Bayesian Network Inference Research Articles

A risk assessment model of a sewer pipeline in an underground utility tunnel based on a Bayesian network

A utility tunnel typically houses various types of urban lifelines. As one of the assembled pipelines in a utility tunnel, the sewer pipeline is of great importance for city life. For example, leakage of toxic and combustible gases and a subsequent explosion can result in severe accidents. The potential hazards in the sewer pipeline compartment of a utility tunnel are greatly different from those in traditional directly buried sewer pipelines. In this study, a risk assessment method based on a Bayesian network (BN) and Dempster-Shafer (D-S) Evidence theory was developed to evaluate complicated sewer pipeline accidents in a utility tunnel. First, potential hazards and typical accident scenarios were identified based on case studies of sewer pipeline accidents and evaluated by experts. Then, a BN-based risk assessment framework for the sewer pipeline in a utility tunnel was established. Using the proposed model, BN inferences of sewer pipeline accident scenarios were conducted. Furthermore, sensitivity analysis (SA) was conducted to identify the critical threats of the sewer pipeline in a utility tunnel. The proposed risk assessment framework can help to prevent and mitigate sewer pipeline accidents in utility tunnels.

Reliability analysis of mooring lines for floating structures using ANN-BN inference

The harsh marine environment is a significant threat to the safety of floating structure systems. To address this, mooring systems have seen widespread application as an important component in the stabilization of floating structures. This article proposes a methodology to assess the reliability of mooring lines under given extreme environmental conditions based on artificial neural network–Bayesian network inference. Different types of artificial neural networks, including radial basis function neural networks and back propagation neural networks, are adopted to predict the extreme response of mooring lines according to a series of measured environmental data. A failure database under extreme sea conditions is then established in accordance with the failure criterion of mooring systems. There is a failure of mooring lines when the maximum tension exceeds the allowable breaking strength. Finally, the reliability analysis of moored floating structures under different load directions is conducted using Bayesian networks. To demonstrate the proposed methodology, the failure probability of a sample semi-submersible platform at a water depth of 1500 m is estimated. This approach utilizes artificial neural networks’ capacity for calculation efficiency and validates artificial neural networks for the response prediction of floating structures. Furthermore, it can also be employed to estimate the failure probability of other complex floating structures.

On a Scalable Entropic Breaching of the Overfitting Barrier for Small Data Problems in Machine Learning.

Overfitting and treatment of small data are among the most challenging problems in machine learning (ML), when a relatively small data statistics size is not enough to provide a robust ML fit for a relatively large data feature dimension . Deploying a massively parallel ML analysis of generic classification problems for different and , we demonstrate the existence of statistically significant linear overfitting barriers for common ML methods. The results reveal that for a robust classification of bioinformatics-motivated generic problems with the long short-term memory deep learning classifier (LSTM), one needs in the best case a statistics that is at least 13.8 times larger than the feature dimension . We show that this overfitting barrier can be breached at a 10 fraction of the computational cost by means of the entropy-optimal scalable probabilistic approximations algorithm (eSPA), performing a joint solution of the entropy-optimal Bayesian network inference and feature space segmentation problems. Application of eSPA to experimental single cell RNA sequencing data exhibits a 30-fold classification performance boost when compared to standard bioinformatics tools and a 7-fold boost when compared to the deep learning LSTM classifier.

Algorithms for Bayesian network modeling and reliability inference of complex multistate systems: Part I – Independent systems

As the number of complex multistate systems’ components increases, one major challenge to analyze the reliabilities of complex multistate systems by Bayesian network (BN) is that the memory storage requirements (MSRs) of conditional probability table (CPT) increase exponentially. When the components reach a certain amount, the MSRs of CPT will exceed the computer's random access memory (RAM). To solve this problem, this two-part paper proposes a novel multistate compression algorithm to compress the CPT so that the MSRs of CPT can be reduced apparently. In this Part I, an independent multistate inference algorithm is proposed to perform the inference of BN based on the compressed CPT for the complex multistate independent systems. Given the evidence of system, the backward inference algorithm is proposed to update the probability distributions of compoents. The above proposed algorithms can be generally applied to any complex multistate independent system without constraints on system structure and state configurations. In addition, the Part II studies the application of compression idea in the complex multistate dependent systems. Finally, two case studies are used to validate the performance of the proposed algorithms.

Phylogenetics of Taxus Using the Internal Transcribed Spacers of Nuclear Ribosomal DNA and Plastid trnL-F Regions

Taxus is a genus of trees and shrubs with high value in horticulture and medicine as a source of the anticancer drug paclitaxel. The taxonomy of the group is complex due to the lack of diagnostic morphological characters and the high degree of similarity among species. Taxus has a wide global geographic distribution and some taxonomists recognize only a single species with geographically defined subgroups, whereas others have described several species. To address these differences in taxonomic circumscription, phylogenetic analyses were conducted on DNA sequences using Maximum Likelihood, Bayesian Inference and TCS haplotype networks on single and combined gene regions obtained for the nuclear ribosomal ITS region and the plastid trnL intron and trnL-F intergenic spacer. Evidence is presented for the sister group status of Pseudotaxus to Taxus and the inclusion of Amentotaxus, Austrotaxus, Cephalotaxus and Torreya within Taxaceae. Results are consistent with the taxonomic recognition of nine species: T. baccata, T. brevifolia, T. canadensis, T. cuspidata, T. floridana, T. fuana, T. globosa, T. sumatrana and T. wallichiana, but evidence is found for less species distinction and considerable reticulation within the T. baccata, T. canadensis and T. cuspidata group. We compare the results to known taxonomy, biogeography, present new leaf anatomical data and discuss the origins of the hybrids T. ×media and T. ×hunnewelliana.

Understanding stroke with Bayesian networks

Background: Stroke is a major source of morbidity worldwide, causing 5.78 million deaths per annum as per WHO global health estimates. An international effort is underway to improve outcomes in stroke by means of secondary and tertiary preventative measures. To maximise the efficacy of such interventions, we must fully understand the processes which lead to stroke-related morbidity and mortality. We propose to reframe stroke as a component of a network system, with multiple interacting causes and consequences. In real-world epidemiology, interactive systems are known to exist between social, behavioural and biological risk factors. The network paradigm accommodates such complexity well, and has demonstrated value in genetics, pathology and therapeutics. We propose Bayesian network inference as a hypothesis-free method of characterising the causal processes of stroke outcomes. Methods: We examine data recorded during the International Stroke trial, a multi-centre interventional trial evaluating the efficacy of anticoagulation and antiplatelet agents as secondary preventative agents in 19,000 cases of stroke. We extract 38 relevant variables, pertaining to patient demographics, stroke presentation, clinical features, diagnosis, management and outcomes. A discrete Bayesian network inferred by optimisation of network score. The performance of several network scores and search algorithms were compared using cross validation. This process identified TABU with K2 score as the optimal network search protocol. Bayesian Network bootstrapping was used to provide an estimate of network structural confidence. Results: Bayesian network inference detected 119 significant conditional dependencies in the International Stroke Trial dataset. These conditional dependencies were consistent with known clinical associations. 14-day mortality was found to be conditionally dependent on age at presentation (Mutual Info: P value Conclusions: We organise the pathogenesis, management and sequelae as a single functional system, in which clinical phenomena are understood to influence one another. We demonstrate the utility of the method to form and test multiple hypotheses in an objective fashion. This methodology is general and may theoretically be applied to various observational datasets across the health sciences.

An adaptive feature extraction model for classification of thyroid lesions in ultrasound images

The thyroid is the chief hormonal gland that controls the growth, metabolism, and maturation of the body. However, the function of the thyroid gland could be disrupted if it produces too much or too little hormones. Furthermore, there could be abnormal growth in thyroid cell tissue, leading to the formation of a benign or malignant thyroid lesion. Ultrasound is a typical non-invasive diagnosis approach to check for cancerous thyroid lesions. However, the visual interpretation of the ultrasound thyroid images is challenging and time-consuming. Hence, a feature engineering model is proposed to overcome these challenges. We propose to transform image pixel intensity values into high dimensional structured data set before fitting a Regression analysis framework to estimate kernel parameters for an image filter model. We then adopt a Bayesian network inference to estimate a subset for the textural features with a significant conditional dependency in the classification of thyroid lesions. The analysis of the proposed feature engineering model showed that the classification performance had an overall significant improvement over other image filter models. We achieve 96.00% classification accuracy with a sensitivity and specificity of 99.64% and 90.23% respectively for a filter size of 13 × 13. The analysis of results indicate that the diagnosis of ultrasound images thyroid nodules is significantly boosts by adaptively learning filter parameters for feature engineering model.

Balanced Quantum-Like Bayesian Networks

Empirical findings from cognitive psychology indicate that, in scenarios under high levels of uncertainty, many people tend to make irrational decisions. To address this problem, models based on quantum probability theory, such as the quantum-like Bayesian networks, have been proposed. However, this model makes use of a Bayes normalisation factor during probabilistic inference to convert the likelihoods that result from quantum interference effects into probability values. The interpretation of this operation is not clear and leads to extremely skewed intensity waves that make the task of prediction of these irrational decisions challenging. This article proposes the law of balance, a novel mathematical formalism for probabilistic inferences in quantum-like Bayesian networks, based on the notion of balanced intensity waves. The general idea is to balance the intensity waves resulting from quantum interference in such a way that, during Bayes normalisation, they cancel each other. With this representation, we also propose the law of maximum uncertainty, which is a method to predict these paradoxes by selecting the amplitudes of the wave with the highest entropy. Empirical results show that the law of balance together with the law of maximum uncertainty were able to accurately predict different experiments from cognitive psychology showing paradoxical or irrational decisions, namely in the Prisoner’s Dilemma game and the Two-Stage Gambling Game.

An Aging-Related Single-Nucleotide Polymorphism is Associated With Altered Clinical Outcomes and Distinct Inflammatory Profiles in Aged Blunt Trauma Patients.

The contribution of individual genetic determinants of aging to the adverse clinical outcomes and altered inflammation mediator networks characteristic of aged trauma patients is unknown. The AA genotype of the aging-related single-nucleotide polymorphism (SNP) rs2075650 in TOMM40 has been associated with longevity, while the AG and GG genotypes are associated with an increased risk of Alzheimer disease. Here, we studied the effect of rs2075650 on clinical outcomes and dynamic biomarker patterns after traumatic injury. Genomic DNA was obtained from blunt trauma patients admitted to the ICU and examined for 551,839 SNPs using an Illumina microarray kit. Plasma was sampled from each patient three times within the first 24 h and daily from day 1 to 7 then assayed for 31 biomarkers using Luminex. Aged patients (65-90 years) were segregated into AA (n = 77) and AG/GG (n = 17) genotypes. Additional comparisons were made with matched groups of young patients (18-30 years), controlling for injury severity score (ISS) and sex ratio, and also segregated into AA (n = 56) and AG/GG (n = 19) genotypes. Aged patients with the AA genotype had a significantly lower requirement for ventilation and fewer days on mechanical ventilation, as well as significantly higher levels of one mediator and lower levels of two mediators. Dynamic Bayesian Network inference revealed IL-23 as a central node in each network regardless of age or genotype, with MIG and IP-10 also as key mediators in the networks of the aged patients. These findings suggest that an aging-related SNP, rs2075650, may influence clinical outcomes and inflammation networks in aged patients following blunt trauma, and thus may serve as a predictive outcome biomarker in the setting of polytrauma.

An incremental explanation of inference in Bayesian networks for increasing model trustworthiness and supporting clinical decision making.

Various AI models are increasingly being considered as part of clinical decision-support tools. However, the trustworthiness of such models is rarely considered. Clinicians are more likely to use a model if they can understand and trust its predictions. Key to this is if its underlying reasoning can be explained. A Bayesian network (BN) model has the advantage that it is not a black-box and its reasoning can be explained. In this paper, we propose an incremental explanation of inference that can be applied to 'hybrid' BNs, i.e. those that contain both discrete and continuous nodes. The key questions that we answer are: (1) which important evidence supports or contradicts the prediction, and (2) through which intermediate variables does the information flow. The explanation is illustrated using a real clinical case study. A small evaluation study is also conducted.

Phylogeography and demographic history of Gyrodactylus konovalovi (Monogenoidea: Gyrodactylidae), an ectoparasite on the East Asia Amur minnow (Cyprinidae) in Central China.

Gyrodactylus konovalovi is an ectoparasite on the Amur minnow (Rhynchocypris lagowskii) that is widely distributed in the cold fresh waters of East Asia. In the present study, the phylogeography and demographic history of G. konovalovi and the distribution of its host in the Qinling Mountains are examined. A total of 79 individual parasites was sequenced for a 528 bp region of the mitochondrial NADH dehydrogenase subunit 5 (ND5) gene, and 25 haplotypes were obtained. The substitution rate (dN/dS) was 0.068 and indicated purifying selection. Haplotype diversity (h) and nucleotide diversity (π) varied widely in the Qinling Mountains. Phylogenetic trees based on Bayesian inference (BI), maximum likelihood (ML), and maximum parsimony (MP) methods and network analysis revealed that all haplotypes were consistently well‐supported in three different lineages, indicating a significant geographic distribution pattern. There was a significant positive correlation between genetic differentiation (F st) and geographic distance. The results of mismatch distribution, neutrality test and Bayesian skyline plot analyses showed that whole populations underwent population contraction during the Pleistocene. Based on the molecular clock calibration, the most common ancestor was estimated to have emerged in the middle Pleistocene. Our study suggests for the first time that a clearly phylogeography of G. konovalovi was shaped by geological events and climate fluctuations, such as orogenesis, drainage capture changes, and vicariance, during the Pleistocene in the Qinling Mountains.

Vehicular Delay Tolerant Network Routing Algorithm Based on Bayesian Network

Delay Tolerant Networks (DTNs) are novel wireless mobile networks, which suffer from frequent disruption, high latency, and the lack of a complete path from source to destination. Vehicular Delay Tolerant Network (VDTN) is a special type of DTNs with vehicles as nodes. In VDTN, most nodes have specific movement patterns, however, traditional routing algorithms in DTNs do not take this characteristic into considerations very well. In this paper, a new routing algorithm based on Bayesian Network (BN) is proposed to construct the prediction model, which intends to predict the movement patterns of nodes in the real VDTN scenarios. Firstly, a comprehensive BN model is established, where more attributes of nodes are selected to improve the accuracy of the model prediction. Then, considering the complexity of the structure learning problem of BN, a novel structure learning algorithm, K2 algorithm based on Genetic Algorithm (K2-GA), is proposed to search the optimal BN structure efficiently. At last, Junction Tree Algorithm (JTA) is adopted in the inference of BN, which can accelerate the inference process through variable elimination and calculation sharing for large scale BN. The simulation results show that the proposed VDTN routing algorithm based on the BN model can improve the delivery ratio with a minor forwarding overhead.

High-Dimensional Bayesian Network Inference From Systems Genetics Data Using Genetic Node Ordering.

Studying the impact of genetic variation on gene regulatory networks is essential to understand the biological mechanisms by which genetic variation causes variation in phenotypes. Bayesian networks provide an elegant statistical approach for multi-trait genetic mapping and modelling causal trait relationships. However, inferring Bayesian gene networks from high-dimensional genetics and genomics data is challenging, because the number of possible networks scales super-exponentially with the number of nodes, and the computational cost of conventional Bayesian network inference methods quickly becomes prohibitive. We propose an alternative method to infer high-quality Bayesian gene networks that easily scales to thousands of genes. Our method first reconstructs a node ordering by conducting pairwise causal inference tests between genes, which then allows to infer a Bayesian network via a series of independent variable selection problems, one for each gene. We demonstrate using simulated and real systems genetics data that this results in a Bayesian network with equal, and sometimes better, likelihood than the conventional methods, while having a significantly higher overlap with groundtruth networks and being orders of magnitude faster. Moreover our method allows for a unified false discovery rate control across genes and individual edges, and thus a rigorous and easily interpretable way for tuning the sparsity level of the inferred network. Bayesian network inference using pairwise node ordering is a highly efficient approach for reconstructing gene regulatory networks when prior information for the inclusion of edges exists or can be inferred from the available data.

ShinyBN: an online application for interactive Bayesian network inference and visualization

BackgroundHigh-throughput technologies have brought tremendous changes to biological domains, and the resulting high-dimensional data has also posed enormous challenges to computational science. A Bayesian network is a probabilistic graphical model represented by a directed acyclic graph, which provides concise semantics to describe the relationship between entities and has an independence assumption that is suitable for sparse omics data. Bayesian networks have been broadly used in biomedical research fields, including disease risk assessment and prognostic prediction. However, the inference and visualization of Bayesian networks are unfriendly to the users lacking programming skills.ResultsWe developed an R/Shiny application, shinyBN, which is an online graphical user interface to facilitate the inference and visualization of Bayesian networks. shinyBN supports multiple types of input and provides flexible settings for network rendering and inference. For output, users can download network plots, prediction results and external validation results in publication-ready high-resolution figures.ConclusionOur user-friendly application (shinyBN) provides users with an easy method for Bayesian network modeling, inference and visualization via mouse clicks. shinyBN can be used in the R environment or online and is compatible with three major operating systems, including Windows, Linux and Mac OS. shinyBN is deployed at https://jiajin.shinyapps.io/shinyBN/. Source codes and the manual are freely available at https://github.com/JiajinChen/shinyBN.

Graph-Based Metamodeling for Characterizing Cold Metal Transfer Process Performance

Abstract Achieving predictable, reliable, and cost-effective operations in wire and arc additive manufacturing is a key concern during production of complex-shaped functional metallic components for demanding applications, such as those found in aerospace and automotive industries. A metamodel combining localized submodels of the different physical phenomena during welding can ensure stable material deposition. Such a metamodel would necessarily combine submodels from multiple domains, such as materials science, thermomechanical engineering, and process planning, and it would provide a holistic systems perspective of the modeled process. An approach using causal graph-based modeling and Bayesian networks is proposed to develop a metamodel for a test case using wire and arc additive manufacturing with cold metal transfer. The developed modeling approach is used to characterize the effect of manufacturing variables on product dimensional quality in the form of a causal graph. A quantitative simulation using Bayesian networks is applied to the causal graph to enable process parameter tuning. The Bayesian network inference mechanism predicts the effects of the parameters on results, whereas, conversely, with known targets, it can predict the required parameter values. Validation of the developed Bayesian network model is performed using experimental tests.

Tuxnet: a simple interface to process RNA sequencing data and infer gene regulatory networks.

Predicting gene regulatory networks (GRNs) from expression profiles is a common approach for identifying important biological regulators. Despite the increased use of inference methods, existing computational approaches often do not integrate RNA-sequencing data analysis, are not automated or are restricted to users with bioinformatics backgrounds. To address these limitations, we developed tuxnet, a user-friendly platform that can process raw RNA-sequencing data from any organism with an existing reference genome using a modified tuxedo pipeline (hisat2+cufflinks package) and infer GRNs from these processed data. tuxnet is implemented as a graphical user interface and can mine gene regulations, either by applying a dynamic Bayesian network(DBN) inference algorithm, genist, or a regression tree-based pipeline, rtp-star. We obtained time-course expression data of a PERIANTHIA (PAN) inducible line and inferred a GRN using genist to illustrate the use of tuxnet while gaining insight into the regulations downstream of the Arabidopsis root stem cell regulator PAN. Using rtp-star, we inferred the network of ATHB13, a downstream gene of PAN, for which we obtained wild-type and mutant expression profiles. Additionally, we generated two networks using temporal data from developmental leaf data and spatial data from root cell-type data to highlight the use of tuxnet to form new testable hypotheses from previously explored data. Our case studies feature the versatility of tuxnet when using different types of gene expression data to infer networks and its accessibility as a pipeline for non-bioinformaticians to analyze transcriptome data, predict causal regulations, assess network topology and identify key regulators.

Diurnal Variation in Systemic Acute Inflammation and Clinical Outcomes Following Severe Blunt Trauma.

Animal studies suggest that the time of day is a determinant of the immunological response to both injury and infection. We hypothesized that due to this diurnal variation, time of injury could affect the systemic inflammatory response and outcomes post-trauma and tested this hypothesis by examining the dynamics of circulating inflammatory mediators in blunt trauma patients injured during daytime vs. nighttime. From a cohort of 472 blunt trauma survivors, two stringently matched sub-cohorts of moderately/severely injured patients [injury severity score (ISS) >20] were identified. Fifteen propensity-matched, daytime-inured (“mDay”) patients (age 43.6 ± 5.2, M/F 11/4, ISS 22.9 ± 0.7) presented during the shortest local annual period (8:00 am−5:00 pm), and 15 propensity-matched “mNight” patients (age 43 ± 4.3, M/F 11/4, ISS 24.5 ± 2.5) presented during the shortest night period (10:00 pm−5:00 am). Serial blood samples were obtained (3 samples within the first 24 h and daily from days 1–7) from all patients. Thirty-two plasma inflammatory mediators were assayed. Two-way Analysis of Variance (ANOVA) was used to compare groups. Dynamic Network Analysis (DyNA) and Dynamic Bayesian Network (DyBN) inference were utilized to infer dynamic interrelationships among inflammatory mediators. Both total hospital and intensive care unit length of stay were significantly prolonged in the mNight group. Circulating IL-17A was elevated significantly in the mNight group from 24 h to 7 days post-injury. Circulating MIP-1α, IL-7, IL-15, GM-CSF, and sST2 were elevated in the mDay group. DyNA demonstrated elevated network complexity in the mNight vs. the mDay group. DyBN suggested that cortisol and sST2 were central nodes upstream of TGF-β1, chemokines, and Th17/protective mediators in both groups, with IL-6 being an additional downstream node in the mNight group only. Our results suggest that time of injury affects clinical outcomes in severely injured patients in a manner associated with an altered systemic inflammation program, possibly implying a role for diurnal or circadian variation in the response to traumatic injury.

Bayesian Networks for Data Integration in the Absence of Foreign Keys

In the era of open data, a single data source rarely contains all of the attributes we need for inference in specific applications. For example, a marketing department may aim to integrate retailer-specific purchase data with separate demographic data for purposes of targeted advertising – a capability not possible with either dataset alone. In this work, we address two key desiderata of an automated framework for probabilistic data integration over multiple data sources: (1) we require that each relational data source share at least one attribute with another relational data source, but we do not require these attributes to be foreign keys (e.g., attributes such as gender, age, and postal code are not foreign keys because they do not uniquely identify individuals in a data source) and (2) we require inference to be probabilistic to reflect inherent uncertainty in population-level predictions given the absence of foreign keys. While some frameworks such as Probabilistic Relational Models (PRMs) address point (2), they do not address point (1) since they rely on foreign keys to link tables. To achieve both desiderata simultaneously, we develop an automated framework to construct Bayesian networks for data integration capable of answering any probabilistic query spanning the attributes of multiple relational data sources. We demonstrate that our framework is able to closely approximate the inference of a global Bayesian network over a single relation that has been projected onto multiple local relations and further investigate properties of local relations such as the number of shared attributes and their cardinality to understand how these properties affect the quality of inference.

Identification of an ancestral haplotype in the mitochondrial phylogeny of the ovine haplogroup B.

BackgroundEuropean mouflon (Ovis orientalis musimon) has been reintroduced in mainland Europe since the 18th-century sourcing from the Sardinian and Corsican autochthonous mouflon populations. The European mouflon is currently considered the feral descendent of the Asian mouflon (O. orientalis), and the result of first wave of sheep domestication occurred 11,000 years ago in the Fertile Crescent, and brought to Corsica and Sardinia ca. 6,000 years ago, where they still live as autochthonous populations. However, this phylogeny is based on mitogenome sequences of European mouflon individuals exclusively.MethodsWe sequenced the first complete mtDNA of the long-time isolated Sardinian mouflon and compared it with several ovine homologous sequences, including mouflon from mainland Europe and samples representative of the five known mitochondrial domestic sheep haplogroups. We applied Bayesian inference, Maximum Likelihood and Integer Neighbour-Joining network methods and provided a robust, fully-resolved phylogeny with strong statistical support for all nodes.ResultsWe identified an early split (110,000 years ago) of the Sardinian mouflon haplotype from both sheep and mainland European mouflon belonging to haplogroup B, the latter two sharing a more recent common ancestor (80,000 years ago). Further, the Sardinian mouflon sequence we generated had the largest genetic distance from domestic sheep haplogroups (0.0136 ± 0.004) among mouflon species. Our results suggest the Sardinian mouflon haplotype as the most ancestral in the HPG-B lineage, hence partially redrawing the known phylogeny of the genus Ovis.

Decoding the secreted inflammatory response of primary human hepatocytes to hypoxic stress in vitro

The cellular and molecular response of liver cells to hypoxic stress is not fully understood. We used computational modeling to gain insights into the inflammatory response of primary human hepatocytes (HC) to hypoxic stress in vitro. Primary HC from cancer patients were exposed to hypoxia (1% O2) or normoxia (21% O2) for 1-48 h, and the cell supernatants were assayed for 21 inflammatory mediators. Data were analyzed by Two-Way ANOVA, Dynamic Bayesian Network (DBN) inference, Dynamic Network Analysis (DyNA), and Time-interval Principal Component Analysis (TI-PCA). The chemokines MCP-1/CCL2 and IP-10/CXCL10, along with the cytokines interleukin (IL)-2 and IL-15 were altered significantly over time in hypoxic vs. normoxic HC. DBN inference suggested central, coordinating roles for MCP-1 and IL-8 in regulating a largely conserved inflammatory program in both hypoxic and normoxic HC. DyNA likewise suggested similar network trajectories of decreasing complexity over time in both hypoxic and normoxic HC, though with differential connectivity of MCP-1, IP-10, IL-8, and Eotaxin. TI-PCA pointed to IL-1β as a central characteristic of inflammation in hypoxic HC across all time intervals, along with IL-15 and IL-10, vs. Eotaxin, IL-7, IL-10, IL-15, and IL-17A in normoxic HC. Thus, diverse human HC appear to respond in a largely conserved fashion to cell culture stress, with distinct characteristics based on the presence or absence of hypoxia.