Dataset Information

Learning from imbalanced fetal outcomes of systemic lupus erythematosus in artificial neural networks.

ABSTRACT:

Objective

To explore an effective algorithm based on artificial neural network to pick correctly the minority of pregnant women with SLE suffering fetal loss outcomes from the majority with live birth and train a well behaved model as a clinical decision assistant.

Methods

We integrated the thoughts of comparative and focused study into the artificial neural network and presented an effective algorithm aiming at imbalanced learning in small dataset.

Results

We collected 469 non-trivial pregnant patients with SLE, where 420 had live-birth outcomes and the other 49 patients ended in fetal loss. A well trained imbalanced-learning model had a high sensitivity of 19/21 ([Formula: see text]) for the identification of patients with fetal loss outcomes.

Discussion

The misprediction of the two patients was explainable. Algorithm improvements in artificial neural network framework enhanced the identification in imbalanced learning problems and the external validation increased the reliability of algorithm.

Conclusion

The well-trained model was fully qualified to assist healthcare providers to make timely and accurate decisions.

SUBMITTER: Ma JH

PROVIDER: S-EPMC8042715 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Publications

Learning from imbalanced fetal outcomes of systemic lupus erythematosus in artificial neural networks.

Ma Jing-Hang JH Feng Zhen Z Wu Jia-Yue JY Zhang Yu Y Di Wen W

BMC medical informatics and decision making 20210413 1

<h4>Objective</h4>To explore an effective algorithm based on artificial neural network to pick correctly the minority of pregnant women with SLE suffering fetal loss outcomes from the majority with live birth and train a well behaved model as a clinical decision assistant.<h4>Methods</h4>We integrated the thoughts of comparative and focused study into the artificial neural network and presented an effective algorithm aiming at imbalanced learning in small dataset.<h4>Results</h4>We collected 469 ...[more]

PMID: 33845834

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Project description:BackgroundChildhood systemic lupus erythematosus (cSLE) is a multisystemic, life-threatening autoimmune disease. Compared to adults, SLE in childhood is more active, can cause multisystem involvement including renal, neurological and hematological, and can cause cumulative damage across systems more rapidly. Autophagy, one of the core functions of cells, is involved in almost every process of the immune response and has been shown to be associated with many autoimmune diseases, being a key factor in the interplay between innate and adaptive immunity. Autophagy influences the onset, progression and severity of SLE. This paper identifies new biomarkers for the diagnosis and treatment of childhood SLE based on an artificial neural network of autophagy-related genes.MethodsWe downloaded dataset GSE100163 from the Gene Expression Omnibus database and used Protein-protein Interaction Network (PPI) and Least Absolute Shrinkage and Selection Operator (LASSO) to screen the signature genes of autophagy-related genes in cSLE. A new artificial neural network model for cSLE diagnosis was constructed using the signature genes. The predictive efficiency of the model was also validated using the dataset GSE65391. Finally, "CIBERSORT" was used to calculate the infiltration of immune cells in cSLE and to analyze the relationship between the signature genes and the infiltration of immune cells.ResultsWe identified 37 autophagy-related genes that differed in cSLE and normal samples, and finally obtained the seven most relevant signature genes for cSLE (DDIT3, GNB2L1, CTSD, HSPA8, ULK1, DNAJB1, CANX) by PPI and LASOO regression screening, and constructed an artificial neural network diagnostic model for cSLE. Using this model, we plotted the ROC curves for the training and validation group diagnoses with the area under the curve of 0.976 and 0.783, respectively. Finally, we performed immunoassays on cSLE samples, and the results showed that Plasma cells, Macrophages M0, Dendritic cells activated and Neutrophils were significantly infiltrated in cSLE.ConclusionWe constructed an artificial neural network diagnostic model of seven autophagy-related genes that can be used for the diagnosis of cSLE. Meanwhile, the characteristic genes affect the immune infiltration of cSLE, which may provide new perspectives for the exploration of cSLE treatment and related mechanisms.

Dataset Information

Learning from imbalanced fetal outcomes of systemic lupus erythematosus in artificial neural networks.

Objective

Methods

Results

Discussion

Conclusion

Publications

Learning from imbalanced fetal outcomes of systemic lupus erythematosus in artificial neural networks.

Similar Datasets

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