@Article{biocell.2023.027373,
AUTHOR = {WEI LIANG, ZONGWEI ZHANG, KEJU YANG, HONGTU HU, QIANG LUO, ANKANG YANG, LI CHANG, YUANYUAN ZENG},
TITLE = {Genetic algorithm-optimized backpropagation neural network establishes a diagnostic prediction model for diabetic nephropathy: Combined machine learning and experimental validation in mice},
JOURNAL = {BIOCELL},
VOLUME = {47},
YEAR = {2023},
NUMBER = {6},
PAGES = {1253--1263},
URL = {http://www.techscience.com/biocell/v47n6/52791},
ISSN = {1667-5746},
ABSTRACT = {<b>Background:</b> Diabetic nephropathy (DN) is the most common complication of type 2 diabetes mellitus and the
main cause of end-stage renal disease worldwide. Diagnostic biomarkers may allow early diagnosis and treatment of DN
to reduce the prevalence and delay the development of DN. Kidney biopsy is the gold standard for diagnosing DN;
however, its invasive character is its primary limitation. The machine learning approach provides a non-invasive and
specific criterion for diagnosing DN, although traditional machine learning algorithms need to be improved to
enhance diagnostic performance. <b>Methods:</b> We applied high-throughput RNA sequencing to obtain the genes related
to DN tubular tissues and normal tubular tissues of mice. Then machine learning algorithms, random forest, LASSO
logistic regression, and principal component analysis were used to identify key genes (CES1G, CYP4A14, NDUFA4,
ABCC4, ACE). Then, the genetic algorithm-optimized backpropagation neural network (GA-BPNN) was used to
improve the DN diagnostic model. <b>Results:</b> The AUC value of the GA-BPNN model in the training dataset was 0.83,
and the AUC value of the model in the validation dataset was 0.81, while the AUC values of the SVM model in the
training dataset and external validation dataset were 0.756 and 0.650, respectively. Thus, this GA-BPNN gave better
values than the traditional SVM model. This diagnosis model may aim for personalized diagnosis and treatment of
patients with DN. Immunohistochemical staining further confirmed that the tissue and cell expression of NADH
dehydrogenase (ubiquinone) 1 alpha subcomplex, 4-like 2 (NDUFA4L2) in tubular tissue in DN mice were decreased.
<b>Conclusion:</b> The GA-BPNN model has better accuracy than the traditional SVM model and may provide an effective
tool for diagnosing DN.},
DOI = {10.32604/biocell.2023.027373}
}