OSCR

DBSCAN applied to EHRs data from patients with glioblastoma clusters patients based on cytosolic Hsp70 protein, sex, and brain subventricular zone.

Overview

Authors: Davide Chicco1,2, Srinjoy Dora3, Luca Oneto4
ORCID iDs: Davide Chicco, Srinjoy Dora, Luca Oneto
  1. Università di Milano-Bicocca, Milan, Italy
  2. University of Toronto, Toronto, ON Canada
  3. Tbilisi State Medical University, Tbilisi, Georgia
  4. Universitá di Genova, Genoa, Italy
Institutions: University of Toronto (Canada); University of Milano-Bicocca (Italy); Tbilisi State Medical University (Georgia); University of Genoa (Italy)
Journal: BioData mining, volume 19, issue 1, article 32
Dates: received 5 December 2025; accepted 17 March 2026; published online 27 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1186/s13040-026-00549-x · PMID 41888940 · PMCID PMC13147812 · OpenAlex W7140622296
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: other (modality), human (organism), other condition (population), clinical / translational (subfield)
Methods: Machine learning, Smoothing, state filtering, decompositions
Keywords: Clustering, Unsupervised machine learning, Machine learning, Glioblastoma, Electronic health records, EHRs
Topic: Heat shock proteins research (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: European Commission
Citations: not cited yet (Europe PMC); 64 references in the paper

Abstract

Glioblastoma is an aggressive brain cancer that kills approximately one hundred thousand people worldwide every year. Unfortunately, treatment and therapy for patients with this disease are complicated and have limited efficacy in improving individuals’ chances of survival. Electronic health records (EHRs) contain patient information collected routinely at hospitals through medical visits and laboratory tests, providing an interesting source of data for computational analyses. Clustering is an area of unsupervised machine learning where an algorithm partitions data according to certain statistical properties or rules, thereby identifying hidden patterns and correlations that would otherwise be difficult to notice. In this study, we applied several clustering techniques to three open datasets (Munich2019, Tainan2020, and Utrecht2019) derived from electronic health records, which included clinical, genetic, and administrative features of patients diagnosed with glioblastoma, considering two possible clusters. We evaluated our clustering results with the Density-Based Clustering Validation (DBCV) index, a relatively new score capable of accurately assessing both convex-shaped and concave-shaped clusters. Among the methods tested, Density-based Spatial Clustering of Applications with Noise (DBSCAN) yielded the best results across all three datasets. We then analyzed the features of the clusters identified by DBSCAN and found that cytosolic Hsp70 protein in the Munich2019 dataset, sex in the Tainan2020 dataset, and brain subventricular zone in the Utrecht2019 resulted significantly capable to distinguish the two clusters.

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

Code

No file of the authors' code could be read here: it is described below, and read at its source.

colab.research.google.com/drive/1hye58egradylwyhjkbvg-wfjngtzrbev

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Software availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
At the source: colab.research.google.com/drive/1hyE58egraDylwyHjKbvg-wfJnGTzRbEv?usp=sharing

Software availability

Our Python scripts can be openly found on Google Colab at https://colab.research.google.com/drive/1hyE58egraDylwyHjKbvg-wfJnGTzRbEv?usp=sharing

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

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;
  • 0 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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

No dataset and no data link were found in the paper.

Data availability

No datasets were generated or analysed during the current study.

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, 3 authors, 6 keywords, 1 funder, 60 references.

Cite

This paper

Chicco, D., Dora, S., & Oneto, L. (2026). DBSCAN applied to EHRs data from patients with glioblastoma clusters patients based on cytosolic Hsp70 protein, sex, and brain subventricular zone. BioData mining, 19(1), 32. https://doi.org/10.1186/s13040-026-00549-x

BibTeX

@article{chicco2026dbscan,
author = {Chicco, Davide and Dora, Srinjoy and Oneto, Luca},
title = {{DBSCAN applied to EHRs data from patients with glioblastoma clusters patients based on cytosolic Hsp70 protein, sex, and brain subventricular zone}},
journal = {BioData mining},
year = {2026},
month = mar,
volume = {19},
number = {1},
pages = {32},
publisher = {BMC},
issn = {1756-0381},
doi = {10.1186/s13040-026-00549-x},
url = {https://doi.org/10.1186/s13040-026-00549-x},
pmid = {41888940},
pmcid = {PMC13147812}
}

RIS

TY - JOUR
AU - Chicco, Davide
AU - Dora, Srinjoy
AU - Oneto, Luca
TI - DBSCAN applied to EHRs data from patients with glioblastoma clusters patients based on cytosolic Hsp70 protein, sex, and brain subventricular zone
T2 - BioData mining
J2 - BioData Min
PY - 2026
DA - 2026/03/27
VL - 19
IS - 1
SP - 32
SN - 1756-0381
PB - BMC
DO - 10.1186/s13040-026-00549-x
UR - https://doi.org/10.1186/s13040-026-00549-x
LA - en
ER -

CSL-JSON

{
"id": "10.1186/s13040-026-00549-x",
"type": "article-journal",
"title": "DBSCAN applied to EHRs data from patients with glioblastoma clusters patients based on cytosolic Hsp70 protein, sex, and brain subventricular zone",
"container-title": "BioData mining",
"author": [
{
"family": "Chicco",
"given": "Davide"
},
{
"family": "Dora",
"given": "Srinjoy"
},
{
"family": "Oneto",
"given": "Luca"
}
],
"container-title-short": "BioData Min",
"volume": "19",
"issue": "1",
"page": "32",
"DOI": "10.1186/s13040-026-00549-x",
"PMID": "41888940",
"PMCID": "PMC13147812",
"ISSN": "1756-0381",
"publisher": "BMC",
"URL": "https://doi.org/10.1186/s13040-026-00549-x",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
27
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.7717/peerj-cs.2256
Ten quick tips for clinical electroencephalographic (EEG) data acquisition and signal processing.
Journal: PeerJ. Computer science
In common: clinical / translational, 1 reference, author Davide Chicco
[2] doi:10.1186/s13293-026-00927-4 [code]
Gene regulatory network analysis identifies dysregulation of hypoxia pathways as contributing to glioblastoma treatment resistance in females.
Journal: Biology of sex differences
In common: other condition, 4 references
[3] doi:10.1038/s41698-024-00632-8 [code]
Combination of MRI-based prediction and CRISPR/Cas12a-based detection for IDH genotyping in glioma.
Journal: NPJ precision oncology
In common: other condition, 3 references
[4] doi:10.1038/s41467-025-65557-7 [code]
Enhanced spatial clustering of single-molecule localizations with graph neural networks.
Journal: Nature communications
In common: 3 references
[5] doi:10.1007/s11060-024-04849-2 [code]
Advanced imaging reveals enhanced malignancy in glioblastomas involving the subventricular zone: evidence of increased infiltrative growth and perfusion.
Journal: Journal of neuro-oncology
In common: clinical / translational, other condition, 2 references
[6] doi:10.1038/s41586-026-10451-5
Androgen loss accelerates brain tumour growth via HPA axis activation.
Journal: Nature
In common: other condition, 2 references
[7] doi:10.7554/elife.79305 [code]
Open-source tools for behavioral video analysis: Setup, methods, and best practices.
Journal: eLife
In common: 3 references
[8] doi:10.1038/s41698-024-00578-x [code]
Evaluating cell culture reliability in pediatric brain tumor primary cells through DNA methylation profiling.
Journal: NPJ precision oncology
In common: other condition, 2 references
[9] doi:10.3389/fpsyt.2026.1855963 [code]
Transient entanglement in minimal open XXZ spin chains: a toy-model analogy for microtubule-inspired quantum biology.
Journal: Frontiers in psychiatry
In common: clinical / translational, 2 references
[10] doi:10.1038/s41467-025-62528-w [code]
Cross-species comparison reveals therapeutic vulnerabilities halting glioblastoma progression.
Journal: Nature communications
In common: other condition, 2 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Add the badge to the code's README

OSCR: the badge of this paper

If you are an author or a maintainer of this paper's code, you can add this badge. It says what OSCR knows of the paper (its code, its validated tracing map) and links the code to this page. Paste one of these snippets under the README's title. Keep the two marks around it, so that the badge can be replaced in place and is never added twice. OSCR changes nothing in your repository.

Markdown
<!-- oscr-badge:start -->
[![OSCR](https://openscicode.org/badge/doi/10.1186/s13040-026-00549-x.svg)](https://openscicode.org/badge/doi/10.1186/s13040-026-00549-x)
<!-- oscr-badge:end -->
reStructuredText
.. oscr-badge:start

.. image:: https://openscicode.org/badge/doi/10.1186/s13040-026-00549-x.svg
   :target: https://openscicode.org/badge/doi/10.1186/s13040-026-00549-x
   :alt: OSCR

.. oscr-badge:end
HTML
<!-- oscr-badge:start -->
<a href="https://openscicode.org/badge/doi/10.1186/s13040-026-00549-x"><img src="https://openscicode.org/badge/doi/10.1186/s13040-026-00549-x.svg" alt="OSCR"></a>
<!-- oscr-badge:end -->

If you choose to, you can have the badge kept up to date for you, with oscr badge --write from the command line or with the GitHub Action in your repository's workflows. Every other form and option (a card, AsciiDoc, LaTeX, shields.io) is on the badge page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: code errors and mismatches between the code and the paper about this paper and its code, from readers and its authors, beside the paper and its tracing map.

Report a mismatch between the code and the paper · Report a code error (signed in)

Reproductions: reports from readers who ran the authors' code and did not get the paper's result, what they ran, with which environment, commit and data.

Report a reproduction failure (signed in)

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.