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Background
Approaching epidemiological data with flexible machine learning algorithms is of great value for understanding disease-specific association patterns. However, it can be difficult to correctly extract and understand those patterns due to the lack of model interpretability.
Method
We here propose a machine learning workflow that combines random forests with Bayesian network surrogate models to allow for a deeper level of interpretation of complex association patterns. We first evaluate the proposed workflow on synthetic data. We then apply it to data from the large population-based Study of Health in Pomerania (SHIP). Based on this combination, we discover and interpret broad patterns of individual serum TSH concentrations, an important marker of thyroid functionality.
Results
Evaluations using simulated data show that feature associations can be correctly recovered by combining random forests and Bayesian networks. The presented model achieves predictive accuracy that is similar to state-of-the-art models (root mean square error of 0.66, mean absolute error of 0.55, coefficient of determination of R2 = 0.15). We identify 62 relevant features from the final random forest model, ranging from general health variables over dietary and genetic factors to physiological, hematological and hemostasis parameters. The Bayesian network model is used to put these features into context and make the black-box random forest model more understandable.
Conclusion
We demonstrate that the combination of random forest and Bayesian network analysis is helpful to reveal and interpret broad association patterns of individual TSH concentrations. The discovered patterns are in line with state-of-the-art literature. They may be useful for future thyroid research and improved dosing of therapeutics.
Objective: In the rat, the pancreatic islet transplantation model is an established method to induce hepatocellular carcinomas (HCC), due to insulin-mediated metabolic and molecular alterations like increased glycolysis and de novo lipogenesis and the oncogenic AKT/mTOR pathway including upregulation of the transcription factor Carbohydrate-response element-binding protein (ChREBP). ChREBP could therefore represent an essential oncogenic co-factor during hormonally induced hepatocarcinogenesis. Methods: Pancreatic islet transplantation was implemented in diabetic C57Bl/6J (wild type, WT) and ChREBP-knockout (KO) mice for 6 and 12 months. Liver tissue was examined using histology, immunohistochemistry, electron microscopy and Western blot analysis. Finally, we performed NGS-based transcriptome analysis between WT and KO liver tumor tissues. Results: Three hepatocellular carcinomas were detectable after 6 and 12 months in diabetic transplanted WT mice, but only one in a KO mouse after 12 months. Pre-neoplastic clear cell foci (CCF) were also present in liver acini downstream of the islets in WT and KO mice. In KO tumors, glycolysis, de novo lipogenesis and AKT/mTOR signalling were strongly downregulated compared to WT lesions. Extrafocal liver tissue of diabetic, transplanted KO mice revealed less glycogen storage and proliferative activity than WT mice. From transcriptome analysis, we identified a set of transcripts pertaining to metabolic, oncogenic and immunogenic pathways that are differentially expressed between tumors of WT and KO mice. Of 315 metabolism-associated genes, we observed 199 genes that displayed upregulation in the tumor of WT mice, whereas 116 transcripts showed their downregulated expression in KO mice tumor. Conclusions: The pancreatic islet transplantation model is a suitable method to study hormonally induced hepatocarcinogenesis also in mice, allowing combination with gene knockout models. Our data indicate that deletion of ChREBP delays insulin-induced hepatocarcinogenesis, suggesting a combined oncogenic and lipogenic function of ChREBP along AKT/mTOR-mediated proliferation of hepatocytes and induction of hepatocellular carcinoma.