A network-centric approach reveals novel pathways impacted by Prader-Willi Syndrome.

Code ↔ Paper

2 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 2 matches

[1] § Results › Links between candidate genes and known PWS-associated genes ↔ find_pws_overlap.py, lines 3–5 · score 0.64 · ATP10A, PWS region, OCA2, NPAP1, SNURF, MKRN3
[2] § Results › Links between candidate genes and known PWS-associated genes ↔ gene_analysis.py, lines 1–15 · score 0.63 · ATP10A, PWS region, OCA2, NPAP1, SNURF, MKRN3

Paper

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The authors' code

Python · 19 lines · 697 B · no license · 1 match

import pandas as pd
# PWS region genes
pws_region_genes = ["MKRN3", "MAGEL2", "NDN", "NPAP1", "SNURF", "SNRPN", "UBE3A", "GABRB3",
"GABRA5", "GABRG3", "ATP10A", "OCA2", "HERC2", "NIPA1", "NIPA2", "CYFIP1", "TUBGCP5"]
# Read candidate genes
candidates_df = pd.read_csv('CT2_log/euclidean/final_candidate/candidate_genes.csv')
# Find overlap
overlap_genes = candidates_df[candidates_df['Gene Name'].isin(pws_region_genes)]
# Save overlap to file
overlap_genes.to_csv('CT2_log/euclidean/final_candidate/overlap.csv', index=False)
# Print results
print(f"Found {len(overlap_genes)} overlapping genes:")
for _, row in overlap_genes.iterrows():
print(f"{row['Gene Name']}")

find_pws_overlap.py at commit 0cae182, no license · at the source

Overview

Authors: Kunal Bham¹, Manju Anandakrishnan², Cathy H Wu^1,2, Karen E Ross¹

ORCID iDs: Manju Anandakrishnan, Cathy H Wu, Karen E Ross

Institutions: Georgetown University (United States); Georgetown University Medical Center (United States); University of Delaware (United States)

Journal: PloS one, volume 21, issue 4, article e0347773

Dates: received 22 January 2026; accepted 7 April 2026; published online 28 April 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1371/journal.pone.0347773 · PMID 42048351 · PMCID PMC13123929 · OpenAlex W7158227000

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: human (organism), cellular / molecular (subfield)

Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing

MeSH: Gene Regulatory Networks*, Prader-Willi Syndrome*, Protein Interaction Maps*, Humans, Protein Interaction Mapping, RNA, Small Nucleolar (* major topic)

Topic: Genetic Syndromes and Imprinting (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex

Funding: National Institute of General Medical Sciences (R35GM141873)

Citations: not cited yet (Europe PMC); 54 references in the paper

Abstract

Prader-Willi Syndrome (PWS), a rare multi-system disorder characterized by insatiable appetite, growth abnormalities, and cognitive delay, results from genetic defects in a paternally expressed region of chromosome 15, q11.2-q13. This region contains several protein-coding genes and several genes encoding small nucleolar RNA (snoRNAs), including the SNORD116 gene cluster, but their exact role in PWS remains unclear. Since snoRNAs have wide-ranging effects on protein expression and proteins interact in a complex network, the genetic aberrations causing PWS are likely to cause far-reaching indirect effects on protein expression and activity. Here, we mapped PWS gene expression data onto a human protein-protein interaction (PPI) network and used graph learning techniques to 1) identify the most impacted proteins and 2) suggest novel disease mechanisms. We adapted GeneEMBED, a network-based method originally developed to model genetic variants associated with Alzheimer’s Disease. Specifically, we integrated PWS or control expression data with the PPI network, calculated node embeddings, and identified proteins with large differences between PWS and control embeddings. These candidate proteins were subjected to functional enrichment analysis to discover altered biological processes in PWS. Candidate proteins were highly enriched for glycosylated proteins. Analysis of candidate glycosylation enzymes suggested abnormalities in mucin-type O-glycosylation, fucosylation, and glycosaminoglycan synthesis. Defects in these glycosylation pathways have been linked to several PWS phenotypes, including obesity, cognitive delay, and production of secondary sex hormones. Homeobox proteins, master regulators of transcription during development, were also overrepresented among the candidate proteins. In particular, we identified homeobox proteins that drive development of GABAergic and dopaminergic neurons. These neuronal pathways regulate appetite and other behaviors that are abnormal in individuals with PWS. Our results were highly reproducible across PWS model systems. This work offers new avenues for further research in PWS and provides a promising approach that can be applied to other complex diseases.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

kunal-bham/PWS-Proj

License: none: the authors keep all their rights

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: 0cae1828530c23ef0d5a2ed2d1d46d7223d69e18, 24 June 2025

Languages: Python (37)

Size: 41 files, 37 scripts

Software Heritage: not archived

Found in: “Data Availability”

Holds: README

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: pandas (35 files), NumPy (24 files), Matplotlib (20 files), SciPy (14 files), NetworkX (5 files), scikit-learn (3 files), igraph (1 file), Plotly (1 file), seaborn (1 file)

Availability: 1 check, the latest on 30 September 2026: the link answers

30 September 2026: the link answers

38 files

add_distances.py, Python, 16 lines
calculate_expression_averages.py, Python, 43 lines
centrality.py, Python, 151 lines
correlation_analysis.py, Python, 158 lines
create_embeddings.py, Python, 223 lines
distance_analysis.py, Python, 291 lines
distance_calculations.py, Python, 364 lines
find_dataset_overlap.py, Python, 16 lines
find_h9_overlap.py, Python, 20 lines
find_original_dataset_overlap.py, Python, 20 lines
find_pws_overlap.py, Python, 19 lines, 1 match
find_small_deletion_overlap.py, Python, 20 lines
finding_outliers.py, Python, 428 lines
format_cluster.py, Python, 14 lines
gene_analysis.py, Python, 158 lines, 1 match
get_distances.py, Python, 62 lines
get_top_200_genes.py, Python, 15 lines
get_top_candidates.py, Python, 18 lines
graphwave.py, Python, 142 lines
pipeline.py, Python, 52 lines
plot_all_statistical_overlaps.py, Python, 122 lines
plot_centrality_distances.py, Python, 89 lines
plot_consensus_overlap.py, Python, 78 lines
plot_control_deletion_distance.py, Python, 43 lines
plot_control_distances.py, Python, 40 lines
plot_distance_expression.py, Python, 62 lines
plot_distances.py, Python, 40 lines
plot_edge_weights.py, Python, 59 lines
plot_edges_distance.py, Python, 54 lines
plot_euclidean_distances.py, Python, 147 lines
plot_statistical_overlap.py, Python, 55 lines
preprocessing.py, Python, 396 lines
sankey.py, Python, 122 lines
statistical_test_overlap.py, Python, 39 lines
stats.py, Python, 283 lines
test.py, Python, 47 lines
visualize.py, Python, 59 lines
README.md, Text, 196 lines

At the source: github.com/kunal-bham/PWS-Proj

Zenodo 17144459

License: CC-BY-4.0

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Size: 1 file

Software Heritage: not checked

Found in: “Data Availability”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: pandas (35 files), NumPy (24 files), Matplotlib (20 files), SciPy (14 files), NetworkX (5 files), scikit-learn (3 files), igraph (1 file), Plotly (1 file), seaborn (1 file)

Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)

30 September 2026: the link answers (HTTP 200)

38 files

add_distances.py, Python, 16 lines
calculate_expression_averages.py, Python, 43 lines
centrality.py, Python, 151 lines
correlation_analysis.py, Python, 158 lines
create_embeddings.py, Python, 223 lines
distance_analysis.py, Python, 291 lines
distance_calculations.py, Python, 364 lines
find_dataset_overlap.py, Python, 16 lines
find_h9_overlap.py, Python, 20 lines
find_original_dataset_overlap.py, Python, 20 lines
find_pws_overlap.py, Python, 19 lines
find_small_deletion_overlap.py, Python, 20 lines
finding_outliers.py, Python, 428 lines
format_cluster.py, Python, 14 lines
gene_analysis.py, Python, 158 lines
get_distances.py, Python, 62 lines
get_top_200_genes.py, Python, 15 lines
get_top_candidates.py, Python, 18 lines
graphwave.py, Python, 142 lines
pipeline.py, Python, 52 lines
plot_all_statistical_overlaps.py, Python, 122 lines
plot_centrality_distances.py, Python, 89 lines
plot_consensus_overlap.py, Python, 78 lines
plot_control_deletion_distance.py, Python, 43 lines
plot_control_distances.py, Python, 40 lines
plot_distance_expression.py, Python, 62 lines
plot_distances.py, Python, 40 lines
plot_edge_weights.py, Python, 59 lines
plot_edges_distance.py, Python, 54 lines
plot_euclidean_distances.py, Python, 147 lines
plot_statistical_overlap.py, Python, 55 lines
preprocessing.py, Python, 396 lines
sankey.py, Python, 122 lines
statistical_test_overlap.py, Python, 39 lines
stats.py, Python, 283 lines
test.py, Python, 47 lines
visualize.py, Python, 59 lines
README.md, Text, 196 lines

At the source: zenodo.org/records/17144459

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:

2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
74 scripts, each with its path and the digest of its content;
2 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

geo:GSE178687, at NCBI GEO; found in the text, “Generalizability of results to other PWS…”

Data Availability

All relevant data are within the paper, its Supporting information files, or were taken from published papers or public data repositories. Code is available at https://github.com/kunal-bham/PWS-Proj (Zenodo doi: https://zenodo.org/records/17144459).

Reproduced under the paper's license (CC BY), 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 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 6 MeSH terms, 1 funder, 52 references.

Cite

This paper

Bham, K., Anandakrishnan, M., Wu, C. H., & Ross, K. E. (2026). A network-centric approach reveals novel pathways impacted by Prader-Willi Syndrome. PloS one, 21(4), e0347773. https://doi.org/10.1371/journal.pone.0347773

BibTeX

@article{bham2026network, author = {Bham, Kunal and Anandakrishnan, Manju and Wu, Cathy H and Ross, Karen E}, title = {{A network-centric approach reveals novel pathways impacted by Prader-Willi Syndrome}}, journal = {PloS one}, year = {2026}, month = apr, volume = {21}, number = {4}, pages = {e0347773}, publisher = {PLOS}, issn = {1932-6203}, doi = {10.1371/journal.pone.0347773}, url = {https://doi.org/10.1371/journal.pone.0347773}, pmid = {42048351}, pmcid = {PMC13123929} }

RIS

TY - JOUR AU - Bham, Kunal AU - Anandakrishnan, Manju AU - Wu, Cathy H AU - Ross, Karen E TI - A network-centric approach reveals novel pathways impacted by Prader-Willi Syndrome T2 - PloS one J2 - PLoS One PY - 2026 DA - 2026/04/28 VL - 21 IS - 4 SP - e0347773 SN - 1932-6203 PB - PLOS DO - 10.1371/journal.pone.0347773 UR - https://doi.org/10.1371/journal.pone.0347773 LA - en ER -

CSL-JSON

{ "id": "10.1371/journal.pone.0347773", "type": "article-journal", "title": "A network-centric approach reveals novel pathways impacted by Prader-Willi Syndrome", "container-title": "PloS one", "author": [ { "family": "Bham", "given": "Kunal" }, { "family": "Anandakrishnan", "given": "Manju" }, { "family": "Wu", "given": "Cathy H" }, { "family": "Ross", "given": "Karen E" } ], "container-title-short": "PLoS One", "volume": "21", "issue": "4", "page": "e0347773", "DOI": "10.1371/journal.pone.0347773", "PMID": "42048351", "PMCID": "PMC13123929", "ISSN": "1932-6203", "publisher": "PLOS", "URL": "https://doi.org/10.1371/journal.pone.0347773", "language": "en", "issued": { "date-parts": [ [ 2026, 4, 28 ] ] } }

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