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pmid:41133270

E-ABIN: an explainable module for anomaly detection in biological networks.

Ugo Lomoio · Tommaso Mazza · Pierangelo Veltri · Pietro Hiram Guzzi

NAR genomics and bioinformatics · 2025 doi:10.1093/nargab/lqaf135

Paper

complete

Experiment

complete

Data

partial

Analysis

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E-ABIN: an explainable module for anomaly detection in biological networks.
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10.1093/nargab/lqaf135
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41133270
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Ugo Lomoio
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Tommaso Mazza
[2]
Pierangelo Veltri
[3]
Pietro Hiram Guzzi
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NAR genomics and bioinformatics
year
2025
abstract
The increasing availability of large-scale omics data calls for robust analytical frameworks capable of handling complex gene expression datasets while offering interpretable results. Recent advances in artificial intelligence have enabled the identification of aberrant molecular patterns distinguishing disease states from healthy controls. Coupled with improvements in model interpretability, these tools now support the identification of genes potentially driving disease phenotypes. However, current approaches to gene anomaly detection often remain limited to single datasets and lack accessible graphical interfaces. We introduce E-ABIN, a general-purpose, explainable framework for anomaly detection in biological networks. E-ABIN combines classical machine learning and graph-based deep learning techniques within a unified, user-friendly platform, enabling the detection and interpretation of anomalies from gene expression or methylation-derived networks. By integrating algorithms such as support vector machines, random forests, graph autoencoders, and graph adversarial attributed networks, E-ABIN ensures high predictive accuracy while maintaining interpretability. We demonstrate the utility of E-ABIN through case studies of bladder cancer and celiac disease, where it effectively uncovers biologically relevant anomalies and offers insights into disease mechanisms. E-ABIN is freely available on the Zenodo platform at https://doi.org/10.5281/zenodo.17062501.
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Receiver Operating Characteristic (ROC) -area under curve (AUC) scores on the test set for all the datasets.
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Specifically, E-ABIN (LR) and Deep GONet achieve perfect scores (1.0) on GSE37817 and GSE21510 and deliver higher performance than Deep GONet on the other two datasets as well (0.73 versus 0.5909 for GSE11501 and 0.64 versus 0.5 for GSE6613).
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sections[8] "Comparison with state-of-the-art tools" paragraph 3
quote
Specifically, E-ABIN (LR) and Deep GONet achieve perfect scores (1.0) on GSE37817 and GSE21510 and deliver higher performance than Deep GONet on the other two datasets as well (0.73 versus 0.5909 for GSE11501 and 0.64 versus 0.5 for GSE6613).
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Specifically, E-ABIN (LR) and Deep GONet achieve perfect scores (1.0) on GSE37817 and GSE21510 and deliver higher performance than Deep GONet on the other two datasets as well (0.73 versus 0.5909 for GSE11501 and 0.64 versus 0.5 for GSE6613).
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sample_count
105
group_sizes
null
group_sizes_basis
null
platform
96
taxon
Homo sapiens
title
Parkinson's disease vs. controls, whole blood
resolution_checked_at
2026-11-01T15:39:55Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
gaps array · 8 items
[0]
asset:data:geo:GSE11501 not downloaded
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asset:data:geo:GSE21510 not downloaded
[3]
asset:data:geo:GSE21510 licence not read
[4]
asset:data:geo:GSE37817 not downloaded
[5]
asset:data:geo:GSE37817 licence not read
[6]
asset:data:geo:GSE6613 not downloaded
[7]
asset:data:geo:GSE6613 licence not read
analysis object · 4 fields
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id
method-1
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principal_component_analysis
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label
Principal component analysis
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pca_fit
missing_operations array · 0 items
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wide_numeric_matrix
evidence object · 3 fields
score
0.8
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PCA
[1] object · 10 fields
id
method-2
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machine_learning
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support_vector_machine
label
Machine learning
dsl_operations array · 3 items
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train_classifier
[1]
cross_validate
[2]
roc_auc
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labelled_feature_matrix
evidence object · 3 fields
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0.7
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support vector machines
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id
method-3
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null
specific
null
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Receiver Operating Characteristic (ROC) -area under curve (AUC)
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missing_operations array · 0 items
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evidence object · 3 fields
score
1
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Receiver Operating Characteristic (ROC) -area under curve (AUC) scores on the test set for all the datasets.
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gaps array · 1 item
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method.reader_named_unmapped
capability object · 3 fields
state
complete
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capability_id
capability:computational_analysis
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Computational analysis platform
assay_families array · 9 items
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meta_analysis
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machine_learning
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survival_analysis
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candidate
Automated Assays Laravel application and simulated adapters
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complete
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nothing:replicable_now
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pca_fit
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basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: pca.
capability object · 4 fields
tier
R1
capability_id
capability:computational_analysis
availability_state
owned
basis
The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
data object · 6 fields
tier
R1
reachable
true
accessions array · 4 items
[0]
GSE37817
[1]
GSE11501
[2]
GSE21510
[3]
GSE6613
resolved_accessions array · 4 items
[0]
GSE37817
[1]
GSE11501
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GSE21510
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GSE6613
statements array · 1 item
[0]
Software and user guide are available at https://doi.org/10.5281/zenodo.17062501.
basis
The paper names GSE37817, GSE11501, GSE21510, GSE6613 in GEO data repository and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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pca_fit
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source:pmid:41133270
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data:geo:GSE11501
gaps array · 0 items
review object · 5 fields
state
partial
machine_critique object · 5 fields
kind
extraction_review
reader object · 5 fields
model
MiniMax-M3.1-Flash
provider
minimax
provider_family
minimax
read_at
2026-11-01T20:03:55Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
verbatim
calls
5
usage object · 3 fields
prompt_tokens
8342
completion_tokens
2476
cached_tokens
256
stages array · 1 item
[0] object · 4 fields
id
extraction
state
complete
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MiniMax-M3.1-Flash
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minimax
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review.human_critique
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absent
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not_run
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null
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type
paper
label
pmid:41133270
[1] object · 3 fields
id
source:pmid:41133270
type
manuscript
label
PMID 41133270 full text
[2] object · 3 fields
id
data:geo:GSE11501
type
dataset
label
GEO GSE11501
activities array · 3 items
[0] object · 4 fields
id
activity-reading-sci-2026-11-01
type
classification
used array · 3 items
[0] object · 3 fields
id
pmid:41133270
type
paper
label
pmid:41133270
[1] object · 3 fields
id
source:pmid:41133270
type
manuscript
label
PMID 41133270 full text
[2] object · 3 fields
id
data:geo:GSE11501
type
dataset
label
GEO GSE11501
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-41133270
type
extraction
used array · 1 item
[0]
source:pmid:41133270
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-GSE11501
type
accession_resolution
used array · 1 item
[0]
data:geo:GSE11501
agent
agent:corpus-assay-engine
agents array · 2 items
[0] object · 3 fields
id
agent:corpus-assay-engine
type
software
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Corpus assay engine
[1] object · 3 fields
id
agent:minimax-reader
type
software
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MiniMax literature reader
source_run_id
reading-sci-2026-11-01
dsl_catalogue_fingerprint
343fb30756ba6ec3
capability_table_fingerprint
2cb3e42f047d2ecf
snapshot_id
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lineage object · 4 fields
parent_id
pmid:41133270
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sha256:a5b5b12799e2ffabd361f5aa94de63a1e1426d869d34f83eadaca5451d2f1143
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null
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[6]
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execution.not_run
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method.reader_named_unmapped
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review.independent_reader
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review.machine_critique
assays array · 1 item
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role
analysis
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pca_fit
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supported
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41133270", "kind": "science-record", "revision": 3, "content_hash": "sha256:636de175ccd2205e6987df53e43c06c4f453700687c92114f14d1ca1fa68a938", "created_at": "2026-11-01T20:31:11Z", "updated_at": "2026-11-01T22:14:53Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "E-ABIN: an explainable module for anomaly detection in biological networks.", "doi": "10.1093/nargab/lqaf135", "pmid": "41133270", "authors": ["Ugo Lomoio", "Tommaso Mazza", "Pierangelo Veltri", "Pietro Hiram Guzzi"], "journal": "NAR genomics and bioinformatics", "year": 2025, "abstract": "The increasing availability of large-scale omics data calls for robust analytical frameworks capable of handling complex gene expression datasets while offering interpretable results. Recent advances in artificial intelligence have enabled the identification of aberrant molecular patterns distinguishing disease states from healthy controls. Coupled with improvements in model interpretability, these tools now support the identification of genes potentially driving disease phenotypes. However, current approaches to gene anomaly detection often remain limited to single datasets and lack accessible graphical interfaces. We introduce E-ABIN, a general-purpose, explainable framework for anomaly detection in biological networks. E-ABIN combines classical machine learning and graph-based deep learning techniques within a unified, user-friendly platform, enabling the detection and interpretation of anomalies from gene expression or methylation-derived networks. By integrating algorithms such as support vector machines, random forests, graph autoencoders, and graph adversarial attributed networks, E-ABIN ensures high predictive accuracy while maintaining interpretability. We demonstrate the utility of E-ABIN through case studies of bladder cancer and celiac disease, where it effectively uncovers biologically relevant anomalies and offers insights into disease mechanisms. 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