A cost-effective machine learning-based method for preeclampsia risk assessment and driver genes discovery Article Swipe
Hao Wang
,
Zhao‐Yue Zhang
,
Haicheng Li
,
Jinzhao Li
,
Hanshuang Li
,
Mingzhu Liu
,
Pengfei Liang
,
Qilemuge Xi
,
Yongqiang Xing
,
Lei Yang
,
Yongchun Zuo
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1186/s13578-023-00991-y
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1186/s13578-023-00991-y
Background The placenta, as a unique exchange organ between mother and fetus, is essential for successful human pregnancy and fetal health. Preeclampsia (PE) caused by placental dysfunction contributes to both maternal and infant morbidity and mortality. Accurate identification of PE patients plays a vital role in the formulation of treatment plans. However, the traditional clinical methods of PE have a high misdiagnosis rate. Results Here, we first designed a computational biology method that used single-cell transcriptome (scRNA-seq) of healthy pregnancy (38 wk) and early-onset PE (28–32 wk) to identify pathological cell subpopulations and predict PE risk. Based on machine learning methods and feature selection techniques, we observed that the Tuning ReliefF (TURF) score hybrid with XGBoost (TURF_XGB) achieved optimal performance, with 92.61% accuracy and 92.46% recall for classifying nine cell subpopulations of healthy placentas. Biological landscapes of placenta heterogeneity could be mapped by the 110 marker genes screened by TURF_XGB, which revealed the superiority of the TURF feature mining. Moreover, we processed the PE dataset with LASSO to obtain 497 biomarkers. Integration analysis of the above two gene sets revealed that dendritic cells were closely associated with early-onset PE, and C1QB and C1QC might drive preeclampsia by mediating inflammation. In addition, an ensemble model-based risk stratification card was developed to classify preeclampsia patients, and its area under the receiver operating characteristic curve (AUC) could reach 0.99. For broader accessibility, we designed an accessible online web server ( http://bioinfor.imu.edu.cn/placenta ). Conclusion Single-cell transcriptome-based preeclampsia risk assessment using an ensemble machine learning framework is a valuable asset for clinical decision-making. C1QB and C1QC may be involved in the development and progression of early-onset PE by affecting the complement and coagulation cascades pathway that mediate inflammation, which has important implications for better understanding the pathogenesis of PE.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s13578-023-00991-y
- https://cellandbioscience.biomedcentral.com/counter/pdf/10.1186/s13578-023-00991-y
- OA Status
- gold
- Cited By
- 46
- References
- 54
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4322616457
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4322616457Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1186/s13578-023-00991-yDigital Object Identifier
- Title
-
A cost-effective machine learning-based method for preeclampsia risk assessment and driver genes discoveryWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-02-28Full publication date if available
- Authors
-
Hao Wang, Zhao‐Yue Zhang, Haicheng Li, Jinzhao Li, Hanshuang Li, Mingzhu Liu, Pengfei Liang, Qilemuge Xi, Yongqiang Xing, Lei Yang, Yongchun ZuoList of authors in order
- Landing page
-
https://doi.org/10.1186/s13578-023-00991-yPublisher landing page
- PDF URL
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https://cellandbioscience.biomedcentral.com/counter/pdf/10.1186/s13578-023-00991-yDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://cellandbioscience.biomedcentral.com/counter/pdf/10.1186/s13578-023-00991-yDirect OA link when available
- Concepts
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Preeclampsia, Transcriptome, Placenta, Pregnancy, Bioinformatics, Receiver operating characteristic, Computational biology, Fetus, Biology, Machine learning, Artificial intelligence, Medicine, Computer science, Gene, Genetics, Gene expressionTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
46Total citation count in OpenAlex
- Citations by year (recent)
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2025: 18, 2024: 19, 2023: 8Per-year citation counts (last 5 years)
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54Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.characteristic | 221 |
| abstract_inverted_index.identification | 38 |
| abstract_inverted_index.stratification | 206 |
| abstract_inverted_index.subpopulations | 92, 131 |
| abstract_inverted_index.decision-making. | 258 |
| abstract_inverted_index.transcriptome-based | 242 |
| abstract_inverted_index.http://bioinfor.imu.edu.cn/placenta | 238 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 99 |
| corresponding_author_ids | https://openalex.org/A5057225835, https://openalex.org/A5101018628 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 11 |
| corresponding_institution_ids | https://openalex.org/I156144747, https://openalex.org/I31637741 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.8100000023841858 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.99523845 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |