zhang, Z.; xu, D.; Akakuru, O.; xu, W.; zhang, Y. Artificial Neural Network Analysis of Geo Database in Diagnosing Papillary Thyroid Carcinoma. Preprints2021, 2021030324. https://doi.org/10.20944/preprints202103.0324.v1
APA Style
zhang, Z., xu, D., Akakuru, O., xu, W., & zhang, Y. (2021). Artificial Neural Network Analysis of Geo Database in Diagnosing Papillary Thyroid Carcinoma. Preprints. https://doi.org/10.20944/preprints202103.0324.v1
Chicago/Turabian Style
zhang, Z., wenjing xu and yewei zhang. 2021 "Artificial Neural Network Analysis of Geo Database in Diagnosing Papillary Thyroid Carcinoma" Preprints. https://doi.org/10.20944/preprints202103.0324.v1
Abstract
The diagnosis of papillary thyroid carcinoma has always been a concerned and challenging issue and it is very important and meaningful to have a definite diagnosis before the operation. In this study, we tried to use an artificial intelligence algorithm instead of medical statistics to analyze the genetic fingerprint from gene chip results to identify papillary thyroid carcinoma. We trained 20 artificial neural network models with differential genes and other important genes related to the cell metabolic cycle as the list of input features, and apply them to the diagnosis of papillary thyroid cancer in the independent validation data set. The results showed that when we used the DEGs and all genes lists as input features the models got the best diagnostic performance with AUC=98.97% and 99.37% and the accuracy were both 96%. This study revealed that the proposed artificial neural network models constructed with genetic fingerprints could achieve a prediction of papillary thyroid carcinoma. Such models can support clinicians to make more accurate clinical diagnoses. At the same time, it provides a novel idea for the application of artificial intelligence in clinical medicine.
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
