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pmid:40647578

Integrating Clinical and Transcriptomic Profiles Associated with Vitamin D to Enhance Disease-Free Survival in Cervical Cancer Recurrence Using the CatBoost Algorithm.

Diagnostics (Basel, Switzerland) · 2025 doi:10.3390/diagnostics15131579

Paper

partial

Experiment

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Data

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Analysis

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:40647578
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science-record
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title
Integrating Clinical and Transcriptomic Profiles Associated with Vitamin D to Enhance Disease-Free Survival in Cervical Cancer Recurrence Using the CatBoost Algorithm.
doi
10.3390/diagnostics15131579
pmid
40647578
authors
null
journal
Diagnostics (Basel, Switzerland)
year
2025
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null
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Result: CatBoost performs the best overall, producing an accuracy of 95.27%.
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Clinical representation of important features.
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3. Materials and Methods
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Clinical representation of important features.
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Patients Count
[2]
Mean
[3]
Std. Dev.
[4]
Min
[5]
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[6]
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[7]
75%
[8]
Max
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24.84
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19.90
[6]
24.95
[7]
29.80
[8]
51.30
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1.87
[3]
0.75
[4]
1
[5]
1
[6]
2
[7]
2
[8]
3
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4.26
[3]
1.94
[4]
1
[5]
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[6]
4
[7]
5
[8]
8
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[5]
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[7]
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Feature importance score for clinical prediction (LightGBM output).
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vitamin_d_level
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Different models’ classification reports.
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Different models’ classification reports.
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Algorithm 1. Mutual information-based CatBoost attribute selection and classification
[1] array · 1 item
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CatBoost attributes were selected and classified [49] well based on mutual information, and the model performed well, with high accuracy in this combination.
[2] array · 1 item
[0]
Step 1: Data preparation.
[3] array · 2 items
[0]
S={yk,zl}k=1 N
[1]
(25)
[4] array · 1 item
[0]
In the full dataset with N samples, each data point consists of
[5] array · 2 items
[0]
y(k)=[y1k,y2k…ydk]∈Rd
[1]
(26)
[6] array · 1 item
[0]
Feature vector of sample k contains d.
[7] array · 2 items
[0]
y(k)∈Y (Target class)
[1]
(27)
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(28)
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[0]
yk—k th feature in the cervical cancer dataset
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fYtraini=catboost classifier.fit(Ytraini,z)
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Descriptive statistics of recurrence status vs. vitamin D.
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4.1. Clinical Correlation of Vitamin D with Recurrence
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Descriptive statistics of recurrence status vs. vitamin D.
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[0] array · 5 items
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[2]
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[3]
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[4]
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[4]
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Vitamin D pathway components and cervical cancer–associated lncRNAs.
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Vitamin D pathway components and cervical cancer–associated lncRNAs.
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CYP24A1
[1]
216.92
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Vitamin D target (strongly upregulated)
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[3]
Cervical cancer poor prognosis lncRNA
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AC017020.2
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[3]
Immune-related lncRNA
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[0]
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[2]
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[3]
Immune/cervical-related lncRNA
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[0]
CRAT8/CRAT37/CRAT40
[1]
~1.08 to −1.12
[2]
~0.95–0.98
[3]
Cervical cancer-associated transcripts
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{"parent_value":95.27,"unit":"%","evidence":"Result: CatBoost performs the best overall, producing an accuracy of 95.27%.","target_result":"CatBoost accuracy","number_stated":true}
reading object · 6 fields
parent_value
95.27
unit
%
evidence
Result: CatBoost performs the best overall, producing an accuracy of 95.27%.
target_result
CatBoost accuracy
number_stated
true
source_locator
chars 1251-1327 of source sha256 8523103ae02f5638e06a46f25f22d70e94b06558034607267ce02bb85335fd26
recorded_at
2026-10-31T15:19:01.292363+00:00
[1] object · 8 fields
provider
minimax_alt
model
MiniMax-M3
prompt_version
replication-target-v2
prompt_hash
f7d48417a74896b065e9097ee2c62387974593488a5590087e26da6eded9690d
source_hash
8523103ae02f5638e06a46f25f22d70e94b06558034607267ce02bb85335fd26
raw_response
<think>We need answer JSON only. Need identify single numerical result replication registered target headline numerical result paper states main finding. Likely CatBoost accuracy 95.27%. Need exact contiguous quote containing number and attribution. Abstract: "Result: CatBoost performs the best overall, producing an accuracy of 95.27%." This is clear headline numerical result. Unit likely "%" or "percent"? Paper prints 95.27%. parent_value 95.27, unit "%" likely. Evidence exact contiguous quote. target_result perhaps "CatBoost accuracy" or sentence. Need ensure schema. They ask headline number only but schema includes target_result. Could state "CatBoost accuracy: 95.27%". Evidence exact clause. JSON valid. Potential ambiguity: headline main finding perhaps clinical 738 patients? Yet registered target "headline numerical result the paper states for its main finding" and abstract explicitly says CatBoost best overall, accuracy 95.27%, likely target. Need report paper stated value. Exact quote no ellipsis. Use "Result: CatBoost performs the best overall, producing an accuracy of 95.27%." Unit printed %. parent_value number 95.27. target_result perhaps "CatBoost accuracy". number_stated true. No extra properties. Ensure evidence exact quotation from source. It is exact. Could target result be "95.27%" and evidence. target_result string "CatBoost achieved the best overall performance, with an accuracy of 95.27%." But request headline number only maybe perhaps target_result should be "95.27%"? Yet parent_value already. "Report the headline number only: the value..." Likely target_result can identify. Maybe "CatBoost accuracy: 95.27%" still two representations. They may expect target_result perhaps statement. We need follow schema. Could set target_result "95.27%" but that's number only. Unit separate. Evidence full quote. Let's formulate concise. Need distinguish unit perhaps "percent" rather than "%" because paper prints "95.27%" and unit must be unit paper prints. Unit should "%". Good. JSON escaping.</think> {"parent_value":95.27,"unit":"%","evidence":"Result: CatBoost performs the best overall, producing an accuracy of 95.27%.","target_result":"95.27%","number_stated":true}
reading object · 6 fields
parent_value
95.27
unit
%
evidence
Result: CatBoost performs the best overall, producing an accuracy of 95.27%.
target_result
95.27%
number_stated
true
source_locator
chars 1251-1327 of source sha256 8523103ae02f5638e06a46f25f22d70e94b06558034607267ce02bb85335fd26
recorded_at
2026-10-31T15:19:04.372101+00:00
agreement
false
disagreements object · 3 fields
target_result object · 3 fields
minimax
CatBoost performed best overall, achieving an accuracy of 95.27%, an ROC AUC of 0.9930, a precision of 0.9296, and a recall of 0.9706.
minimax_alt
CatBoost performs the best overall, producing an accuracy of 95.27%.
decisive
false
analysis_methods object · 3 fields
minimax array · 10 items
[0]
CatBoost
[1]
LightGBM
[2]
Extra Trees
[3]
Logistic Regression
[4]
ANOVA F-test
[5]
mutual information
[6]
Chi-squared test
[7]
Recursive Feature Elimination (RFE)
[8]
differential gene-expression analysis
[9]
UMAP
minimax_alt array · 10 items
[0]
CatBoost
[1]
LightGBM
[2]
Extra Trees
[3]
Logistic Regression
[4]
ANOVA F-test
[5]
mutual information
[6]
Chi-squared test
[7]
Recursive Feature Elimination (RFE)
[8]
differential expression analysis
[9]
Uniform Manifold Approximation and Projection (UMAP)
decisive
false
statistical_method object · 3 fields
minimax
Model performance was evaluated using accuracy, precision, recall, F1-score, and ROC AUC metrics.
minimax_alt
CatBoost
decisive
true
agreement_basis object · 2 fields
policy
decisive_fields
decisive_fields array · 4 items
[0]
work_type
[1]
number_stated
[2]
parent_value
[3]
unit
blinded
true
human_critique object · 5 fields
status
not_run
reviewer
null
signed_at
null
conflict_checked
false
commentary
null
stages array · 7 items
[0] object · 4 fields
key
target_materials
status
pending
reason
null
evidence
null
[1] object · 4 fields
key
independent_readings
status
blocked
reason
The independent readings are incomplete or disagree; the source evidence must resolve the difference.
evidence object · 1 field
provider_errors array · 0 items
[2] object · 4 fields
key
source_resolution
status
pending
reason
null
evidence
null
[3] object · 4 fields
key
protocol_reconstruction
status
pending
reason
null
evidence
null
[4] object · 4 fields
key
control_execution
status
pending
reason
null
evidence
null
[5] object · 4 fields
key
control_comparison
status
pending
reason
null
evidence
null
[6] object · 4 fields
key
publication
status
pending
reason
null
evidence
null
gaps array · 2 items
[0]
review.human_critique
[1]
review.machine_critique.blocked
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 · 1 item
[0] object · 3 fields
id
package:c225dbecc9816adeb6a6763302f89ceed14c115e0bfa24828410529d1f9c4449
type
sep_package
label
SEP v1 package for pmid:40647578
activities array · 1 item
[0] object · 4 fields
id
activity:conversion:pmid:40647578
type
conversion
used array · 1 item
[0]
package:c225dbecc9816adeb6a6763302f89ceed14c115e0bfa24828410529d1f9c4449
agent
agent:corpus-assay-engine
agents array · 3 items
[0] object · 3 fields
id
agent:minimax
type
model
label
minimax
[1] object · 3 fields
id
agent:minimax_alt
type
model
label
minimax_alt
[2] object · 3 fields
id
agent:corpus-assay-engine
type
software
label
Corpus assay engine
source_run_id
run-20261023T122449232913
dsl_catalogue_fingerprint
343fb30756ba6ec3
capability_table_fingerprint
2cb3e42f047d2ecf
snapshot_id
ba34b49490d9bfaf
lineage object · 4 fields
parent_id
pmid:40647578
parent_hash
sha256:371dc0ddb398aa738d478ee3d5c1e51410fb60fbff1a215a48fd99aafccebae1
fork_of
null
gaps array · 13 items
[0]
abstract
[1]
asset:source:pmid:40647578 not verified
[2]
authors
[3]
capability
[4]
corpus_record_absent
[5]
execution.not_run
[6]
method
[7]
review.human_critique
[8]
review.machine_critique.blocked
[9]
step:obtain_source_data blocked on data:not_downloaded
[10]
step:run_analysis blocked on no owned computational capability is bound to this experiment
[11]
step:run_analysis input 'materials:the source data behind the published result in pmid:40647578' does not resolve to a declared asset
[12]
targets
Complete file
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Split Selection in Trees", "row_count": 5, "column_count": 2}, "table": {"label": "Table 2", "caption": "Feature importance score for clinical prediction (LightGBM output).", "rows": [["Feature", "Importance Score"], ["vitamin_d_level", "0.30"], ["vitamin_d_supplement_Yes", "0.25"], ["FIGO_STAGE", "0.20"], ["Symptoms", "0.15"]], "row_count": 5, "column_count": 2, "numeric_cells": 4, "header_rows": 1, "locator": "section[15]/table-wrap[1]#diagnostics-15-01579-t002"}}, {"id": "asset:table:pmid:40647578:3", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:182f332b895eafc61f9a3fdaacf073839b2855934718a9ac808c5f6af7cf04f7", "bytes": 475, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[17]/table-wrap[1]#diagnostics-15-01579-t003", "version": null, "note": "Different models’ classification reports.", "structure": {"pmcid": "PMC12248449", "section_index": 17, "section_title": "3.3.4. 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Logistic Regression", "row_count": 23, "column_count": 2}, "table": {"label": null, "caption": null, "rows": [["Algorithm 1. Mutual information-based CatBoost attribute selection and classification"], ["CatBoost attributes were selected and classified [49] well based on mutual information, and the model performed well, with high accuracy in this combination."], ["Step 1: Data preparation."], ["S={yk,zl}k=1 N", "(25)"], ["In the full dataset with N samples, each data point consists of"], ["y(k)=[y1k,y2k…ydk]∈Rd", "(26)"], ["Feature vector of sample k contains d."], ["y(k)∈Y (Target class)", "(27)"], ["Step 2: yl∈Y computes its mutual information with target Z."], ["MIyl,Z=∑yk∈Yk∑z∈ZPyk,z∗log(Pyk,zPyk·Pz)", "(28)"], ["yk—k th feature in the cervical cancer dataset"], ["Yk—Set of all possible values that feature yk can take"], ["z—Target variable, Z—Set of all possible target value"], ["Step 3: Feature Ranking and Selection."], ["Rank features by their mutual information score."], ["Rankyk=arg sort(MI(yk,z))", "(29)"], ["The top K features are selected."], ["Yselected=ykk∈Top K IndicesMIyk,z", "(30)"], ["Step 4: Model training with CatBoost."], ["Train a CatBoost classifier on selected features."], ["fYtraini=catboost classifier.fit(Ytraini,z)", "(31)"], ["Ytraini—Training data matrix consist of only top i selected feature"], ["z—target vector"]], "row_count": 23, "column_count": 2, "numeric_cells": 14, "header_rows": 0, "locator": "section[17]/table-wrap[2]#array1"}}, {"id": "asset:table:pmid:40647578:5", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:54ceb4fd618b81dab3c4511329366522cf3e587b2107b4a9f1277edb4ce9bd23", "bytes": 432, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[19]/table-wrap[1]#diagnostics-15-01579-t004", "version": null, "note": "Descriptive statistics of recurrence status vs. vitamin D.", "structure": {"pmcid": "PMC12248449", "section_index": 19, "section_title": "4.1. Clinical Correlation of Vitamin D with Recurrence", "row_count": 3, "column_count": 5}, "table": {"label": "Table 4", "caption": "Descriptive statistics of recurrence status vs. vitamin D.", "rows": [["Recurrence Status", "Mean Vitamin D (ng/mL)", "Median Vitamin D (ng/mL)", "Std. Dev.", "Sample Size (n)"], ["0 (No Recurrence)", "24.94", "25.04", "7.83", "638"], ["1 (Recurrence)", "14.73", "15.02", "7.78", "100"]], "row_count": 3, "column_count": 5, "numeric_cells": 10, "header_rows": 1, "locator": "section[19]/table-wrap[1]#diagnostics-15-01579-t004"}}, {"id": "asset:table:pmid:40647578:6", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:7bcf29aab78b25943a5f03c216e2837993882bb8adf0aeda288101a64b04ab6f", "bytes": 431, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[19]/table-wrap[2]#diagnostics-15-01579-t005", "version": null, "note": "T-test and Mann–Whitney U test interpretations.", "structure": {"pmcid": "PMC12248449", "section_index": 19, "section_title": "4.1. Clinical Correlation of Vitamin D with Recurrence", "row_count": 3, "column_count": 4}, "table": {"label": "Table 5", "caption": "T-test and Mann–Whitney U test interpretations.", "rows": [["Test", "Statistic", "p-Value", "Interpretation"], ["T-test", "t = 12.186", "<0.0001", "Significant difference (parametric)"], ["Mann–Whitney U test", "U = 52878.0", "<0.0001", "Significant difference (non-parametric)"]], "row_count": 3, "column_count": 4, "numeric_cells": 4, "header_rows": 1, "locator": "section[19]/table-wrap[2]#diagnostics-15-01579-t005"}}, {"id": "asset:table:pmid:40647578:7", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:28b47d8eb5120646d6a5f1536cd00c3df66d04cd6412292874db6a124ac215e2", "bytes": 877, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[20]/table-wrap[1]#diagnostics-15-01579-t006", "version": null, "note": "Vitamin D pathway components and cervical cancer–associated lncRNAs.", "structure": {"pmcid": "PMC12248449", "section_index": 20, "section_title": "4.2. Key Findings from the Dataset of GSE267715 That Strongly Support the Findings", "row_count": 10, "column_count": 4}, "table": {"label": "Table 6", "caption": "Vitamin D pathway components and cervical cancer–associated lncRNAs.", "rows": [["Gene Symbol", "Fold Change", "FDR", "Relevance"], ["CYP24A1", "216.92", "0.0093", "Vitamin D target (strongly upregulated)"], ["CYP27B1", "1.04", "0.9900", "Enzyme that activates vitamin D"], ["VDR", "−1.03", "0.9625", "Vitamin D receptor"], ["GC", "1.05", "0.9995", "Vitamin D-binding protein"], ["RP11-396F22.1", "−1.03", "0.9821", "Cervical cancer poor prognosis lncRNA"], ["AC017020.2", "+4.25", "0.9239", "Immune-related lncRNA"], ["MIR548AU", "−1.92", "~0.97", "Immune/cervical-related lncRNA"], ["CRAT8/CRAT37/CRAT40", "~1.08 to −1.12", "~0.95–0.98", "Cervical cancer-associated transcripts"], ["RP11-89H19.1", "+1.08", "0.9873", "Antisense to VDR gene"]], "row_count": 10, "column_count": 4, "numeric_cells": 25, "header_rows": 1, "locator": "section[20]/table-wrap[1]#diagnostics-15-01579-t006"}}, {"id": "asset:table:pmid:40647578:8", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:707c2af63455eda2c715fea0e6e394e2b98e0dc1d869053e02a63d21c8b9eaf6", "bytes": 457, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[20]/table-wrap[2]#diagnostics-15-01579-t007", "version": null, "note": "Top cervical cancer-related lncRNA.", "structure": {"pmcid": "PMC12248449", "section_index": 20, "section_title": "4.2. 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Need identify single numerical result replication registered target headline numerical result paper states main finding. Likely CatBoost accuracy 95.27%. Need exact contiguous quote containing number and attribution. Abstract: \"Result: CatBoost performs the best overall, producing an accuracy of 95.27%.\" This is clear headline numerical result. Unit likely \"%\" or \"percent\"? Paper prints 95.27%. parent_value 95.27, unit \"%\" likely. Evidence exact contiguous quote. target_result perhaps \"CatBoost accuracy\" or sentence. Need ensure schema. They ask headline number only but schema includes target_result. Could state \"CatBoost accuracy: 95.27%\". Evidence exact clause. JSON valid.\n\nPotential ambiguity: headline main finding perhaps clinical 738 patients? Yet registered target \"headline numerical result the paper states for its main finding\" and abstract explicitly says CatBoost best overall, accuracy 95.27%, likely target. Need report paper stated value. Exact quote no ellipsis. Use \"Result: CatBoost performs the best overall, producing an accuracy of 95.27%.\" Unit printed %. parent_value number 95.27. target_result perhaps \"CatBoost accuracy\". number_stated true. No extra properties. Ensure evidence exact quotation from source. It is exact.\n\nCould target result be \"95.27%\" and evidence. target_result string \"CatBoost achieved the best overall performance, with an accuracy of 95.27%.\" But request headline number only maybe perhaps target_result should be \"95.27%\"? Yet parent_value already. \"Report the headline number only: the value...\" Likely target_result can identify. Maybe \"CatBoost accuracy: 95.27%\" still two representations. They may expect target_result perhaps statement. We need follow schema. Could set target_result \"95.27%\" but that's number only. Unit separate. Evidence full quote. Let's formulate concise.\n\nNeed distinguish unit perhaps \"percent\" rather than \"%\" because paper prints \"95.27%\" and unit must be unit paper prints. Unit should \"%\". Good.\n\nJSON escaping.</think>\n\n{\"parent_value\":95.27,\"unit\":\"%\",\"evidence\":\"Result: CatBoost performs the best overall, producing an accuracy of 95.27%.\",\"target_result\":\"95.27%\",\"number_stated\":true}", "reading": {"parent_value": 95.27, "unit": "%", "evidence": "Result: CatBoost performs the best overall, producing an accuracy of 95.27%.", "target_result": "95.27%", "number_stated": true, "source_locator": "chars 1251-1327 of source sha256 8523103ae02f5638e06a46f25f22d70e94b06558034607267ce02bb85335fd26"}, "recorded_at": "2026-10-31T15:19:04.372101+00:00"}], "agreement": false, "disagreements": {"target_result": {"minimax": "CatBoost performed best overall, achieving an accuracy of 95.27%, an ROC AUC of 0.9930, a precision of 0.9296, and a recall of 0.9706.", "minimax_alt": "CatBoost performs the best overall, producing an accuracy of 95.27%.", "decisive": false}, "analysis_methods": {"minimax": ["CatBoost", "LightGBM", "Extra Trees", "Logistic Regression", "ANOVA F-test", "mutual information", "Chi-squared test", "Recursive Feature Elimination (RFE)", "differential gene-expression analysis", "UMAP"], "minimax_alt": ["CatBoost", "LightGBM", "Extra Trees", "Logistic Regression", "ANOVA F-test", "mutual information", "Chi-squared test", "Recursive Feature Elimination (RFE)", "differential expression analysis", "Uniform Manifold Approximation and Projection (UMAP)"], "decisive": false}, "statistical_method": {"minimax": "Model performance was evaluated using accuracy, precision, recall, F1-score, and ROC AUC metrics.", "minimax_alt": "CatBoost", "decisive": true}}, "agreement_basis": {"policy": "decisive_fields", "decisive_fields": ["work_type", "number_stated", "parent_value", "unit"]}, "blinded": true}, "human_critique": {"status": "not_run", "reviewer": null, "signed_at": null, "conflict_checked": false, "commentary": null}, "stages": [{"key": "target_materials", "status": "pending", "reason": null, "evidence": null}, {"key": "independent_readings", "status": "blocked", "reason": "The independent readings are incomplete or disagree; the source evidence must resolve the difference.", "evidence": {"provider_errors": []}}, {"key": "source_resolution", "status": "pending", "reason": null, "evidence": null}, {"key": "protocol_reconstruction", "status": "pending", "reason": null, "evidence": null}, {"key": "control_execution", "status": "pending", "reason": null, "evidence": null}, {"key": "control_comparison", "status": "pending", "reason": null, "evidence": null}, {"key": "publication", "status": "pending", "reason": null, "evidence": null}], "gaps": ["review.human_critique", "review.machine_critique.blocked"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "package:c225dbecc9816adeb6a6763302f89ceed14c115e0bfa24828410529d1f9c4449", "type": "sep_package", "label": "SEP v1 package for pmid:40647578"}], "activities": [{"id": "activity:conversion:pmid:40647578", "type": "conversion", "used": ["package:c225dbecc9816adeb6a6763302f89ceed14c115e0bfa24828410529d1f9c4449"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:minimax", "type": "model", "label": "minimax"}, {"id": "agent:minimax_alt", "type": "model", "label": "minimax_alt"}, {"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}], "source_run_id": "run-20261023T122449232913", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:40647578", "parent_hash": "sha256:371dc0ddb398aa738d478ee3d5c1e51410fb60fbff1a215a48fd99aafccebae1", "fork_of": null, "generation": 2}, "gaps": ["abstract", "asset:source:pmid:40647578 not verified", "authors", "capability", "corpus_record_absent", "execution.not_run", "method", "review.human_critique", "review.machine_critique.blocked", "step:obtain_source_data blocked on data:not_downloaded", "step:run_analysis blocked on no owned computational capability is bound to this experiment", "step:run_analysis input 'materials:the source data behind the published result in pmid:40647578' does not resolve to a declared asset", "targets"]}