• Bayesian network
• SHELF
• pancreatic cancer
• prior elicitation
• prognosis

• NA Fondazione Umberto Veronesi

• P50 HD105353 NICHD NIH HHS
• T15 LM007359 NLM NIH HHS
• National Institutes of Health
• National Library of Medicine

• Bayesian inference
• Epidemiological modelling
• Markov chain Monte Carlo
• Networks
• Stochastic simulation algorithm
• Tensor train

• Algorithms
• Bayes Theorem
• Humans
• Computer Simulation

• EP/T031255/1 Engineering and Physical Sciences Research Council
• EP/R014604/1 Engineering and Physical Sciences Research Council
• RF-2021-258 Leverhulme Trust
• Simons Foundation

• Gene Regulatory Networks/genetics
• Bayes Theorem
• Algorithms
• Computational Biology/methods

• gene regulatory networks
• data integration
• machine learning

• Humans
• Animals
• Gene Regulatory Networks
• Transcription Factors/genetics
• Gene Expression Regulation
• Systems Biology
• Drosophila/genetics
• Saccharomyces cerevisiae/genetics
• Gene Expression

• P41 GM132087 NIGMS NIH HHS
• R01 GM067761 NIGMS NIH HHS
• R35 GM147241 NIGMS NIH HHS
• R35GM147241 NIGMS NIH HHS
• U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)

• Chow–Liu algorithm
• Metabolite pathway analysis
• Model pool
• Prior structure
• Structural BIC
• Structure learning algorithm
• Two-step algorithm
• Undirected Gaussian graphical model

• Algorithms
• Bayes Theorem
• Cohort Studies
• Gene Expression Profiling/methods
• Humans
• Normal Distribution

• R01 LM012012 NLM NIH HHS
• UH3 OD023275 NIH HHS
• R01 LM012723 NLM NIH HHS
• P30 CA023108 NCI NIH HHS
• P01 ES022832 NIEHS NIH HHS
• P30 DK056350 NIDDK NIH HHS
• US National Institutes of Health
• US Environmental Protection Agency
• NIH

• chaotic oscillators
• excitable dynamics
• modular graphs
• random graphs
• scale-free graphs
• synchronisation

• Brain
• Ecology
• Ecosystem

• H2020 Marie Skłodowska-Curie Actions

• Algorithms
• Bayes Theorem
• Colonic Neoplasms/genetics
• Colonic Neoplasms/metabolism
• Colonic Neoplasms/pathology
• Computational Biology/methods
• Gene Expression Profiling
• Gene Regulatory Networks/genetics
• Humans
• Liver Neoplasms/genetics
• Liver Neoplasms/metabolism
• Liver Neoplasms/secondary
• Transcriptome/genetics

• Bayesian methods
• Complex data
• Genomics
• Graphical models
• Neuroimaging

• P30 CA046592 NCI NIH HHS
• R01 CA160736 NCI NIH HHS
• R01 CA244845 NCI NIH HHS
• R21 CA220299 NCI NIH HHS
• MIUR
• Division of Mathematical Sciences
• National Institutes of Health (US)
• National Science Foundation (US)
• Università degli Studi di Firenze

• CRISPR
• Cas9
• Machine learning
• Nested Effects Models
• Network biology
• Networks
• Sequencing
• Single-cell genomics
• scRNA-seq

• CRISPR-Cas Systems
• Cell Cycle
• Gene Regulatory Networks
• RNA-Seq/methods
• Single-Cell Analysis/methods

• 19274 Cancer Research UK
• 28290 Cancer Research UK
• BB/R006563/1 Biotechnology and Biological Sciences Research Council

• Bayesian network
• expression quantitative trait loci analysis
• gene expression
• network inference
• systems genetics

• Data integration
• Gene expression
• Network inference
• Regulatory networks
• Transcription factor

• Computational Biology
• Databases, Genetic
• Gene Regulatory Networks
• Humans

• Bayesian nonparametrics
• graphical models
• hierarchical models
• normalized completely random measures
• radiomics data
• t-distribution

• T32 CA096520 NCI NIH HHS

• Bayesian network
• ICU
• cardiac function
• cardiology
• clinical decision-support
• cognition
• critical care
• educated guess
• medical education
• physical examination

• Bayesian networks
• genetic regulatory networks
• microarray data
• prior knowledge
• structural restrictions

• Algorithms
• Bayes Theorem
• Colonic Neoplasms/genetics
• Databases, Genetic
• Gene Expression Profiling/methods
• Genomics/methods
• Humans
• Models, Statistical
• Normal Distribution

Cascades of phosphorylation between protein kinases comprise a core mechanism in the integration and propagation of intracellular signals. Although we have accumulated a wealth of knowledge around some such pathways, this is subject to study biases and much remains to be uncovered. Phosphoproteomics, the identification and quantification of phosphorylated proteins on a proteomic scale, provides a high-throughput means of interrogating the state of intracellular phosphorylation, both at the pathway level and at the whole-cell level. In this review, we discuss methods for using human quantitative phosphoproteomic data to reconstruct the underlying signalling networks that generated it. We address several challenges imposed by the data on such analyses and we consider promising advances towards reconstructing unbiased, kinome-scale signalling networks.

• biological networks
• intracellular signaling
• phosphorylation/dephosphorylation
• systems biology

• gene regulatory networks
• network topology

• Animals
• Cellular Reprogramming/genetics
• Drosophila melanogaster/genetics
• Escherichia coli/metabolism
• Female
• Gene Expression Regulation
• Gene Regulatory Networks
• Male
• Reproducibility of Results
• Sex Characteristics

• Intramural Research Programs of the National Institute of Diabetes and Digestive and Kidney Diseases,the National Institutes of Health (NIH)
• Intramural Research Programs of the National Library of Medicine, the National Institutes of Health (NIH)

Transcriptional regulatory networks specify regulatory proteins controlling the context-specific expression levels of genes. Inference of genome-wide regulatory networks is central to understanding gene regulation, but remains an open challenge. Expression-based network inference is among the most popular methods to infer regulatory networks, however, networks inferred from such methods have low overlap with experimentally derived (e.g. ChIP-chip and transcription factor (TF) knockouts) networks. Currently we have a limited understanding of this discrepancy. To address this gap, we first develop a regulatory network inference algorithm, based on probabilistic graphical models, to integrate expression with auxiliary datasets supporting a regulatory edge. Second, we comprehensively analyze our and other state-of-the-art methods on different expression perturbation datasets. Networks inferred by integrating sequence-specific motifs with expression have substantially greater agreement with experimentally derived networks, while remaining more predictive of expression than motif-based networks. Our analysis suggests natural genetic variation as the most informative perturbation for network inference, and, identifies core TFs whose targets are predictable from expression. Multiple reasons make the identification of targets of other TFs difficult, including network architecture and insufficient variation of TF mRNA level. Finally, we demonstrate the utility of our inference algorithm to infer stress-specific regulatory networks and for regulator prioritization.

To develop a framework to incorporate background domain knowledge into classification rule learning for knowledge discovery in biomedicine.

Bayesian rule learning (BRL) is a rule-based classifier that uses a greedy best-first search over a space of Bayesian belief-networks (BN) to find the optimal BN to explain the input dataset, and then infers classification rules from this BN. BRL uses a Bayesian score to evaluate the quality of BNs. In this paper, we extended the Bayesian score to include informative structure priors, which encodes our prior domain knowledge about the dataset. We call this extension of BRL as BRL_p. The structure prior has a λ hyperparameter that allows the user to tune the degree of incorporation of the prior knowledge in the model learning process. We studied the effect of λ on model learning using a simulated dataset and a real-world lung cancer prognostic biomarker dataset, by measuring the degree of incorporation of our specified prior knowledge. We also monitored its effect on the model predictive performance. Finally, we compared BRL_p to other state-of-the-art classifiers commonly used in biomedicine.

We evaluated the degree of incorporation of prior knowledge into BRL_p, with simulated data by measuring the Graph Edit Distance between the true data-generating model and the model learned by BRL_p. We specified the true model using informative structure priors. We observed that by increasing the value of λ we were able to increase the influence of the specified structure priors on model learning. A large value of λ of BRL_p caused it to return the true model. This also led to a gain in predictive performance measured by area under the receiver operator characteristic curve (AUC). We then obtained a publicly available real-world lung cancer prognostic biomarker dataset and specified a known biomarker from literature [the epidermal growth factor receptor (EGFR) gene]. We again observed that larger values of λ led to an increased incorporation of EGFR into the final BRL_p model. This relevant background knowledge also led to a gain in AUC.

BRL_p enables tunable structure priors to be incorporated during Bayesian classification rule learning that integrates data and knowledge as demonstrated using lung cancer biomarker data.

• Supervised machine learning
• Rule-based models
• Bayesian methods
• Background knowledge
• Informative priors
• Biomarker discovery

• Bayesian inference
• Multi-level modeling
• regulation and signaling dynamics
• statistical network analysis

• 19274 Cancer Research UK
• R01 GM096193 NIGMS NIH HHS

• Gene networks
• Obesity
• RNA-Seq
• Transcription factors
• Transcriptomics
• WGCNA

Alzheimer’s disease (AD) progressively destroys cognitive abilities in the aging population with tremendous effects on memory. Despite recent progress in understanding the underlying mechanisms, high drug attrition rates have put a question mark behind our knowledge about its etiology. Re-evaluation of past studies could help us to elucidate molecular-level details of this disease. Several methods to infer such networks exist, but most of them do not elaborate on context specificity and completeness of the generated networks, missing out on lesser-known candidates. In this study, we present a novel strategy that corroborates common mechanistic patterns across large scale AD gene expression studies and further prioritizes potential biomarker candidates. To infer gene regulatory networks (GRNs), we applied an optimized version of the BC3Net algorithm, named BC3Net10, capable of deriving robust and coherent patterns. In principle, this approach initially leverages the power of literature knowledge to extract AD specific genes for generating viable networks. Our findings suggest that AD GRNs show significant enrichment for key signaling mechanisms involved in neurotransmission. Among the prioritized genes, well-known AD genes were prominent in synaptic transmission, implicated in cognitive deficits. Moreover, less intensive studied AD candidates (STX2, HLA-F, HLA-C, RAB11FIP4, ARAP3, AP2A2, ATP2B4, ITPR2, and ATP2A3) are also involved in neurotransmission, providing new insights into the underlying mechanism. To our knowledge, this is the first study to generate knowledge-instructed GRNs that demonstrates an effective way of combining literature-based knowledge and data-driven analysis to identify lesser known candidates embedded in stable and robust functional patterns across disparate datasets.

• Alzheimer’s disease
• gene regulatory networks
• microarray analysis
• synaptic transmission

• Bayesian inference
• Chronic obstructive pulmonary disease (COPD)
• Gaussian graphical model
• Gene network
• Markov random field prior
• Spike-and-slab prior

• Adolescent
• Adult
• Bayes Theorem
• Child
• Female
• Humans
• Male
• Malocclusion/pathology
• Malocclusion/physiopathology
• Mandible/growth & development
• Mandible/pathology
• Maxilla/growth & development
• Maxilla/pathology
• Models, Biological

• Enzyme kinetics
• Kinetic models
• Metabolic control analysis
• Metabolic modelling
• Monte Carlo simulation
• Parameter estimation

• Empirical Bayes
• High-dimensional Bayesian inference
• Prior information
• Undirected network
• Variational approximation

• Algorithms
• Computer Simulation
• Data Interpretation, Statistical
• Models, Theoretical

• Algorithms
• Calibration
• Fuzzy Logic
• Gene Regulatory Networks/physiology
• Humans
• Models, Biological
• Proteomics/methods
• Signal Transduction/physiology

• Bayesian inference
• cancer prognosis
• gene regulatory networks
• network uncertainty

• cancer
• heterogeneity
• microfluidics
• molecular analysis
• single-cell analysis
• tumors

• Boolean mode ling
• DDEPN
• Network reconstruction
• Protein signaling
• Proteomics
• Reverse-phase protein arrays

• Algorithms
• Databases, Protein
• Humans
• Protein Array Analysis/methods
• Protein Interaction Mapping/methods
• Proteomics/methods
• Signal Transduction

Motivation: Markov networks are undirected graphical models that are widely used to infer relations between genes from experimental data. Their state-of-the-art inference procedures assume the data arise from a Gaussian distribution. High-throughput omics data, such as that from next generation sequencing, often violates this assumption. Furthermore, when collected data arise from multiple related but otherwise nonidentical distributions, their underlying networks are likely to have common features. New principled statistical approaches are needed that can deal with different data distributions and jointly consider collections of datasets.

Results: We present FuseNet, a Markov network formulation that infers networks from a collection of nonidentically distributed datasets. Our approach is computationally efficient and general: given any number of distributions from an exponential family, FuseNet represents model parameters through shared latent factors that define neighborhoods of network nodes. In a simulation study, we demonstrate good predictive performance of FuseNet in comparison to several popular graphical models. We show its effectiveness in an application to breast cancer RNA-sequencing and somatic mutation data, a novel application of graphical models. Fusion of datasets offers substantial gains relative to inference of separate networks for each dataset. Our results demonstrate that network inference methods for non-Gaussian data can help in accurate modeling of the data generated by emergent high-throughput technologies.

Availability and implementation: Source code is at https://github.com/marinkaz/fusenet.

Contact:blaz.zupan@fri.uni-lj.si

Supplementary information:Supplementary information is available at Bioinformatics online.