Prognostic significance of acute kidney injury stage 1B in hospitalized patients with cirrhosis: A US nationwide study.

Understanding the prognostic significance of acute kidney injury (AKI) stage 1B [serum creatinine (sCr) ≥1.5 mg/dL] compared with stage 1A (sCr < 1.5 mg/dL) in a US population is important as it can impact initial management decisions for AKI in hospitalized cirrhosis patients. Therefore, we aimed to define outcomes associated with stage 1B in a nationwide US cohort of hospitalized cirrhosis patients with AKI. Hospitalized cirrhosis patients with AKI in the Cerner-Health-Facts database from January 2009 to September 2017 (n = 6250) were assessed for AKI stage 1 (≥1.5-2-fold increase in sCr from baseline) and were followed for 90 days for outcomes. The primary outcome was 90-day mortality; secondary outcomes were in-hospital AKI progression and AKI recovery. Competing-risk multivariable analysis was performed to determine the independent association between stage 1B, 90-day mortality (liver transplant as a competing risk), and AKI recovery (death/liver transplant as a competing risk). Multivariable logistic regression analysis was performed to determine the independent association between stage 1B and AKI progression. In all, 4654 patients with stage 1 were analyzed: 1A (44.3%) and 1B (55.7%). Stage 1B patients had a significantly higher cumulative incidence of 90-day mortality compared with stage 1A patients, 27.2% versus 19.7% ( p < 0.001). In multivariable competing-risk analysis, patients with stage 1B (vs. 1A) had a higher risk for mortality at 90 days [sHR 1.52 (95% CI 1.20-1.92), p = 0.001] and decreased probability for AKI recovery [sHR 0.76 (95% CI 0.69-0.83), p < 0.001]. Furthermore, in multivariable logistic regression analysis, AKI stage 1B (vs. 1A) was independently associated with AKI progression, OR 1.42 (95% CI 1.14-1.72) ( p < 0.001). AKI stage 1B patients have a significantly higher risk for 90-day mortality, AKI progression, and reduced probability of AKI recovery compared with AKI stage 1A patients. These results could guide initial management decisions for AKI in hospitalized patients with cirrhosis.

Acute kidney disease is common and associated with poor outcomes in patients with cirrhosis and acute kidney injury.

BACKGROUND & AIMS: Acute kidney disease (AKD) is the persistence of acute kidney injury (AKI) for up to 3 months, which is proposed to be the time-window where critical interventions can be initiated to alter downstream outcomes of AKI. In cirrhosis, AKD and its impact on outcomes have been scantly investigated. We aimed to define the incidence and outcomes associated with AKD in a nationwide US cohort of hospitalized patients with cirrhosis and AKI. METHODS: Hospitalized patients with cirrhosis and AKI in the Cerner-Health-Facts database from 1/2009-09/2017 (n = 6,250) were assessed for AKD and were followed-up for 180 days. AKI and AKD were defined based on KDIGO and ADQI AKD and renal recovery consensus criteria, respectively. The primary outcome measure was mortality, and the secondary outcome measure was de novo chronic kidney disease (CKD). Competing-risk multivariable models were used to determine the independent association of AKD with primary and secondary outcomes. RESULTS: AKD developed in 32% of our cohort. On multivariable competing-risk analysis adjusting for significant confounders, patients with AKD had higher risk of mortality at 90 (subdistribution hazard ratio [sHR] 1.37; 95% CI 1.14-1.66; p = 0.001) and 180 (sHR 1.37; 95% CI 1.14-1.64; p = 0.001) days. The incidence of de novo CKD was 37.5%: patients with AKD had higher rates of de novo CKD (64.0%) compared to patients without AKD (30.7%; p <0.001). After adjusting for confounders, AKD was independently associated with de novo CKD (sHR 2.52; 95% CI 2.01-3.15; p <0.001) on multivariable competing-risk analysis. CONCLUSIONS: AKD develops in 1 in 3 hospitalized patients with cirrhosis and AKI and it is associated with worse survival and de novo CKD. Interventions that target AKD may improve outcomes of patients with cirrhosis and AKI. LAY SUMMARY: In a nationwide US cohort of hospitalized patients with cirrhosis and acute kidney injury, acute kidney disease developed in 1 in 3 patients and was associated with worse survival and chronic kidney disease. Interventions that target acute kidney disease may improve outcomes of patients with cirrhosis and acute kidney injury.

Practice patterns and outcomes associated with intravenous albumin in patients with cirrhosis and acute kidney injury.

BACKGROUND & AIMS: Guidelines recommend albumin as the plasma-expander of choice for acute kidney injury (AKI) in cirrhosis. However, the impact of these recommendations on patient outcomes remains unclear. We aimed to determine the practice-patterns and outcomes associated with albumin use in a large, nationwide-US cohort of hospitalized cirrhotics with AKI. METHODS: A retrospective cohort study was performed in hospitalized cirrhotics with AKI using Cerner-Health-Facts database from January 2009 to March 2018. 6786 were included for analysis on albumin-practice-patterns, and 4126 had available outcomes data. Propensity-score-adjusted model was used to determine the association between albumin use, AKI-recovery and in-hospital survival. RESULTS: Median age was 61-years (60% male, 70% white), median serum-creatinine was 1.8 mg/dL and median Model for End-stage Liver Disease Sodium (MELD-Na) score was 24. Albumin was given to 35% of patients, of which 50% received albumin within 48-hours of AKI-onset, and 17% received appropriate weight-based dosing. Albumin was used more frequently in patients with advanced complications of cirrhosis, higher MELD-Na scores and patients admitted to urban-teaching hospitals. After propensity-matching and multivariable adjustment, albumin use was not associated with AKI-recovery (odds ratio [OR] 0.70, 95% confidence-interval [CI]: 0.59-1.07, P = .130) or in-hospital survival (OR 0.76 [95% CI: 0.46-1.25], P = .280), compared with crystalloids. Findings were unchanged in subgroup analyses of patients with varying cirrhosis complications and disease severity. CONCLUSIONS: USA hospitalized patients with cirrhosis and AKI frequently do not receive intravenous albumin, and albumin use was not associated with improved clinical outcomes. Prospective randomised trials are direly needed to evaluate the impact of albumin in cirrhotics with AKI.

Federation in genomics pipelines: techniques and challenges.

Federation is a popular concept in building distributed cyberinfrastructures, whereby computational resources are provided by multiple organizations through a unified portal, decreasing the complexity of moving data back and forth among multiple organizations. Federation has been used in bioinformatics only to a limited extent, namely, federation of datastores, e.g. SBGrid Consortium for structural biology and Gene Expression Omnibus (GEO) for functional genomics. Here, we posit that it is important to federate both computational resources (CPU, GPU, FPGA, etc.) and datastores to support popular bioinformatics portals, with fast-increasing data volumes and increasing processing requirements. A prime example, and one that we discuss here, is in genomics and metagenomics. It is critical that the processing of the data be done without having to transport the data across large network distances. We exemplify our design and development through our experience with metagenomics-RAST (MG-RAST), the most popular metagenomics analysis pipeline. Currently, it is hosted completely at Argonne National Laboratory. However, through a recently started collaborative National Institutes of Health project, we are taking steps toward federating this infrastructure. Being a widely used resource, we have to move toward federation without disrupting 50 K annual users. In this article, we describe the computational tools that will be useful for federating a bioinformatics infrastructure and the open research challenges that we see in federating such infrastructures. It is hoped that our manuscript can serve to spur greater federation of bioinformatics infrastructures by showing the steps involved, and thus, allow them to scale to support larger user bases.

MG-RAST version 4-lessons learned from a decade of low-budget ultra-high-throughput metagenome analysis.

As technologies change, MG-RAST is adapting. Newly available software is being included to improve accuracy and performance. As a computational service constantly running large volume scientific workflows, MG-RAST is the right location to perform benchmarking and implement algorithmic or platform improvements, in many cases involving trade-offs between specificity, sensitivity and run-time cost. The work in [Glass EM, Dribinsky Y, Yilmaz P, et al. ISME J 2014;8:1-3] is an example; we use existing well-studied data sets as gold standards representing different environments and different technologies to evaluate any changes to the pipeline. Currently, we use well-understood data sets in MG-RAST as platform for benchmarking. The use of artificial data sets for pipeline performance optimization has not added value, as these data sets are not presenting the same challenges as real-world data sets. In addition, the MG-RAST team welcomes suggestions for improvements of the workflow. We are currently working on versions 4.02 and 4.1, both of which contain significant input from the community and our partners that will enable double barcoding, stronger inferences supported by longer-read technologies, and will increase throughput while maintaining sensitivity by using Diamond and SortMeRNA. On the technical platform side, the MG-RAST team intends to support the Common Workflow Language as a standard to specify bioinformatics workflows, both to facilitate development and efficient high-performance implementation of the community's data analysis tasks.

Randomized Linear Algebra Approaches to Estimate the Von Neumann Entropy of Density Matrices.

The von Neumann entropy, named after John von Neumann, is an extension of the classical concept of entropy to the field of quantum mechanics. From a numerical perspective, von Neumann entropy can be computed simply by computing all eigenvalues of a density matrix, an operation that could be prohibitively expensive for large-scale density matrices. We present and analyze three randomized algorithms to approximate von Neumann entropy of real density matrices: our algorithms leverage recent developments in the Randomized Numerical Linear Algebra (RandNLA) literature, such as randomized trace estimators, provable bounds for the power method, and the use of random projections to approximate the eigenvalues of a matrix. All three algorithms come with provable accuracy guarantees and our experimental evaluations support our theoretical findings showing considerable speedup with small loss in accuracy.

AIKYATAN: mapping distal regulatory elements using convolutional learning on GPU.

BACKGROUND: The data deluge can leverage sophisticated ML techniques for functionally annotating the regulatory non-coding genome. The challenge lies in selecting the appropriate classifier for the specific functional annotation problem, within the bounds of the hardware constraints and the model's complexity. In our system AIKYATAN, we annotate distal epigenomic regulatory sites, e.g., enhancers. Specifically, we develop a binary classifier that classifies genome sequences as distal regulatory regions or not, given their histone modifications' combinatorial signatures. This problem is challenging because the regulatory regions are distal to the genes, with diverse signatures across classes (e.g., enhancers and insulators) and even within each class (e.g., different enhancer sub-classes). RESULTS: We develop a suite of ML models, under the banner AIKYATAN, including SVM models, random forest variants, and deep learning architectures, for distal regulatory element (DRE) detection. We demonstrate, with strong empirical evidence, deep learning approaches have a computational advantage. Plus, convolutional neural networks (CNN) provide the best-in-class accuracy, superior to the vanilla variant. With the human embryonic cell line H1, CNN achieves an accuracy of 97.9% and an order of magnitude lower runtime than the kernel SVM. Running on a GPU, the training time is sped up 21x and 30x (over CPU) for DNN and CNN, respectively. Finally, our CNN model enjoys superior prediction performance vis-'a-vis the competition. Specifically, AIKYATAN-CNN achieved 40% higher validation rate versus CSIANN and the same accuracy as RFECS. CONCLUSIONS: Our exhaustive experiments using an array of ML tools validate the need for a model that is not only expressive but can scale with increasing data volumes and diversity. In addition, a subset of these datasets have image-like properties and benefit from spatial pooling of features. Our AIKYATAN suite leverages diverse epigenomic datasets that can then be modeled using CNNs with optimized activation and pooling functions. The goal is to capture the salient features of the integrated epigenomic datasets for deciphering the distal (non-coding) regulatory elements, which have been found to be associated with functional variants. Our source code will be made publicly available at: https://bitbucket.org/cellsandmachines/aikyatan.

The MG-RAST metagenomics database and portal in 2015.

MG-RAST (http://metagenomics.anl.gov) is an open-submission data portal for processing, analyzing, sharing and disseminating metagenomic datasets. The system currently hosts over 200,000 datasets and is continuously updated. The volume of submissions has increased 4-fold over the past 24 months, now averaging 4 terabasepairs per month. In addition to several new features, we report changes to the analysis workflow and the technologies used to scale the pipeline up to the required throughput levels. To show possible uses for the data from MG-RAST, we present several examples integrating data and analyses from MG-RAST into popular third-party analysis tools or sequence alignment tools.

A convex optimization approach for identification of human tissue-specific interactomes.

MOTIVATION: Analysis of organism-specific interactomes has yielded novel insights into cellular function and coordination, understanding of pathology, and identification of markers and drug targets. Genes, however, can exhibit varying levels of cell type specificity in their expression, and their coordinated expression manifests in tissue-specific function and pathology. Tissue-specific/tissue-selective interaction mechanisms have significant applications in drug discovery, as they are more likely to reveal drug targets. Furthermore, tissue-specific transcription factors (tsTFs) are significantly implicated in human disease, including cancers. Finally, disease genes and protein complexes have the tendency to be differentially expressed in tissues in which defects cause pathology. These observations motivate the construction of refined tissue-specific interactomes from organism-specific interactomes. RESULTS: We present a novel technique for constructing human tissue-specific interactomes. Using a variety of validation tests (Edge Set Enrichment Analysis, Gene Ontology Enrichment, Disease-Gene Subnetwork Compactness), we show that our proposed approach significantly outperforms state-of-the-art techniques. Finally, using case studies of Alzheimer's and Parkinson's diseases, we show that tissue-specific interactomes derived from our study can be used to construct pathways implicated in pathology and demonstrate the use of these pathways in identifying novel targets. AVAILABILITY AND IMPLEMENTATION: http://www.cs.purdue.edu/homes/mohammas/projects/ActPro.html CONTACT: mohammadi@purdue.edu.

MicroRNA target prediction using thermodynamic and sequence curves.

BACKGROUND: MicroRNAs (miRNAs) are small regulatory RNA that mediate RNA interference by binding to various mRNA target regions. There have been several computational methods for the identification of target mRNAs for miRNAs. However, these have considered all contributory features as scalar representations, primarily, as thermodynamic or sequence-based features. Further, a majority of these methods solely target canonical sites, which are sites with "seed" complementarity. Here, we present a machine-learning classification scheme, titled Avishkar, which captures the spatial profile of miRNA-mRNA interactions via smooth B-spline curves, separately for various input features, such as thermodynamic and sequence features. Further, we use a principled approach to uniformly model canonical and non-canonical seed matches, using a novel seed enrichment metric. RESULTS: We demonstrate that large number of seed-match patterns have high enrichment values, conserved across species, and that majority of miRNA binding sites involve non-canonical matches, corroborating recent findings. Using spatial curves and popular categorical features, such as target site length and location, we train a linear SVM model, utilizing experimental CLIP-seq data. Our model significantly outperforms all established methods, for both canonical and non-canonical sites. We achieve this while using a much larger candidate miRNA-mRNA interaction set than prior work. CONCLUSIONS: We have developed an efficient SVM-based model for miRNA target prediction using recent CLIP-seq data, demonstrating superior performance, evaluated using ROC curves, specifically about 20% better than the state-of-the-art, for different species (human or mouse), or different target types (canonical or non-canonical). To the best of our knowledge we provide the first distributed framework for microRNA target prediction based on Apache Hadoop and Spark. AVAILABILITY: All source code and data is publicly available at https://bitbucket.org/cellsandmachines/avishkar.

May I see what you see? Predicting visual features from neuronal activity.

Understanding brain response to audiovisual stimuli is a key challenge in understanding neuronal processes. In this paper, we describe our effort aimed at reconstructing video frames from observed functional MRI images. We also demonstrate that our model can predict visual objects. Our method constructs an autoencoder model for a set of training video segments to code video streams into their corresponding latent representations. Next, we learn a mapping from the observed fMRI response to the corresponding latent video frame representation. Finally, we pass the latent vectors computed using the fMRI response through the decoder to reconstruct the predicted image. We show that the representations of video frames and those constructed from corresponding fMRI images are highly clustered, the latent representations can be used to predict objects in video frames using just the fMRI frames, and fMRI responses can be used to reconstruct the inputs to predict the presence of faces.

Identification of predictive patient characteristics for assessing the probability of COVID-19 in-hospital mortality.

As the world emerges from the COVID-19 pandemic, there is an urgent need to understand patient factors that may be used to predict the occurrence of severe cases and patient mortality. Approximately 20% of SARS-CoV-2 infections lead to acute respiratory distress syndrome caused by the harmful actions of inflammatory mediators. Patients with severe COVID-19 are often afflicted with neurologic symptoms, and individuals with pre-existing neurodegenerative disease have an increased risk of severe COVID-19. Although collectively, these observations point to a bidirectional relationship between severe COVID-19 and neurologic disorders, little is known about the underlying mechanisms. Here, we analyzed the electronic health records of 471 patients with severe COVID-19 to identify clinical characteristics most predictive of mortality. Feature discovery was conducted by training a regularized logistic regression classifier that serves as a machine-learning model with an embedded feature selection capability. SHAP analysis using the trained classifier revealed that a small ensemble of readily observable clinical features, including characteristics associated with cognitive impairment, could predict in-hospital mortality with an accuracy greater than 0.85 (expressed as the area under the ROC curve of the classifier). These findings have important implications for the prioritization of clinical measures used to identify patients with COVID-19 (and, potentially, other forms of acute respiratory distress syndrome) having an elevated risk of death.

Longer time to recovery from acute kidney injury is associated with major adverse kidney events in patients with cirrhosis.

BACKGROUND: In patients with cirrhosis and acute kidney injury (AKI), longer time to AKI-recovery may increase the risk of subsequent major-adverse-kidney-events (MAKE). AIMS: To examine the association between timing of AKI-recovery and risk of MAKE in patients with cirrhosis. METHODS: Hospitalised patients with cirrhosis and AKI (n = 5937) in a nationwide database were assessed for time to AKI-recovery and followed for 180-days. Timing of AKI-recovery (return of serum creatinine <0.3 mg/dL of baseline) from AKI-onset was grouped by Acute-Disease-Quality-Initiative Renal Recovery consensus: 0-2, 3-7, and >7-days. Primary outcome was MAKE at 90-180-days. MAKE is an accepted clinical endpoint in AKI and defined as the composite outcome of ≥25% decline in estimated-glomerular-filtration-rate (eGFR) compared with baseline with the development of de-novo chronic-kidney-disease (CKD) stage ≥3 or CKD progression (≥50% reduction in eGFR compared with baseline) or new haemodialysis or death. Landmark competing-risk multivariable analysis was performed to determine the independent association between timing of AKI-recovery and risk of MAKE. RESULTS: 4655 (75%) achieved AKI-recovery: 0-2 (60%), 3-7 (31%), and >7-days (9%). Cumulative-incidence of MAKE was 15%, 20%, and 29% for 0-2, 3-7, >7-days recovery groups, respectively. On adjusted multivariable competing-risk analysis, compared to 0-2-days, recovery at 3-7 and >7-days was independently associated with an increased risk for MAKE: sHR 1.45 (95% CI 1.01-2.09, p = 0.042), sHR 2.33 (95% CI 1.40-3.90, p = 0.001), respectively. CONCLUSION: Longer time to recovery is associated with an increased risk of MAKE in patients with cirrhosis and AKI. Further research should examine interventions to shorten AKI-recovery time and its impact on subsequent outcomes.

Universal programmable quantum circuit schemes to emulate an operator.

Unlike fixed designs, programmable circuit designs support an infinite number of operators. The functionality of a programmable circuit can be altered by simply changing the angle values of the rotation gates in the circuit. Here, we present a new quantum circuit design technique resulting in two general programmable circuit schemes. The circuit schemes can be used to simulate any given operator by setting the angle values in the circuit. This provides a fixed circuit design whose angles are determined from the elements of the given matrix-which can be non-unitary-in an efficient way. We also give both the classical and quantum complexity analysis for these circuits and show that the circuits require a few classical computations. For the electronic structure simulation on a quantum computer, one has to perform the following steps: prepare the initial wave function of the system; present the evolution operator U = e(-iHt) for a given atomic and molecular Hamiltonian H in terms of quantum gates array and apply the phase estimation algorithm to find the energy eigenvalues. Thus, in the circuit model of quantum computing for quantum chemistry, a crucial step is presenting the evolution operator for the atomic and molecular Hamiltonians in terms of quantum gate arrays. Since the presented circuit designs are independent from the matrix decomposition techniques and the global optimization processes used to find quantum circuits for a given operator, high accuracy simulations can be done for the unitary propagators of molecular Hamiltonians on quantum computers. As an example, we show how to build the circuit design for the hydrogen molecule.

Identifying and analyzing sepsis states: A retrospective study on patients with sepsis in ICUs.

Sepsis accounts for more than 50% of hospital deaths, and the associated cost ranks the highest among hospital admissions in the US. Improved understanding of disease states, progression, severity, and clinical markers has the potential to significantly improve patient outcomes and reduce cost. We develop a computational framework that identifies disease states in sepsis and models disease progression using clinical variables and samples in the MIMIC-III database. We identify six distinct patient states in sepsis, each associated with different manifestations of organ dysfunction. We find that patients in different sepsis states are statistically significantly composed of distinct populations with disparate demographic and comorbidity profiles. Our progression model accurately characterizes the severity level of each pathological trajectory and identifies significant changes in clinical variables and treatment actions during sepsis state transitions. Collectively, our framework provides a holistic view of sepsis, and our findings provide the basis for future development of clinical trials, prevention, and therapeutic strategies for sepsis.

Constructing Compact Signatures for Individual Fingerprinting of Brain Connectomes.

Recent neuroimaging studies have shown that functional connectomes are unique to individuals, i.e., two distinct fMRIs taken over different sessions of the same subject are more similar in terms of their connectomes than those from two different subjects. In this study, we present new results that identify specific parts of resting state and task-specific connectomes that are responsible for the unique signatures. We show that a very small part of the connectome can be used to derive features for discriminating between individuals. A network of these features is shown to achieve excellent training and test accuracy in matching imaging datasets. We show that these features are statistically significant, robust to perturbations, invariant across populations, and are localized to a small number of structural regions of the brain. Furthermore, we show that for task-specific connectomes, the regions identified by our method are consistent with their known functional characterization. We present a new matrix sampling technique to derive computationally efficient and accurate methods for identifying the discriminating sub-connectome and support all of our claims using state-of-the-art statistical tests and computational techniques.

Functional characterization and topological modularity of molecular interaction networks.

BACKGROUND: Analyzing interaction networks for functional characterization poses significant challenges arising from the noisy, incomplete, and generic nature of both the interaction data as well as functional annotation of molecules. Network-based methods focus on interacting molecules (pairs or sets) occurring in close proximity to infer functional associations. RESULTS: In this paper we perform a formal comparative investigation of the relationship between functional coherence and topological proximity in networks. We investigate the problem of assessing the coherence of sets of biomolecules (or segments thereof) taking into account functional specificity as well as the distribution of functional attributes across entity groups. We also propose novel measures of topological proximity that are more robust to noisy and incomplete interaction data. CONCLUSION: We derive the following results in this paper: (i) there exists strong correlation between functional similarity and topological proximity in various network abstractions, with domain interaction networks (DDIs) demonstrating higher correlation than protein interaction networks (PPIs); (ii) measures that quantify coherence among entire sets of proteins are superior to aggregates of known pair-wise measures; and (iii) random-walk based measures of topological proximity are better suited to existing interaction data. We validate our methods on diverse data, including experimentally and computationally derived PPIs and DDIs, as well as on sets of known biologically related groups of molecules.

A reactive molecular dynamics simulation of the silica-water interface.

We report our study of a silica-water interface using reactive molecular dynamics. This first-of-its-kind simulation achieves length and time scales required to investigate the detailed chemistry of the system. Our molecular dynamics approach is based on the ReaxFF force field of van Duin et al. [J. Phys. Chem. A 107, 3803 (2003)]. The specific ReaxFF implementation (SERIALREAX) and force fields are first validated on structural properties of pure silica and water systems. Chemical reactions between reactive water and dangling bonds on a freshly cut silica surface are analyzed by studying changing chemical composition at the interface. In our simulations, reactions involving silanol groups reach chemical equilibrium in approximately 250 ps. It is observed that water molecules penetrate a silica film through a proton-transfer process we call "hydrogen hopping," which is similar to the Grotthuss mechanism. In this process, hydrogen atoms pass through the film by associating and dissociating with oxygen atoms within bulk silica, as opposed to diffusion of intact water molecules. The effective diffusion constant for this process, taken to be that of hydrogen atoms within silica, is calculated to be 1.68 x 10(-6) cm(2)/s. Polarization of water molecules in proximity of the silica surface is also observed. The subsequent alignment of dipoles leads to an electric potential difference of approximately 10.5 V between the silica slab and water.

Integrated querying and version control of context-specific biological networks.

MOTIVATION: Biomolecular data stored in public databases is increasingly specialized to organisms, context/pathology and tissue type, potentially resulting in significant overhead for analyses. These networks are often specializations of generic interaction sets, presenting opportunities for reducing storage and computational cost. Therefore, it is desirable to develop effective compression and storage techniques, along with efficient algorithms and a flexible query interface capable of operating on compressed data structures. Current graph databases offer varying levels of support for network integration. However, these solutions do not provide efficient methods for the storage and querying of versioned networks. RESULTS: We present VerTIoN, a framework consisting of novel data structures and associated query mechanisms for integrated querying of versioned context-specific biological networks. As a use case for our framework, we study network proximity queries in which the user can select and compose a combination of tissue-specific and generic networks. Using our compressed version tree data structure, in conjunction with state-of-the-art numerical techniques, we demonstrate real-time querying of large network databases. CONCLUSION: Our results show that it is possible to support flexible queries defined on heterogeneous networks composed at query time while drastically reducing response time for multiple simultaneous queries. The flexibility offered by VerTIoN in composing integrated network versions opens significant new avenues for the utilization of ever increasing volume of context-specific network data in a broad range of biomedical applications. AVAILABILITY AND IMPLEMENTATION: VerTIoN is implemented as a C++ library and is available at http://compbio.case.edu/omics/software/vertion and https://github.com/tjcowman/vertion. CONTACT: tyler.cowman@case.edu.