Integrated Learning: Screening Optimal Biomarkers for Identifying Preeclampsia in Placental mRNA Samples

Guo, Rong; Teng, Zhaogang; Wang, Yiding; Zhou, Xin; Xu, Heze; Liú, Dan

doi:10.1155/2021/6691096

Cited by 4 publications

(3 citation statements)

References 36 publications

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“…Their ML-based approach allowed them to select a subset of 13 mRNA features: HTRA4, PROCR, MYCN, ERO1A, EAF1, PPP1R16B, CRH, FLNB, PIK3CB, PLAAT3, FBN2, RFLNB, and TKT, which were combined and tested for PE and control subjects classification by ML. Their model, which fused three ML classifiers, C4.5, AdaBoost and multilayer perceptron, yielded an accuracy of 82.2% (53). A different case is the study of Bodnar et al, who aimed to assess the effect of fruit and vegetable intake and dietary synergy on the risk of various adverse pregnancy outcomes.…”

Section: Preeclampsiamentioning

confidence: 99%

Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications

Mennickent,

Rodríguez,

Opazo

et al. 2023

Front. Endocrinol.

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IntroductionMachine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to find hidden correlations and accomplish accurate predictions regarding different conditions. ML has been successfully used to solve varied problems in different areas of science, such as psychology, economics, biology and chemistry. Therefore, we wondered how far it has penetrated into the field of obstetrics and gynecology.AimTo describe the state of art regarding the use of ML in the context of pregnancy diseases and complications.MethodologyPublications were searched in PubMed, Web of Science and Google Scholar. Seven subjects of interest were considered: gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations.Current stateML has been widely applied in all the included subjects. Its uses are varied, the most common being the prediction of perinatal disorders. Other ML applications include (but are not restricted to) biomarker discovery, risk estimation, correlation assessment, pharmacological treatment prediction, drug screening, data acquisition and data extraction. Most of the reviewed articles were published in the last five years. The most employed ML methods in the field are non-linear. Except for logistic regression, linear methods are rarely used.Future challengesTo improve data recording, storage and update in medical and research settings from different realities. To develop more accurate and understandable ML models using data from cutting-edge instruments. To carry out validation and impact analysis studies of currently existing high-accuracy ML models.ConclusionThe use of ML in pregnancy diseases and complications is quite recent, and has increased over the last few years. The applications are varied and point not only to the diagnosis, but also to the management, treatment, and pathophysiological understanding of perinatal alterations. Facing the challenges that come with working with different types of data, the handling of increasingly large amounts of information, the development of emerging technologies, and the need of translational studies, it is expected that the use of ML continue growing in the field of obstetrics and gynecology.

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Section: Preeclampsiamentioning

confidence: 99%

Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications

Mennickent,

Rodríguez,

Opazo

et al. 2023

Front. Endocrinol.

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“…We next compared DEGs between all four studies to identify common dysregulation. This step was performed to replace the stringent quality filtering commonly used in individual RNA-seq studies, which are: adjusted P values (P values adjusted for multiple testing of the individual genes) greater than 0.05 [44], and a log 2 fold change (log 2 FC) greater than 1 or less than À1 [50,[63][64][65].…”

Section: Screening For Biomarker Candidates Using Multiple Rna-seq St...mentioning

confidence: 99%

Biomarker screening in preeclampsia: an RNA-sequencing approach based on data from multiple studies

Rezk¹,

Grasegger²,

Brandstetter³

et al. 2022

Journal of Hypertension

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Objective:Biomarkers have become important in the prognosis and diagnosis of various diseases. High-throughput methods, such as RNA sequencing facilitate the detection of differentially expressed genes (DEGs), hence potential biomarker candidates. Individual studies suggest long lists of DEGs, hampering the identification of clinically relevant ones. Concerning preeclampsia – a major obstetric burden with high risk for adverse maternal and/or neonatal outcomes – limitations in diagnosis and prediction are still important issues. We, therefore, developed a workflow to facilitate the screening for biomarkers.Methods:On the basis of the tool DESeq2, a comprehensive workflow for identifying DEGs was established, analyzing data from several publicly available RNA-sequencing studies. We applied it to four RNA-sequencing datasets (one blood, three placenta) analyzing patients with preeclampsia and normotensive controls. We compared our results with other published approaches and evaluated their performance.Results:We identified 110 genes that are dysregulated in preeclampsia, observed in at least three of the studies analyzed, six even in all four studies. These included FLT-1, TREM-1, and FN1, which either represent established biomarkers at protein level, or promising candidates based on recent studies. For comparison, using a published meta-analysis approach, 5240 DEGs were obtained.Conclusion:This study presents a data analysis workflow for preeclampsia biomarker screening, capable of identifying promising biomarker candidates, while drastically reducing the numbers of candidates. Moreover, we were also able to confirm its performance for heart failure. This approach can be applied to additional diseases for biomarker identification, and the set of DEGs identified in preeclampsia represents a resource for further studies.

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“…In the absence of heterogeneous and multimodal data such as placental mRNA Samples [ 42 ], Uterine artery Doppler measurements [ 5 , 43 – 45 ], medications [ 10 ], and images [ 46 ] to predict PE, the contributions of this paper are multi-fold as stated below. First, using the chi-square feature selection method, we identify the significant clinical and demographic attributes associated with developing PE among women from AA and NA populations as well as general populations using large datasets.…”

Section: Introductionmentioning

confidence: 99%

An imbalance-aware deep neural network for early prediction of preeclampsia

et al. 2022

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Preeclampsia (PE) is a hypertensive complication affecting 8-10% of US pregnancies annually. While there is no cure for PE, aspirin may reduce complications for those at high risk for PE. Furthermore, PE disproportionately affects racial minorities, with a higher burden of morbidity and mortality. Previous studies have shown early prediction of PE would allow for prevention. We approached the prediction of PE using a new method based on a cost-sensitive deep neural network (CSDNN) by considering the severe imbalance and sparse nature of the data, as well as racial disparities. We validated our model using large extant rich data sources that represent a diverse cohort of minority populations in the US. These include Texas Public Use Data Files (PUDF), Oklahoma PUDF, and the Magee Obstetric Medical and Infant (MOMI) databases. We identified the most influential clinical and demographic features (predictor variables) relevant to PE for both general populations and smaller racial groups. We also investigated the effectiveness of multiple network architectures using three hyperparameter optimization algorithms: Bayesian optimization, Hyperband, and random search. Our proposed models equipped with focal loss function yield superior and reliable prediction performance compared with the state-of-the-art techniques with an average area under the curve (AUC) of 66.3% and 63.5% for the Texas and Oklahoma PUDF respectively, while the CSDNN model with weighted cross-entropy loss function outperforms with an AUC of 76.5% for the MOMI data. Furthermore, our CSDNN model equipped with focal loss function leads to an AUC of 66.7% for Texas African American and 57.1% for Native American. The best results are obtained with 62.3% AUC with CSDNN with weighted cross-entropy loss function for Oklahoma African American, 58% AUC with DNN and balanced batch for Oklahoma Native American, and 72.4% AUC using either CSDNN with weighted cross-entropy loss function or CSDNN with focal loss with balanced batch method for MOMI African American dataset. Our results provide the first evidence of the predictive power of clinical databases for PE prediction among minority populations.

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Integrated Learning: Screening Optimal Biomarkers for Identifying Preeclampsia in Placental mRNA Samples

Cited by 4 publications

References 36 publications

Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications

Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications

Biomarker screening in preeclampsia: an RNA-sequencing approach based on data from multiple studies

An imbalance-aware deep neural network for early prediction of preeclampsia

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