Corpus Engine Towards a reproducible .science file.
← All .science files

pmid:35722410

Bioinformatic analyses of the role of m6A RNA methylation regulators in abdominal aortic aneurysm.

Changgeng Fu · Linghe Feng · Jiawei Zhang · Dajun Sun

Annals of translational medicine · 2022 doi:10.21037/atm-22-1891

Paper

complete

Experiment

partial

Data

partial

Analysis

partial

Execution

absent

Execution evidence has not yet been added to this .science file. Explore its methods, data and open questions to plan the next step.
Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
id
pmid:35722410
kind
science-record
content_hash
sha256:bf776045ab774a49dce4ba462019a8cc6a348af23b9b648c6818ce8774f36197
schema
https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json
created_at
2026-11-01T18:14:44Z
updated_at
2026-11-01T22:06:50Z
generator object · 2 fields
name
corpus-assay-engine
run_id
reading-sci-2026-11-01
paper object · 10 fields
state
complete
title
Bioinformatic analyses of the role of m6A RNA methylation regulators in abdominal aortic aneurysm.
doi
10.21037/atm-22-1891
pmid
35722410
authors array · 4 items
[0]
Changgeng Fu
[1]
Linghe Feng
[2]
Jiawei Zhang
[3]
Dajun Sun
journal
Annals of translational medicine
year
2022
abstract
Background: N6-methyladenosine (m6A) is found in almost all nuclear RNAs of eukaryotes, playing an important and diverse role in many biological processes. Nonetheless, the roles of m6A regulators in abdominal aortic aneurysm (AAA) unknown. Therefore, there is a pressing need to identify m6A RNA methylation regulators in the diagnosis of AAA, determination of individualized risk, discovery of therapeutic targets, and improve understanding of pathogenesis. Methods: The GSE98278 dataset were obtained from the Gene Expression Omnibus (GEO) database to perform differential analysis of m6A-related regulators between elective stable abdominal aortic aneurysms (eAAA) and abdominal aortic aneurysm ruptured (rAAA). The random forest model was used to screen candidate m6A regulators to predict the risk of rAAA. The single sample gene set enrichment analysis (ssGSEA) method was then used to evaluate the abundance of 23 immune cells in AAA. The m6A RNA Methylation Quantification Kit was used to measure the total m6A levels of AAA and normal abdominal aorta. The terminal deoxynucleotidyl transferase mediated dUTP Nick-End Labeling (TUNEL) apoptosis assay kit was used to detect the human aortic smooth muscle cells (HASMCs) apoptotic after RBM15 knockdown. Mechanically, RBM15 knockdown was found to reduce the expression of CASP3 in an m6A-dependent manner by Western blotting, RNA immunoprecipitation (RIP), and methylated RNA immunoprecipitation-quantitative polymerase chain reaction (MeRIP-RT-PCR). Results: RBM15, WTAP, ALKBH5, and IGFBP3 were highly expressed in rAAA. In contrast, RBM15B showed opposite results (P<0.05). The high m6A level in the rAAA compared with eAAA and normal abdominal aorta (P<0.05). The random forest model was used to screen 5 candidate m6A regulators to predict the risk of rAAA. Expression of the 5 m6A methylation regulators was validated in AAA samples (P<0.05). RBM15 knockdown inhibited the apoptosis of HASMCs. RBM15 knockdown reduced the expression of CASP3 in an m6A-dependent manner. A strong correlation between the five m6A methylation regulators and immune cell infiltration was identified. Conclusions: In summary, m6A regulators play nonnegligible roles in the occurrence of rAAA. Our investigation of m6A patterns may be able to guide future immunotherapy strategies for AAA.
claims array · 3 items
[0] object · 6 fields
id
claim-method
statement
The RF method was used to differentially express m6A methylation regulators to predict the occurrence of rAAA, and the calculations were performed using the R library “randomForest”.
kind
methodological
target
null
evidence array · 1 item
[0] object · 3 fields
asset
source:pmid:35722410
locator
sections[4] "The feature genes were screened based on the RF analysis" paragraph 2
quote
The RF method was used to differentially express m6A methylation regulators to predict the occurrence of rAAA, and the calculations were performed using the R library “randomForest”.
state
complete
[1] object · 6 fields
id
claim-data-access
statement
Data Sharing Statement: Available at https://atm.amegroups.com/article/view/10.21037/atm-22-1891/dss
kind
methodological
target
null
evidence array · 1 item
[0] object · 3 fields
asset
source:pmid:35722410
locator
sections[26] "(untitled)" paragraph 1
quote
Data Sharing Statement: Available at https://atm.amegroups.com/article/view/10.21037/atm-22-1891/dss
state
complete
[2] object · 6 fields
id
claim-target-result
statement
A random forest model screened five candidate m6A regulators to predict the risk of rAAA.
kind
qualitative
target
null
evidence array · 1 item
[0] object · 3 fields
asset
source:pmid:35722410
locator
sections[0] "Abstract" paragraph 3
quote
The random forest model was used to screen 5 candidate m6A regulators to predict the risk of rAAA.
state
complete
gaps array · 0 items
experiment object · 8 fields
state
partial
objective
A random forest model screened five candidate m6A regulators to predict the risk of rAAA.
steps array · 3 items
[0] object · 13 fields
id
read_published_estimate
order
1
name
Read the published estimate from the source
kind
other
state
complete
blocked_on
null
description
The claim, method and evidence span published with the source article.
parameters object · 0 fields
inputs array · 1 item
[0]
source:pmid:35722410
outputs array · 1 item
[0]
the published estimate and its evidence
instruments array · 0 items
reagents array · 0 items
[1] object · 13 fields
id
obtain_source_data
order
2
name
Obtain the source data from the named repository
kind
data-preparation
state
complete
blocked_on
null
description
The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.
parameters object · 0 fields
inputs array · 1 item
[0]
data:geo:GSE98278
outputs array · 1 item
[0]
the source data with its repository provenance
instruments array · 0 items
reagents array · 0 items
[2] object · 13 fields
id
run_analysis
order
3
name
Run the published analysis
kind
analysis
state
blocked
blocked_on
method:machine_learning_random_forest
description
Apply the paper's statistical method to the resolved series and compare the result with the published target.
parameters object · 0 fields
inputs array · 1 item
[0]
data:geo:GSE98278
outputs array · 1 item
[0]
the replicated result and its comparison
instruments array · 0 items
reagents array · 0 items
materials array · 0 items
instruments array · 0 items
reagents array · 0 items
conditions object · 0 fields
gaps array · 1 item
[0]
step:run_analysis blocked on method:machine_learning_random_forest
data object · 3 fields
state
partial
assets array · 2 items
[0] object · 15 fields
id
source:pmid:35722410
role
manuscript
format
xml
media_type
application/xml
uri
https://doi.org/10.21037/atm-22-1891
sha256
sha256:0b2a75d39f1d9423f138914d5d366a5544863133925d530a3005ade231d6f258
bytes
35049
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
10.21037/atm-22-1891
version
null
note
Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.
structure object · 4 fields
pmcid
PMC9201186
source
europe_pmc_fulltext
coverage
whole_body
text_hash_scope
rendered_section_body
[1] object · 15 fields
id
data:geo:GSE98278
role
raw
format
geo-series
media_type
application/vnd.ncbi.geo-series+json
uri
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE98278
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE98278
version
null
note
Data Sharing Statement: Available at https://atm.amegroups.com/article/view/10.21037/atm-22-1891/dss
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
Data Sharing Statement: Available at https://atm.amegroups.com/article/view/10.21037/atm-22-1891/dss
access_statement_kind
data_availability_statement
sample_accessions array · 48 items
[0]
GSM2590370
[1]
GSM2590371
[2]
GSM2590372
[3]
GSM2590373
[4]
GSM2590374
[5]
GSM2590375
[6]
GSM2590376
[7]
GSM2590377
[8]
GSM2590378
[9]
GSM2590379
[10]
GSM2590380
[11]
GSM2590381
[12]
GSM2590382
[13]
GSM2590383
[14]
GSM2590384
[15]
GSM2590385
[16]
GSM2590386
[17]
GSM2590387
[18]
GSM2590388
[19]
GSM2590389
[20]
GSM2590390
[21]
GSM2590391
[22]
GSM2590392
[23]
GSM2590393
[24]
GSM2590394
[25]
GSM2590395
[26]
GSM2590396
[27]
GSM2590397
[28]
GSM2590398
[29]
GSM2590399
[30]
GSM2590400
[31]
GSM2590401
[32]
GSM2590402
[33]
GSM2590403
[34]
GSM2590404
[35]
GSM2590405
[36]
GSM2590406
[37]
GSM2590407
[38]
GSM2590408
[39]
GSM2590409
[40]
GSM2590410
[41]
GSM2590411
[42]
GSM2590412
[43]
GSM2590413
[44]
GSM2590414
[45]
GSM2590415
[46]
GSM2590416
[47]
GSM2590417
sample_count
48
group_sizes
null
group_sizes_basis
null
platform
10558
taxon
Homo sapiens
title
A molecular fingerprint for terminal abdominal aortic aneurysm progression
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 · 2 items
[0]
asset:data:geo:GSE98278 not downloaded
[1]
asset:data:geo:GSE98278 licence not read
analysis object · 4 fields
state
partial
methods array · 7 items
[0] object · 10 fields
id
method-1
family
machine_learning
specific
random_forest
label
Machine learning
dsl_operations array · 0 items
missing_operations array · 1 item
[0]
machine_learning_random_forest
expressible
false
parameters object · 0 fields
data_shapes array · 1 item
[0]
labelled_feature_matrix
evidence object · 3 fields
score
1
band
verbatim
quote
The RF method was used to differentially express m6A methylation regulators to predict the occurrence of rAAA, and the calculations were performed using the R library “randomForest”.
[1] object · 10 fields
id
method-2
family
group_comparison
specific
anova
label
Group comparison
dsl_operations array · 4 items
[0]
group_summary
[1]
welch_t
[2]
bootstrap_mean_difference
[3]
group_comparison_anova
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
two_or_more_groups_continuous
evidence object · 3 fields
score
0.8
band
high
quote
analysis of variance
[2] object · 10 fields
id
method-3
family
machine_learning
specific
support_vector_machine
label
Machine learning
dsl_operations array · 3 items
[0]
train_classifier
[1]
cross_validate
[2]
roc_auc
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
labelled_feature_matrix
evidence object · 3 fields
score
0.8
band
high
quote
SVM
[3] object · 10 fields
id
method-4
family
regression_modelling
specific
ols
label
Regression modelling
dsl_operations array · 1 item
[0]
ols_fit
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
predictors_and_outcome
evidence object · 3 fields
score
0.8
band
high
quote
Linear regression
[4] object · 10 fields
id
method-5
family
correlation_analysis
specific
pearson
label
Correlation analysis
dsl_operations array · 1 item
[0]
pearson_r
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
paired_numeric_columns
evidence object · 3 fields
score
0.8
band
high
quote
Pearson correlation
[5] object · 10 fields
id
method-6
family
diagnostic_evaluation
specific
roc
label
Diagnostic test evaluation
dsl_operations array · 6 items
[0]
confusion_counts
[1]
roc_curve
[2]
sensitivity_specificity
[3]
solve_confusion_matrix
[4]
classification_metrics
[5]
published_precision_envelope
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
reference_standard_and_test
evidence object · 3 fields
score
0.8
band
high
quote
ROC curve
[6] object · 10 fields
id
method-7
family
model_calibration
specific
decision_curve
label
Model calibration / clinical utility
dsl_operations array · 2 items
[0]
calibration_bins
[1]
net_benefit
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
predictions_and_outcomes
evidence object · 3 fields
score
0.8
band
high
quote
decision curve analysis
targets array · 0 items
gaps array · 2 items
[0]
method.reader_differs_from_extractor
[1]
method.machine_learning_random_forest
capability object · 3 fields
state
complete
requirements array · 1 item
[0] object · 6 fields
capability_id
capability:computational_analysis
label
Computational analysis platform
assay_families array · 9 items
[0]
meta_analysis
[1]
machine_learning
[2]
survival_analysis
[3]
statistical_regression
[4]
pca
[5]
molecular_docking
[6]
network_analysis
[7]
differential_expression
[8]
gwas
candidate
Automated Assays Laravel application and simulated adapters
gaps array · 0 items
readiness object · 8 fields
state
complete
tier
R1
blocked_on
nothing:replicable_now
axes object · 3 fields
method object · 5 fields
tier
R1
expressible
false
required_operations array · 0 items
missing_operations array · 1 item
[0]
machine_learning_random_forest
basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: random_forest.
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 · 1 item
[0]
GSE98278
resolved_accessions array · 1 item
[0]
GSE98278
statements array · 1 item
[0]
Data Sharing Statement: Available at https://atm.amegroups.com/article/view/10.21037/atm-22-1891/dss
basis
The paper names GSE98278 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
can_run object · 3 fields
dsl_operations array · 0 items
assets array · 2 items
[0]
source:pmid:35722410
[1]
data:geo:GSE98278
steps array · 0 items
must_build object · 5 fields
dsl_operations array · 1 item
[0] object · 3 fields
operation
machine_learning_random_forest
priority
null
converters array · 0 items
readers array · 0 items
capabilities array · 0 items
data_gaps array · 1 item
[0]
data:geo:GSE98278
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
2026-11-01T17:51:34Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
verbatim
calls
5
usage object · 3 fields
prompt_tokens
10905
completion_tokens
1022
cached_tokens
128
stages array · 1 item
[0] object · 4 fields
id
extraction
state
complete
reader
MiniMax-M3.1-Flash
provider
minimax
gaps array · 3 items
[0]
review.independent_reader
[1]
review.machine_critique
[2]
review.human_critique
execution object · 5 fields
state
absent
status
not_run
mode
null
runs array · 0 items
gaps array · 1 item
[0]
execution.not_run
provenance object · 7 fields
entities array · 3 items
[0] object · 3 fields
id
pmid:35722410
type
paper
label
pmid:35722410
[1] object · 3 fields
id
source:pmid:35722410
type
manuscript
label
PMID 35722410 full text
[2] object · 3 fields
id
data:geo:GSE98278
type
dataset
label
GEO GSE98278
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:35722410
type
paper
label
pmid:35722410
[1] object · 3 fields
id
source:pmid:35722410
type
manuscript
label
PMID 35722410 full text
[2] object · 3 fields
id
data:geo:GSE98278
type
dataset
label
GEO GSE98278
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-35722410
type
extraction
used array · 1 item
[0]
source:pmid:35722410
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-GSE98278
type
accession_resolution
used array · 1 item
[0]
data:geo:GSE98278
agent
agent:corpus-assay-engine
agents array · 2 items
[0] object · 3 fields
id
agent:corpus-assay-engine
type
software
label
Corpus assay engine
[1] object · 3 fields
id
agent:minimax-reader
type
software
label
MiniMax literature reader
source_run_id
reading-sci-2026-11-01
dsl_catalogue_fingerprint
343fb30756ba6ec3
capability_table_fingerprint
2cb3e42f047d2ecf
snapshot_id
ba34b49490d9bfaf
lineage object · 4 fields
parent_id
pmid:35722410
parent_hash
sha256:936e9a1d1d50171989c228542692d4aad29c26225b0082bd6bda9dc69e7dc83b
fork_of
null
gaps array · 9 items
[0]
asset:data:geo:GSE98278 licence not read
[1]
asset:data:geo:GSE98278 not downloaded
[2]
execution.not_run
[3]
method.machine_learning_random_forest
[4]
method.reader_differs_from_extractor
[5]
review.human_critique
[6]
review.independent_reader
[7]
review.machine_critique
[8]
step:run_analysis blocked on method:machine_learning_random_forest
assays array · 5 items
[0] object · 11 fields
role
data_generation
definition_id
apoptosis_assay
definition_version
99e1195c7d6f
required_operations array · 0 items
support_state
partial
source_hashes object · 1 field
assay_lexicon
99e1195c7d6f
confidence
0.93
evidence array · 2 items
[0]
rase mediated dUTP Nick-End Labeling (TUNEL) apoptosis assay kit was used to detect the human aorti
[1]
ples (P<0.05). RBM15 knockdown inhibited the apoptosis of HASMCs. RBM15 knockdown reduced the expre
order_basis
paper_text_order:1198
id
assay:1
[1] object · 11 fields
role
data_generation
definition_id
transfection
definition_version
99e1195c7d6f
required_operations array · 0 items
support_state
partial
source_hashes object · 1 field
assay_lexicon
99e1195c7d6f
confidence
0.99
evidence array · 4 items
[0]
muscle cells (HASMCs) apoptotic after RBM15 knockdown. Mechanically, RBM15 knockdown was found to
[1]
c after RBM15 knockdown. Mechanically, RBM15 knockdown was found to reduce the expression of CASP3
[2]
was validated in AAA samples (P<0.05). RBM15 knockdown inhibited the apoptosis of HASMCs. RBM15 kno
[3]
own inhibited the apoptosis of HASMCs. RBM15 knockdown reduced the expression of CASP3 in an m6A-de
order_basis
paper_text_order:1306
id
assay:2
[2] object · 11 fields
role
data_generation
definition_id
western_blot
definition_version
99e1195c7d6f
required_operations array · 0 items
support_state
partial
source_hashes object · 1 field
assay_lexicon
99e1195c7d6f
confidence
0.93
evidence array · 1 item
[0]
ssion of CASP3 in an m6A-dependent manner by Western blotting, RNA immunoprecipitation (RIP), and methylat
order_basis
paper_text_order:1420
id
assay:3
[3] object · 11 fields
role
data_generation
definition_id
cell_culture
definition_version
99e1195c7d6f
required_operations array · 0 items
support_state
partial
source_hashes object · 1 field
assay_lexicon
99e1195c7d6f
confidence
0.99
evidence array · 2 items
[0]
was taken from all the patients. HASMCs were cultured in a smooth muscle cell growth medium (SMCM), w
[1]
was taken from all the patients. HASMCs were cultured in a smooth muscle cell growth medium (SMCM)
order_basis
paper_text_order:2887
id
assay:4
[4] object · 11 fields
role
analysis
definition_id
machine_learning
definition_version
f2132e96ae6c7a4e
required_operations array · 0 items
support_state
partial
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
The RF method was used to differentially express m6A methylation regulators to predict the occurrence of rAAA, and the calculations were performed using the R library “randomForest”.
order_basis
consumes_earlier_output
id
assay:5
Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35722410", "kind": "science-record", "revision": 4, "content_hash": "sha256:bf776045ab774a49dce4ba462019a8cc6a348af23b9b648c6818ce8774f36197", "created_at": "2026-11-01T18:14:44Z", "updated_at": "2026-11-01T22:06:50Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Bioinformatic analyses of the role of m6A RNA methylation regulators in abdominal aortic aneurysm.", "doi": "10.21037/atm-22-1891", "pmid": "35722410", "authors": ["Changgeng Fu", "Linghe Feng", "Jiawei Zhang", "Dajun Sun"], "journal": "Annals of translational medicine", "year": 2022, "abstract": "Background: N6-methyladenosine (m6A) is found in almost all nuclear RNAs of eukaryotes, playing an important and diverse role in many biological processes. Nonetheless, the roles of m6A regulators in abdominal aortic aneurysm (AAA) unknown. Therefore, there is a pressing need to identify m6A RNA methylation regulators in the diagnosis of AAA, determination of individualized risk, discovery of therapeutic targets, and improve understanding of pathogenesis.\nMethods: The GSE98278 dataset were obtained from the Gene Expression Omnibus (GEO) database to perform differential analysis of m6A-related regulators between elective stable abdominal aortic aneurysms (eAAA) and abdominal aortic aneurysm ruptured (rAAA). The random forest model was used to screen candidate m6A regulators to predict the risk of rAAA. The single sample gene set enrichment analysis (ssGSEA) method was then used to evaluate the abundance of 23 immune cells in AAA. The m6A RNA Methylation Quantification Kit was used to measure the total m6A levels of AAA and normal abdominal aorta. The terminal deoxynucleotidyl transferase mediated dUTP Nick-End Labeling (TUNEL) apoptosis assay kit was used to detect the human aortic smooth muscle cells (HASMCs) apoptotic after RBM15 knockdown. Mechanically, RBM15 knockdown was found to reduce the expression of CASP3 in an m6A-dependent manner by Western blotting, RNA immunoprecipitation (RIP), and methylated RNA immunoprecipitation-quantitative polymerase chain reaction (MeRIP-RT-PCR).\nResults: RBM15, WTAP, ALKBH5, and IGFBP3 were highly expressed in rAAA. In contrast, RBM15B showed opposite results (P<0.05). The high m6A level in the rAAA compared with eAAA and normal abdominal aorta (P<0.05). The random forest model was used to screen 5 candidate m6A regulators to predict the risk of rAAA. Expression of the 5 m6A methylation regulators was validated in AAA samples (P<0.05). RBM15 knockdown inhibited the apoptosis of HASMCs. RBM15 knockdown reduced the expression of CASP3 in an m6A-dependent manner. A strong correlation between the five m6A methylation regulators and immune cell infiltration was identified.\nConclusions: In summary, m6A regulators play nonnegligible roles in the occurrence of rAAA. Our investigation of m6A patterns may be able to guide future immunotherapy strategies for AAA.", "claims": [{"id": "claim-method", "statement": "The RF method was used to differentially express m6A methylation regulators to predict the occurrence of rAAA, and the calculations were performed using the R library “randomForest”.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35722410", "locator": "sections[4] \"The feature genes were screened based on the RF analysis\" paragraph 2", "quote": "The RF method was used to differentially express m6A methylation regulators to predict the occurrence of rAAA, and the calculations were performed using the R library “randomForest”."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data Sharing Statement: Available at https://atm.amegroups.com/article/view/10.21037/atm-22-1891/dss", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35722410", "locator": "sections[26] \"(untitled)\" paragraph 1", "quote": "Data Sharing Statement: Available at https://atm.amegroups.com/article/view/10.21037/atm-22-1891/dss"}], "state": "complete"}, {"id": "claim-target-result", "statement": "A random forest model screened five candidate m6A regulators to predict the risk of rAAA.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35722410", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The random forest model was used to screen 5 candidate m6A regulators to predict the risk of rAAA."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "A random forest model screened five candidate m6A regulators to predict the risk of rAAA.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:35722410"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE98278"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:machine_learning_random_forest", "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE98278"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:machine_learning_random_forest"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35722410", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/atm-22-1891", "sha256": "sha256:0b2a75d39f1d9423f138914d5d366a5544863133925d530a3005ade231d6f258", "bytes": 35049, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.21037/atm-22-1891", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9201186", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE98278", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE98278", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE98278", "version": null, "note": "Data Sharing Statement: Available at https://atm.amegroups.com/article/view/10.21037/atm-22-1891/dss", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Data Sharing Statement: Available at https://atm.amegroups.com/article/view/10.21037/atm-22-1891/dss", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2590370", "GSM2590371", "GSM2590372", "GSM2590373", "GSM2590374", "GSM2590375", "GSM2590376", "GSM2590377", "GSM2590378", "GSM2590379", "GSM2590380", "GSM2590381", "GSM2590382", "GSM2590383", "GSM2590384", "GSM2590385", "GSM2590386", "GSM2590387", "GSM2590388", "GSM2590389", "GSM2590390", "GSM2590391", "GSM2590392", "GSM2590393", "GSM2590394", "GSM2590395", "GSM2590396", "GSM2590397", "GSM2590398", "GSM2590399", "GSM2590400", "GSM2590401", "GSM2590402", "GSM2590403", "GSM2590404", "GSM2590405", "GSM2590406", "GSM2590407", "GSM2590408", "GSM2590409", "GSM2590410", "GSM2590411", "GSM2590412", "GSM2590413", "GSM2590414", "GSM2590415", "GSM2590416", "GSM2590417"], "sample_count": 48, "group_sizes": null, "group_sizes_basis": null, "platform": "10558", "taxon": "Homo sapiens", "title": "A molecular fingerprint for terminal abdominal aortic aneurysm progression", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE98278 not downloaded", "asset:data:geo:GSE98278 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "machine_learning", "specific": "random_forest", "label": "Machine learning", "dsl_operations": [], "missing_operations": ["machine_learning_random_forest"], "expressible": false, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "The RF method was used to differentially express m6A methylation regulators to predict the occurrence of rAAA, and the calculations were performed using the R library “randomForest”."}}, {"id": "method-2", "family": "group_comparison", "specific": "anova", "label": "Group comparison", "dsl_operations": ["group_summary", "welch_t", "bootstrap_mean_difference", "group_comparison_anova"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "analysis of variance"}}, {"id": "method-3", "family": "machine_learning", "specific": "support_vector_machine", "label": "Machine learning", "dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "SVM"}}, {"id": "method-4", "family": "regression_modelling", "specific": "ols", "label": "Regression modelling", "dsl_operations": ["ols_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["predictors_and_outcome"], "evidence": {"score": 0.8, "band": "high", "quote": "Linear regression"}}, {"id": "method-5", "family": "correlation_analysis", "specific": "pearson", "label": "Correlation analysis", "dsl_operations": ["pearson_r"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["paired_numeric_columns"], "evidence": {"score": 0.8, "band": "high", "quote": "Pearson correlation"}}, {"id": "method-6", "family": "diagnostic_evaluation", "specific": "roc", "label": "Diagnostic test evaluation", "dsl_operations": ["confusion_counts", "roc_curve", "sensitivity_specificity", "solve_confusion_matrix", "classification_metrics", "published_precision_envelope"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["reference_standard_and_test"], "evidence": {"score": 0.8, "band": "high", "quote": "ROC curve"}}, {"id": "method-7", "family": "model_calibration", "specific": "decision_curve", "label": "Model calibration / clinical utility", "dsl_operations": ["calibration_bins", "net_benefit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["predictions_and_outcomes"], "evidence": {"score": 0.8, "band": "high", "quote": "decision curve analysis"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor", "method.machine_learning_random_forest"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": false, "required_operations": [], "missing_operations": ["machine_learning_random_forest"], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: random_forest."}, "capability": {"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": {"tier": "R1", "reachable": true, "accessions": ["GSE98278"], "resolved_accessions": ["GSE98278"], "statements": ["Data Sharing Statement: Available at https://atm.amegroups.com/article/view/10.21037/atm-22-1891/dss"], "basis": "The paper names GSE98278 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:35722410", "data:geo:GSE98278"], "steps": []}, "must_build": {"dsl_operations": [{"operation": "machine_learning_random_forest", "unlocks_papers": null, "priority": null}], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE98278"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T17:51:34Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 10905, "completion_tokens": 1022, "cached_tokens": 128}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:35722410", "type": "paper", "label": "pmid:35722410"}, {"id": "source:pmid:35722410", "type": "manuscript", "label": "PMID 35722410 full text"}, {"id": "data:geo:GSE98278", "type": "dataset", "label": "GEO GSE98278"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:35722410", "type": "paper", "label": "pmid:35722410"}, {"id": "source:pmid:35722410", "type": "manuscript", "label": "PMID 35722410 full text"}, {"id": "data:geo:GSE98278", "type": "dataset", "label": "GEO GSE98278"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-35722410", "type": "extraction", "used": ["source:pmid:35722410"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE98278", "type": "accession_resolution", "used": ["data:geo:GSE98278"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:35722410", "parent_hash": "sha256:936e9a1d1d50171989c228542692d4aad29c26225b0082bd6bda9dc69e7dc83b", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE98278 licence not read", "asset:data:geo:GSE98278 not downloaded", "execution.not_run", "method.machine_learning_random_forest", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:run_analysis blocked on method:machine_learning_random_forest"], "assays": [{"role": "data_generation", "definition_id": "apoptosis_assay", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["rase mediated dUTP Nick-End Labeling (TUNEL) apoptosis assay kit was used to detect the human aorti", "ples (P<0.05). RBM15 knockdown inhibited the apoptosis of HASMCs. RBM15 knockdown reduced the expre"], "order_basis": "paper_text_order:1198", "id": "assay:1"}, {"role": "data_generation", "definition_id": "transfection", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["muscle cells (HASMCs) apoptotic after RBM15 knockdown. Mechanically, RBM15 knockdown was found to", "c after RBM15 knockdown. Mechanically, RBM15 knockdown was found to reduce the expression of CASP3", "was validated in AAA samples (P<0.05). RBM15 knockdown inhibited the apoptosis of HASMCs. RBM15 kno", "own inhibited the apoptosis of HASMCs. RBM15 knockdown reduced the expression of CASP3 in an m6A-de"], "order_basis": "paper_text_order:1306", "id": "assay:2"}, {"role": "data_generation", "definition_id": "western_blot", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["ssion of CASP3 in an m6A-dependent manner by Western blotting, RNA immunoprecipitation (RIP), and methylat"], "order_basis": "paper_text_order:1420", "id": "assay:3"}, {"role": "data_generation", "definition_id": "cell_culture", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["was taken from all the patients. HASMCs were cultured in a smooth muscle cell growth medium (SMCM), w", "was taken from all the patients. HASMCs were cultured in a smooth muscle cell growth medium (SMCM)"], "order_basis": "paper_text_order:2887", "id": "assay:4"}, {"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["The RF method was used to differentially express m6A methylation regulators to predict the occurrence of rAAA, and the calculations were performed using the R library “randomForest”."], "order_basis": "consumes_earlier_output", "id": "assay:5"}]}