17. library(scales)
  18. library(stringr)
  21. allowed_terms_path <- "../data/clusters_annotations.csv"
  22. bp_data_path <- "../data/annotations.csv"
  25. allowed_terms_df <- read_csv(allowed_terms_path) %>%
  26. mutate(Term = str_squish(Term),
  27. CLUSTER_NAME = str_squish(CLUSTER_NAME))
  29. bp_data <- read_delim(bp_data_path, delim = ",", quote = "\"") %>%
  30. mutate(Term = str_squish(Term),
  31. Genes = str_squish(Genes)) %>%
  32. separate_rows(Genes, sep = ",\\s*") %>%
  33. mutate(Genes = str_remove_all(Genes, '"|\\s+'),
  34. Genes = str_squish(Genes))
  37. cluster_order <- c(
  38. "mRNA biogenesis", "Pscychiatric disorder", "FXS",
  39. "Intelectual disability", "Cognition", "Neurodegeneration",
  40. "Neuropathy/Ataxia", "Epilepsy", "Synapse",
  41. "Neurotransmitter Signaling", "Neurotransmitter signaling by GPCR",
  42. "GPCR & G-Protein Signaling", "Small GTPase / Rho GTPase Signaling",
  43. "Second Messenger & Kinase Pathways", "General & Broad Signal Transduction",
  44. "Receptor Tyrosine Kinase (RTK) Signaling", "Hormone&Nuclear Receptor",
  45. "Neurotrophic and Growth Factor mediated neurodevelopment and brain function",
  46. "Development and function of the CNS", "Neurodeveloppement/Neurogenesis",
  47. "Neurodevelopment, Axon guidance, Axon regeneration", "Cytoskeleton",
  48. "Mechanotransduction & Specialized Cellular Signaling",
  49. "Immune response/Inflammation/Neuroinflammation", "Neuro-inflammation"
  50. )
  52. gene_order <- rev(c(
  53. 'PKP4', 'STOX2', 'NEDD4', 'PUM1', 'STAG2', 'FMR1', 'ZNF207','CLIC4', 'LPGAT1',
  54. 'SMG5', 'ARC', 'FIZ1', 'LONP1', 'TMEM47', 'VPS4B', 'TDRD3','ASCL1','SNX6',
  55. 'NSMCE2','SHB','PRKAR2A','TMOD3','KLC2'
  56. ))
  58. bp_filtered <- bp_data %>%
  59. filter(Term %in% allowed_terms_df$Term) %>%
  60. left_join(allowed_terms_df %>% select(Term, CLUSTER_NAME), by = "Term") %>%
  61. mutate(
  62. Category = case_when(
  63. Category == 1 ~ "RNA binding/translation regulator",
  64. Category == 2 ~ "DNA/chromatin binding",
  65. Category == 3 ~ "Transcription regulator",
  66. Category == 4 ~ "Pathways",
  67. Category == 5 ~ "FXS phenotype-associated",
  68. TRUE ~ as.character(Category)
  69. ),
  70. CLUSTER_NAME = factor(CLUSTER_NAME, levels = cluster_order),
  71. Genes = factor(Genes, levels = gene_order)
  72. ) %>%
  73. # Remove any rows that failed to match a cluster in your list
  74. filter(!is.na(CLUSTER_NAME)) %>%
  75. distinct()
  78. create_dotplot <- function(df, keep_categories, title, row_expand = 1) {
  80. # Filter data for specific categories (Pathways & FXS)
  81. df_plot <- df %>% filter(Category %in% keep_categories)
  83. # CRITICAL: Force Term order to follow Cluster order
  84. term_order_levels <- df_plot %>%
  85. arrange(CLUSTER_NAME, Term) %>%
  86. pull(Term) %>%
  87. unique()
  89. df_plot <- df_plot %>%
  90. mutate(Term = factor(Term, levels = term_order_levels))
  92. # Generate colors for the bottom bars
  93. cluster_colors <- scales::hue_pal()(length(cluster_order))
  94. names(cluster_colors) <- cluster_order
  96. # Calculate positions for the rectangles
  97. gap <- 0.5
  98. cluster_blocks <- df_plot %>%
  99. group_by(CLUSTER_NAME) %>%
  100. summarise(
  101. xmin = min(as.numeric(Term)) - 0.5 + gap/2,
  102. xmax = max(as.numeric(Term)) + 0.5 - gap/2,
  103. ymin = -0.5,
  104. ymax = -0.2,
  105. .groups = "drop"
  106. )
  108. # Build the plot
  109. p <- ggplot(df_plot, aes(x = Term, y = Genes)) +
  110. # Grid lines
  111. geom_hline(yintercept = seq_along(levels(df_plot$Genes)),
  112. color = "lightgrey", size = 0.3) +
  114. # THE DOTS: All grey, black outline, no mapping to Category
  115. geom_point(fill = "grey70", shape = 21, color = "black", size = 3) +
  117. scale_y_discrete(drop = FALSE, expand = expansion(add = c(0.5 * row_expand, 0.5 * row_expand))) +
  118. coord_cartesian(clip = "off") +
  119. theme_minimal(base_size = 10) +
  120. theme(
  121. axis.text.x = element_blank(),
  122. axis.text.y = element_text(size = 7, face = "bold"),
  123. axis.title = element_blank(),
  124. panel.grid.major = element_blank(),
  125. legend.position = "none",
  126. plot.margin = margin(t = 10, r = 10, b = 150, l = 10) # Space for labels
  127. ) +
  128. labs(title = title)
  130. # Add the color bars for clusters
  131. for(cluster in unique(cluster_blocks$CLUSTER_NAME)){
  132. cluster_data <- cluster_blocks %>% filter(CLUSTER_NAME == cluster)
  133. p <- p + geom_rect(
  134. data = cluster_data,
  135. aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
  136. fill = cluster_colors[as.character(cluster)],
  137. inherit.aes = FALSE
  138. )
  139. }
  141. # Add vertical separators between clusters
  142. if(nrow(cluster_blocks) > 1){
  143. for(i in 2:nrow(cluster_blocks)){
  144. line_x <- cluster_blocks$xmin[i] - gap/2
  145. p <- p + geom_segment(
  146. x = line_x, xend = line_x,
  147. y = -0.2,
  148. yend = length(levels(df_plot$Genes)),
  149. color = "lightgrey", size = 0.4, inherit.aes = FALSE
  150. )
  151. }
  152. }
  154. # Add rotated labels
  155. cluster_labels <- cluster_blocks %>%
  156. mutate(x = (xmin + xmax)/2,
  157. y = -1.2, # Vertical offset
  158. label = stringr::str_wrap(CLUSTER_NAME, width = 40))
  160. p <- p + geom_text(data = cluster_labels,
  161. aes(x = x, y = y, label = label),
  162. angle = 90, hjust = 1, vjust = 0.5, size = 3,
  163. fontface = "bold", inherit.aes = FALSE)
  165. return(p)
  166. }
  168. dotplot_final <- create_dotplot(
  169. bp_filtered,
  170. keep_categories = c("Pathways", "FXS phenotype-associated"),
  171. title = "Final Analysis",
  172. row_expand = 50
  173. )
  175. print(dotplot_final)
  177. #ggsave("plots/dotplot_pathways.png", dotplot_pathways, width = 12, height = 10, dpi = 1200)
  178. #ggsave("plots/dotplot_pathways.pdf", dotplot_final, width = 12, height = 8)
  180. ```
  185. ```{r}
  186. library(dplyr)
  187. library(tidyr)
  188. library(ggplot2)
  189. library(readr)
  190. library(stringr)
  192. setwd("..")
  193. getwd()
  196. bp_data_path <- "data/annotations.csv"
  198. # --- Load MF data, split Genes, remove quotes ---
  199. bp_data <- read_delim(bp_data_path, delim = ",", quote = "\"") %>%
  200. mutate(
  201. Term = trimws(Term),
  202. Genes = trimws(Genes)
  203. ) %>%
  204. separate_rows(Genes, sep = ",\\s*") %>%
  205. mutate(Genes = str_remove_all(Genes,'"|\\s+'),
  206. Genes = trimws(Genes)) %>%
  207. filter(GROUP == "MF") # Keep only MF
  209. bp_data <- bp_data %>%
  210. mutate(Term_short = str_replace(Term, "~.*$", "") %>% str_trim())
  212. #write.csv(bp_data , "bp_expanded_MF.csv", row.names = FALSE)
  215. # --- Define group color mapping ---
  216. group_colors <- c(
  217. "1" = "#F0E2B6", # RNA binding/translation regulator
  218. "2" = "#D3EEEA", # DNA/chromatin binding
  219. "3" = "#CFD5E8" # Transcription regulator
  220. )
  222. # --- Order terms by Category, then Term_short ---
  223. term_order <- bp_data %>%
  224. group_by(Category, Term_short) %>%
  225. summarise(n = n(), .groups = "drop") %>%
  226. arrange(Category, Term_short) %>%
  227. pull(Term_short) %>% unique()
  229. bp_data <- bp_data %>%
  230. mutate(Term_short = factor(Term_short, levels = term_order),
  231. Category = factor(Category, levels = c(1,2,3)))
  233. selected_genes <- c('PKP4', 'STOX2', 'NEDD4', 'PUM1', 'STAG2', 'ZNF207',
  234. 'FMR1', 'CLIC4', 'SMG5', 'LPGAT1', 'ARC', 'LONP1',
  235. 'FIZ1', 'TMEM47', 'VPS4B')
  237. all_genes <- unique(bp_data$Genes)
  238. gene_levels <- c(setdiff(all_genes, selected_genes), rev(selected_genes))
  239. bp_data <- bp_data %>%
  240. mutate(Genes = factor(Genes, levels = gene_levels))
  242. p <- ggplot(bp_data, aes(x = Term_short, y = Genes)) +
  243. geom_point(aes(color = Category), size = 3) +
  244. scale_color_manual(values = group_colors, name = "MF Group") +
  246. geom_tile(aes(y = -0.5, fill = Category), height = 0.3) +
  247. scale_fill_manual(values = group_colors, guide = "none") +
  249. theme_minimal(base_size = 10) +
  250. theme(
  251. axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
  252. axis.title = element_blank(),
  253. legend.position = "right",
  254. panel.grid.major = element_blank()
  255. ) +
  256. labs(title = "MF Terms - Colored by Group (1,2,3)")
  258. print(p)
  260. ```
  261. Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Ctrl+Alt+I*.
  263. When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Ctrl+Shift+K* to preview the HTML file).
  265. The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike *Knit*, *Preview* does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.

enrichment.Rmd at commit 31d52c6, no license · at the source

Overview

Authors: Wassila Khatir1,2, Marco Lorenzi1, Irene Balelli1, Carole Gwizdek2
ORCID iDs: Wassila Khatir
  1. INRIA Center at Université Côte d’Azur, Epione Team, 2004 Rte des Lucioles, 06560 Valbonne, France
  2. IPMC, CNRS, Université Côte d’Azur, 660 Rte des Lucioles, 06560 Valbonne, France
Institutions: Centre National de la Recherche Scientifique (France); Institut national de recherche en sciences et technologies du numérique (France); Université Côte d'Azur (France); Centre Inria d'Université Côte d'Azur (France); Institut de Pharmacologie Moléculaire et Cellulaire (France); EPIONE: E-Patient : Images, données & mOdèles pour la médeciNe numériquE (France)
Journal: NAR molecular medicine, volume 3, issue 2, article ugag028
Dates: received 8 January 2026; accepted 13 May 2026; published online 3 June 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1093/narmme/ugag028 · PMID 42266503 · PMCID PMC13245416 · OpenAlex W7163537245
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), other condition (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning
Topic: Genetics and Neurodevelopmental Disorders (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Agence Nationale de la Recherche (ANR-19-P3IA-0002, ANR‐15‐IDEX‐01)
Citations: not cited yet (Europe PMC); 84 references in the paper

Abstract

Fragile X Syndrome (FXS) is a neurodevelopmental disorder caused by mutations in the FMR1 gene, resulting in the loss of FMRP, an RNA-binding protein regulating translation of hundreds of mRNAs. The Fmr1 knock-out mouse models this deficiency and is used to study molecular perturbations in the FXS brain. Omics analysis shows that FMRP loss disrupts coordination between transcriptomic and translatomic layers. But limited sample availability and dataset heterogeneity hinder detection of subtle, coordinated multi-omic dysregulations. To address this, we trained a Multi-Channel Variational Autoencoder (MCVAE) on wild-type samples to learn a shared latent representation of transcriptomic and translatomic modalities via cross-modal reconstruction. Testing MCVAE on Fmr1-knock-out samples revealed deviations from wild-type as anomalies, uncovering known and novel perturbations. Compared to alternative methods, MCVAE shows stronger enrichment for FMRP mRNA targets and improved genotype discriminative power in post-hoc tests. Translatomic anomalies exhibited coordinated relationships with transcriptomic anomalies, as supported by publicly available databases exploration. Moreover, these anomalies mapped to validated FMRP regulators and neurodevelopmental pathways, establishing MCVAE as a framework to uncover coordinated molecular perturbations underlying the FXS pathophysiology and guide biomarker and therapeutic target identification.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 14 matches between paragraphs and lines of code.

gitlab.inria.fr/wkhatir/mcvae_anomaly_detection

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 31d52c607466bac22b69b40810cee3037017ff81, 21 September 2026
Languages: Python (36), R (4), Jupyter (1)
Size: 4,007 files, 41 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, 2 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (26 files), pandas (26 files), Matplotlib (22 files), PyTorch (14 files), scikit-learn (13 files), seaborn (13 files), SciPy (10 files), statsmodels (8 files), rpy2 (5 files), TensorFlow (3 files), tidyverse (3 files), ggplot2 (2 files), clusterProfiler (1 file), DESeq2 (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
42 files
At the source: gitlab.inria.fr/wkhatir/MCVAE_anomaly_detection

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 41 scripts, each with its path and the digest of its content;
  • 14 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability

All transcriptomic () and translatomic () datasets analyzed in this study are publicly available through the Gene Expression Omnibus (GEO) repository. The study utilized a total of 35 unique GEO accession codes, which include: GSE140565 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE140565), GSE127845 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE127845), GSE143330 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE143330), GSE174303 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE174303), GSE112502 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE112502), GSE165872 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE165872), GSE119681 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE119681), GSE145102 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE145102), GSE121162 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE121162), GSE227891 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE227891), GSE158881 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE158881), GSE144539 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE144539), GSE82068 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE82068), GSE254224 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE254224), GSE156414 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE156414), GSE147830 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE147830), GSE137283 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137283), GSE180766 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE180766), GSE51424 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE51424), GSE141204 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE141204), GSE141624 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE141624), GSE141899 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE141899), GSE141979 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE141979), GSE209902 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE209902), GSE242245 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE242245), GSE153872 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE153872), GSE108371 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE108371), GSE108372 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE108372), GSE248013 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE248013), GSE51619 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE51619), GSE131536 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE131536), GSE143659 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE143659), GSE114064 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE114064), GSE201239 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE201239), and GSE101823 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE101823). A complete categorization of these accession numbers is detailed in Table 1 of the manuscript.

The source code used for this analysis is publicly available at https://gitlab.inria.fr/wkhatir/MCVAE_anomaly_detection.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

  • Funding: added Agence Nationale de la Recherche: ANR-19-P3IA-0002, ANR‐15‐IDEX‐01

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 79 references.

Cite

This paper

Khatir, W., Lorenzi, M., Balelli, I., & Gwizdek, C. (2026). MCVAE-based multi-omic anomaly detection in Fragile X Syndrome. NAR molecular medicine, 3(2), ugag028. https://doi.org/10.1093/narmme/ugag028

BibTeX

@article{khatir2026mcvae,
author = {Khatir, Wassila and Lorenzi, Marco and Balelli, Irene and Gwizdek, Carole},
title = {{MCVAE-based multi-omic anomaly detection in Fragile X Syndrome}},
journal = {NAR molecular medicine},
year = {2026},
month = apr,
volume = {3},
number = {2},
pages = {ugag028},
publisher = {Oxford University Press},
issn = {2976-856X},
doi = {10.1093/narmme/ugag028},
url = {https://doi.org/10.1093/narmme/ugag028},
pmid = {42266503},
pmcid = {PMC13245416}
}

RIS

TY - JOUR
AU - Khatir, Wassila
AU - Lorenzi, Marco
AU - Balelli, Irene
AU - Gwizdek, Carole
TI - MCVAE-based multi-omic anomaly detection in Fragile X Syndrome
T2 - NAR molecular medicine
J2 - NAR Mol Med
PY - 2026
DA - 2026/04/01
VL - 3
IS - 2
SP - ugag028
SN - 2976-856X
PB - Oxford University Press
DO - 10.1093/narmme/ugag028
UR - https://doi.org/10.1093/narmme/ugag028
LA - en
ER -

CSL-JSON

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"id": "10.1093/narmme/ugag028",
"type": "article-journal",
"title": "MCVAE-based multi-omic anomaly detection in Fragile X Syndrome",
"container-title": "NAR molecular medicine",
"author": [
{
"family": "Khatir",
"given": "Wassila"
},
{
"family": "Lorenzi",
"given": "Marco"
},
{
"family": "Balelli",
"given": "Irene"
},
{
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"given": "Carole"
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],
"container-title-short": "NAR Mol Med",
"volume": "3",
"issue": "2",
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"DOI": "10.1093/narmme/ugag028",
"PMID": "42266503",
"PMCID": "PMC13245416",
"ISSN": "2976-856X",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/narmme/ugag028",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
1
]
]
}
}

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