From heterogeneous healthcare data to disease-specific biomarker networks: A hierarchical Bayesian network approach

Becker, Ann‐Kristin; Dörr, Marcus; Felix, Stephan B.; Frost, Fabian; Grabe, Hans J.; Lerch, Markus M.; Nauck, Matthias; Völker, Uwe; Völzke, Henry; Kaderali, Lars

doi:10.1371/journal.pcbi.1008735

Cited by 13 publications

(9 citation statements)

References 41 publications

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“…Their graphical representation allows for intuitive interpretation, also for non-experts. Based on the methodology described in an earlier study [ 28 ], we train a BN, including only those features that were identified as relevant in the RF model. To reduce the network’s complexity, we aggregated highly collinear features to represent them as one single node in the network.…”

Section: Resultsmentioning

confidence: 99%

Analysis of epidemiological association patterns of serum thyrotropin by combining random forests and Bayesian networks

et al. 2022

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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.

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Section: Resultsmentioning

confidence: 99%

Analysis of epidemiological association patterns of serum thyrotropin by combining random forests and Bayesian networks

et al. 2022

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“…Since the BN in our case is defined over low dimensional representations of groups of variables, we call the structure Modular Bayesian Network (MBN). Notably, a MBN is a special instance of a hierarchical BN over a structured input domain [63][64][65][66].…”

Section: Modular Bayesian Networkmentioning

confidence: 99%

AI reveals insights into link between CD33 and cognitive impairment in Alzheimer’s Disease

Raschka

Sood

Schultz

et al. 2022

Preprint

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Modeling biological mechanisms is a key for disease understanding and drug-target identification. However, formulating quantitative models in the field of Alzheimer’s Disease is challenged by a lack of detailed knowledge of relevant biochemical processes. Additionally, fitting differential equation systems usually requires time resolved data and the possibility to perform intervention experiments, which is difficult in neurological disorders. This work addresses these challenges by employing the recently published Variational Autoencoder Modular Bayesian Networks (VAMBN) method, which we here trained on combined clinical and patient level gene expression data while incorporating a disease specific knowledge graph. Our approach, called iVAMBN, resulted in a quantitative model that allowed us to simulate a down-expression of the putative drug target CD33, including potential impact on cognitive impairment and brain pathophysiology. Experimental validation demonstrated a high overlap of molecular mechanism predicted to be altered by CD33 perturbation with cell line data. Altogether, our modeling approach may help to select promising drug targets.

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“…10,11 The proposed framework may also be useful for the novel and active research area of in-silico clinical trials, 12 for which generation of realistic virtual patients is a key consideration. While not in the context of the subject area explored in this study, Bayesian techniques have been widely used in biomedical engineering for network modelling, 13 uncertainty quantification, 14,15 parameter estimation and inference, [16][17][18][19][20][21][22][23][24][25] optimisation, 26 and optimal design. 27 This article is organised as follows.…”

Section: Introductionmentioning

confidence: 99%

A physiologically realistic virtual patient database for the study of arterial haemodynamics

Jones

Parr

Nithiarasu

et al. 2021

Numer Methods Biomed Eng

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This study creates a physiologically realistic virtual patient database (VPD), representing the human arterial system, for the primary purpose of studying the effects of arterial disease on haemodynamics. A low dimensional representation of an anatomically detailed arterial network is outlined, and a physiologically realistic posterior distribution for its parameters constructed through the use of a Bayesian approach. This approach combines both physiological/geometrical constraints and the available measurements reported in the literature. A key contribution of this work is to present a framework for including all such available information for the creation of virtual patients (VPs). The Markov Chain Monte Carlo (MCMC) method is used to sample random VPs from this posterior distribution, and the pressure and flow‐rate profiles associated with each VP computed through a physics based model of pulse wave propagation. This combination of the arterial network parameters (representing a virtual patient) and the haemodynamics waveforms of pressure and flow‐rates at various locations (representing functional response and potential measurements that can be acquired in the virtual patient) makes up the VPD. While 75,000 VPs are sampled from the posterior distribution, 10,000 are discarded as the initial burn‐in period of the MCMC sampler. A further 12,857 VPs are subsequently removed due to the presence of negative average flow‐rate, reducing the VPD to 52,143. Due to undesirable behaviour observed in some VPs—asymmetric under‐ and over‐damped pressure and flow‐rate profiles in left and right sides of the arterial system—a filter is proposed to remove VPs showing such behaviour. Post application of the filter, the VPD has 28,868 subjects. It is shown that the methodology is appropriate by comparing the VPD statistics to those reported in literature across real populations. Generally, a good agreement between the two is found while respecting physiological/geometrical constraints.

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From heterogeneous healthcare data to disease-specific biomarker networks: A hierarchical Bayesian network approach

Cited by 13 publications

References 41 publications

Analysis of epidemiological association patterns of serum thyrotropin by combining random forests and Bayesian networks

Analysis of epidemiological association patterns of serum thyrotropin by combining random forests and Bayesian networks

AI reveals insights into link between CD33 and cognitive impairment in Alzheimer’s Disease

A physiologically realistic virtual patient database for the study of arterial haemodynamics

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