XGBoost-SHAP Interpretable Modeling Identifies and Validates an Eight-Gene Biomarker for Hepatic Encephalopathy Risk Prediction in Cirrhosis.

Code ↔ Paper

18 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 18 matches

[1] § 4. Materials and Methods › 4.6. Construction and Validation of XGBoost–SHAP Models ↔ r.07_xgboost.R, lines 29–136 · score 0.89 · colsample_bytree, XGBoost regression, cross validation, squarederror, subsampling, depth
[2] § 4. Materials and Methods › 4.2. Differential Expression Analysis ↔ r.01_DEG.R, lines 15–100 · score 0.86 · contrasts.fit, eBayes, lmFit, linear model, limma, volcano
[3] § 2. Results › 2.1. Workflow of the Study ↔ r.02_Mfuzz.R, lines 15–56 · score 0.79 · chronic liver failure, eCLD, decompensated cirrhosis, ACLF, acute, CC
[4] § 2. Results › 2.1. Workflow of the Study ↔ r.11_PCA.R, lines 17–63 · score 0.78 · eCLD, chronic liver failure, decompensated cirrhosis, ACLF, CC, healthy
[5] § 4. Materials and Methods › 4.5. Machine Learning-Based Marker Gene Selection ↔ r.06_RF.R, lines 17–75 · score 0.74 · randomForest, IncMSE, IncNodePurity, regression, modeling, gene
[6] § 4. Materials and Methods › 4.8. Gene Set Enrichment Analysis (GSEA) ↔ r.10_GSEA.R, lines 68–119 · score 0.74 · gradient permutation, minSize, maxSize, Spearman, NES, thresholds
[7] § 2. Results › 2.5. Multi-Algorithm Cross-Validated Screening for HE-Specific Marker Genes ↔ r.06_RF.R, lines 17–75 · score 0.70 · IncMSE, IncNodePurity, random forest, genes
[8] § 4. Materials and Methods › 4.3. Functional Enrichment Analysis ↔ r.03_GOKEGG.R, lines 39–92 · score 0.69 · enrichGO, enrichKEGG, bitr, BH, enrichment, gene
[9] § 4. Materials and Methods › 4.6. Construction and Validation of XGBoost–SHAP Models ↔ r.12_ROC.R, lines 150–213 · score 0.64 · fusion weights, Risk scores, w1, w2, AUC, models
[10] § 4. Materials and Methods › 4.8. Gene Set Enrichment Analysis (GSEA) ↔ R/fgsea.R, lines 316–376 · score 0.63 · minSize, maxSize, fgsea, BH, NES, permutation
[11] § 4. Materials and Methods › 4.7. Structural Equation Modeling and Mediation Effect Analysis ↔ r.09_SEM.R, lines 305–351 · score 0.60 · TUBA1C, mediation, mediated, bootstrap, indirect, SEM
[12] § 2. Results › 2.4. Identification of Prognosis-Associated DEGs ↔ r.01_DEG.R, lines 102–148 · score 0.57 · good prognosis, poor prognosis, limma, DEGs, cirrhotic, GSE15654
[13] § 4. Materials and Methods › 4.7. Structural Equation Modeling and Mediation Effect Analysis ↔ r.09_SEM.R, lines 70–93 · score 0.56 · ComBat, batch correction, SEM, Modeling
[14] § 4. Materials and Methods › 4.6. Construction and Validation of XGBoost–SHAP Models ↔ r.08_model_validation.R, lines 141–200 · score 0.56 · KM survival, model validation, probability, median, risk
[15] § 4. Materials and Methods › 4.1. Data Acquisition and Cohort Construction ↔ r.11_PCA.R, lines 17–63 · score 0.56 · eCLD, ACLF, DC, CC, healthy, clustering
[16] § 2. Results › 2.5. Multi-Algorithm Cross-Validated Screening for HE-Specific Marker Genes ↔ r.04_LASSO.R, lines 122–164 · score 0.55 · partial likelihood deviance, cross validation, optimal, LASSO
[17] § 2. Results › 2.6. Dual-Model Construction Based on XGBoost–SHAP and Multi-Level Clinical Validation ↔ packaging.R, lines 1–47 · score 0.54 · SHapley, exPlanations, Additive, XGBoost, model
[18] § 2. Results › 2.7. Structural Equation Modeling-Based Analyses of Characteristic Gene Regulatory Networks ↔ r.09_SEM.R, lines 305–351 · score 0.51 · TUBA1C, mediated, indirect, SEM, mediation, LRRC32

Paper

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The authors' code

R · 484 lines · 17 KB · MIT · 3 matches

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It can be read at the source: r.09_SEM.R.

Overview

Authors: Yuanfeng Lan^1,2,3,4, Tian Zhao^1,2,3, Ying Xu⁴, Haihong Ye^1,2,3

ORCID iDs: Yuanfeng Lan, Haihong Ye

Department of Medical Genetics and Developmental Biology, School of Basic Medical Sciences, Capital Medical University, Beijing 100069, China; (Y.L.); (T.Z.)
Laboratory for Clinical Medicine, Capital Medical University, Beijing 100069, China
Beijing Key Laboratory of Cell and Gene Therapy in Otology, Beijing 100069, China
Department of Human Cell Biology and Genetics, SUSTech Homeostatic Medicine Institute, School of Medicine, Southern University of Science and Technology, Shenzhen 518055, China

Institutions: Capital Medical University (China); Southern University of Science and Technology (China)

Journal: International journal of molecular sciences, volume 27, issue 15, article 6925

Dates: received 1 July 2026; accepted 30 July 2026; published online 1 August 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.3390/ijms27156925 · PMID 42589578 · PMCID PMC13467542 · OpenAlex W7172247900

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: genetics / omics (modality), human (organism), other condition (population)

Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing

Keywords: cirrhosis, hepatic encephalopathy, machine learning, prediction model, prognostic analysis

MeSH: Biomarkers*, Hepatic Encephalopathy*, Liver Cirrhosis*, Boosting Machine Learning Algorithms, Gene Expression Profiling, Humans, Prognosis, Transcriptome (* major topic)

Topic: Liver Disease and Transplantation (Hepatology, Medicine), according to OpenAlex

Funding: Beijing Municipal Natural Science Foundation (7262004)

Citations: not cited yet (Europe PMC); 39 references in the paper

Abstract

Cirrhosis, accounting for 2.4% of global mortality in 2019, represents a leading cause of death in chronic liver disease. Hepatic encephalopathy (HE), a decompensated complication of cirrhosis, is associated with a median survival of only 0.92 years post-diagnosis. Current screening methods relying on neuropsychological tests (e.g., Psychometric Hepatic Encephalopathy Score, PHES) have limitations such as time-consuming procedures and subjective interpretation, potentially delaying diagnosis. To address this, we integrated four cirrhotic transcriptomic cohorts (GSE41919, GSE57193, GSE139602, and GSE15654) and employed an integrated algorithm (LASSO [Least Absolute Shrinkage and Selection Operator]–RFE [Recursive Feature Elimination]–random forest) to identify HE-specific biomarker genes. Ultimately, we developed an HE risk-prediction system centered on eight HE-specific marker genes, namely, PRB2, TUBA1C, NPC2, LRRC32, TLN1, SOX9, SERPINA3 and RNASE4. Based on these genes, an XGBoost (eXtreme Gradient Boosting)-based HE risk stratification model was constructed, and SHAP (SHapley Additive exPlanations) analysis was further introduced to address the “black-box” limitation of conventional machine learning models and to improve the interpretability. The finalized eight-gene system enables accurate, efficient, and interpretable HE risk assessment in patients with cirrhosis. Functional characterization through gene set enrichment analysis and structural equation modeling further revealed that these marker genes converge on four interconnected biological processes, namely, metabolic homeostasis, synaptic and neural transmission, immune inflammatory signaling, and hepatic detoxification, which collectively reflect the gut–liver–brain axis disruption central to HE pathogenesis. This dual-model system, incorporating both cirrhosis progression and survival prognosis, provides a reliable and clinically applicable tool for early HE risk warning and stratification, reducing the limitations of traditional neuropsychological screening and offering a translational foundation for timely intervention and prognostic optimization in high-risk cirrhotic patients.

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 18 matches between paragraphs and lines of code.

seandavi/GEOquery

License: MIT

State: the link answers, verified on 26 September 2026

Evidence: files inventoried

Commit: cb12423bf2691af82f742fc10f891835e8bab3ce, 17 August 2026

Languages: R (53), Quarto (6)

Size: 154 files, 59 scripts

Software Heritage: not archived

Found in: the text, “4.1. Data Acquisition and Cohort Construction”

Holds: README, license file, CITATION.cff, environment (DESCRIPTION, Dockerfile), tests, continuous integration, documentation, 6 notebooks

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Availability: 1 check, the latest on 26 September 2026: the link answers

26 September 2026: the link answers

62 files, not copied: shown from their source

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At the source: github.com/seandavi/GEOquery

ModelOriented/shapviz

License: GPL-2.0

State: the link answers, verified on 26 September 2026

Evidence: files inventoried

Commit: a3ead2491c66b7345eaf23e212eb1edda03b4697, 31 August 2026

Languages: R (28)

Size: 101 files, 28 scripts

Software Heritage: not archived

Found in: the text, “4.6. Construction and Validation of XGBoost–SHAP”

Holds: README, license file, environment (DESCRIPTION), tests, continuous integration, documentation, 4 notebooks

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Availability: 1 check, the latest on 26 September 2026: the link answers

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At the source: github.com/ModelOriented/shapviz

alserglab/fgsea

License: MIT

State: the link answers, verified on 26 September 2026

Evidence: files inventoried

Commit: 570f5903545835cb340e699fcdfdf444b1ba07ec, 22 September 2026

Languages: R (33), C++ (10), C/C++ (8)

Size: 107 files, 51 scripts

Software Heritage: archived

Found in: the text, “4.8. Gene Set Enrichment Analysis (GSEA)”

Holds: README, license file, environment (DESCRIPTION), tests, continuous integration, documentation, 2 notebooks

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Availability: 1 check, the latest on 26 September 2026: the link answers

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At the source: github.com/alserglab/fgsea

yuanfeng-lan/HE-risk-Prediction

License: MIT

State: the link answers, verified on 26 September 2026

Evidence: files inventoried

Commit: b1fc48c5cbe5977c65ab0cb4a0b9cccdeb51e2d4, 24 July 2026

Languages: R (13)

Size: 17 files, 13 scripts

Software Heritage: not archived

Found in: the text, “4.9. Statistical Analysis and Data Visualization”

Holds: README, environment (renv.lock)

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

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Availability: 1 check, the latest on 26 September 2026: the link answers

26 September 2026: the link answers

14 files, not copied: shown from their source

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r.00_rawdata.R — R, 108 lines, shown from its source
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r.02_Mfuzz.R — R, 241 lines, 1 match, shown from its source
r.03_GOKEGG.R — R, 218 lines, 1 match, shown from its source
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At the source: github.com/yuanfeng-lan/HE-risk-Prediction

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:

4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
151 scripts, each with its path and the digest of its content;
18 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:GSE41919 — at NCBI GEO; found in the text, “2.1. Workflow of the Study”

Data Availability Statement

All data utilized in this study were obtained from the Gene Expression Omnibus (GEO) database, a publicly accessible repository maintained by the National Center for Biotechnology Information (NCBI). All datasets are freely available for download without any access restrictions. The specific GEO accession numbers and corresponding datasets are cited within the manuscript.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 5 keywords, 8 MeSH terms, 1 funder, 38 references.

Cite

This paper

Lan, Y., Zhao, T., Xu, Y., & Ye, H. (2026). XGBoost-SHAP Interpretable Modeling Identifies and Validates an Eight-Gene Biomarker for Hepatic Encephalopathy Risk Prediction in Cirrhosis. International journal of molecular sciences, 27(15), 6925. https://doi.org/10.3390/ijms27156925

BibTeX

@article{lan2026xgboost, author = {Lan, Yuanfeng and Zhao, Tian and Xu, Ying and Ye, Haihong}, title = {{XGBoost-SHAP Interpretable Modeling Identifies and Validates an Eight-Gene Biomarker for Hepatic Encephalopathy Risk Prediction in Cirrhosis}}, journal = {International journal of molecular sciences}, year = {2026}, month = aug, volume = {27}, number = {15}, pages = {6925}, publisher = {Multidisciplinary Digital Publishing Institute (MDPI)}, issn = {1422-0067}, doi = {10.3390/ijms27156925}, url = {https://doi.org/10.3390/ijms27156925}, pmid = {42589578}, pmcid = {PMC13467542} }

RIS

TY - JOUR AU - Lan, Yuanfeng AU - Zhao, Tian AU - Xu, Ying AU - Ye, Haihong TI - XGBoost-SHAP Interpretable Modeling Identifies and Validates an Eight-Gene Biomarker for Hepatic Encephalopathy Risk Prediction in Cirrhosis T2 - International journal of molecular sciences J2 - Int J Mol Sci PY - 2026 DA - 2026/08/01 VL - 27 IS - 15 SP - 6925 SN - 1422-0067 PB - Multidisciplinary Digital Publishing Institute (MDPI) DO - 10.3390/ijms27156925 UR - https://doi.org/10.3390/ijms27156925 LA - en ER -

CSL-JSON

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

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[6] doi:10.1016/j.xcrm.2026.102766 [code]: A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma.
Journal: Cell reports. Medicine
In common: pROC, SingleCellExperiment, limma, 10 other tools, genetics / omics, other condition
[7] doi:10.1093/neuonc/noag128 [code]: Spatially-resolved single-cell imaging of melanoma brain metastases identifies localized immune patterns predictive of immune checkpoint blockade response.
Journal: Neuro-oncology
In common: pROC, glmnet, survival, 8 other tools, genetics / omics, other condition
[8] doi:10.1016/j.cell.2026.05.026 [code]: The critical role of the endogenous immune compartment after CAR T cell therapy in recurrent GBM.
Journal: Cell
In common: survival, SingleCellExperiment, limma, 8 other tools, genetics / omics, other condition, 1 reference
[9] doi:10.1038/s41593-026-02367-0 [code]: A reproducible three-dimensional model of human brain tissue to investigate physiological and disease-associated microglia phenotypes.
Journal: Nature neuroscience
In common: SingleCellExperiment, limma, clusterProfiler, 9 other tools, 1 reference
[10] doi:10.1038/s41598-026-51501-2 [code]: Expanding canonical cortical cell type markers in the era of single-cell transcriptomics.
Journal: Scientific reports
In common: glmnet, SingleCellExperiment, clusterProfiler, 8 other tools, genetics / omics, 1 reference

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XGBoost-SHAP Interpretable Modeling Identifies and Validates an Eight-Gene Biomarker for Hepatic Encephalopathy Risk Prediction in Cirrhosis.

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seandavi/GEOquery

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yuanfeng-lan/HE-risk-Prediction

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