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

Wang, Lingfei; Audenaert, Pieter; Michoel, Tom

doi:10.3389/fgene.2019.01196

Cited by 21 publications

(30 citation statements)

References 61 publications

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“…As illustrated in Figure 4b, this GRN can be substituted for a simple network topology, the so-called Bayesian network (BN), and its constituent nodes and edges represent the level of gene expression and the direction of the regulatory effect between genes, respectively. [72,76] In general, the BN can infer the posterior probabilities of hidden and unobserved variables based on the prior probabilities from the various observations, providing essential information such as the most likely conclusion, potential alternatives, and uncertainty of the inferred results. [72,76] Because of these benefits, BNs for GRN applications have been widely implemented, mainly using software approaches.…”

Section: Probabilistic Bayesian Inference For Gene Regulatory Networkmentioning

confidence: 99%

Controllable SiO_x Nanorod Memristive Neuron for Probabilistic Bayesian Inference

et al. 2021

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Modern artificial neural network technology using a deterministic computing framework is faced with a critical challenge in dealing with massive data that are largely unstructured and ambiguous. This challenge demands the advances of an elementary physical device for tackling these uncertainties. Here, we designed and fabricated a SiOx nanorod memristive device by employing the glancing angle deposition (GLAD) technique, suggesting a controllable stochastic artificial neuron that can mimic the fundamental integrate‐and‐fire signaling and stochastic dynamics of a biological neuron. The nanorod structure provides the random distribution of multiple nanopores all across the active area, capable of forming a multitude of Si filaments at many SiOx nanorod edges after the electromigration process, leading to a stochastic switching event with very high dynamic range (≈5.15 × 1010) and low energy (≈4.06 pJ). Different probabilistic activation (ProbAct) functions in a sigmoid form are implemented, showing its controllability with low variation by manufacturing and electrical programming schemes. Furthermore, as an application prospect, based on the suggested memristive neuron, we demonstrated the self‐resting neural operation with the local circuit configuration and revealed probabilistic Bayesian inferences for genetic regulatory networks with low normalized mean squared errors (≈2.41 × 10‐2) and its robustness to the ProbAct variation.

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Section: Probabilistic Bayesian Inference For Gene Regulatory Networkmentioning

confidence: 99%

Controllable SiO_x Nanorod Memristive Neuron for Probabilistic Bayesian Inference

et al. 2021

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“…CAUSE is a recent extension of MR that uses genome-wide summary statistics to model causal effects while accounting for pleiotropy 20 . Another type of algorithms address gene network inference by joint analysis of genetic variants and gene expression data in order to learn a large-scale graphical model with causal links among genes 21 – 23 . Recently, Howey et al explored similar methodology as an alternative for MR 24 .…”

Section: Introductionmentioning

confidence: 99%

Graphical analysis for phenome-wide causal discovery in genotyped population-scale biobanks

et al. 2021

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Causal inference via Mendelian randomization requires making strong assumptions about horizontal pleiotropy, where genetic instruments are connected to the outcome not only through the exposure. Here, we present causal Graphical Analysis Using Genetics (cGAUGE), a pipeline that overcomes these limitations using instrument filters with provable properties. This is achievable by identifying conditional independencies while examining multiple traits. cGAUGE also uses ExSep (Exposure-based Separation), a novel test for the existence of causal pathways that does not require selecting instruments. In simulated data we illustrate how cGAUGE can reduce the empirical false discovery rate by up to 30%, while retaining the majority of true discoveries. On 96 complex traits from 337,198 subjects from the UK Biobank, our results cover expected causal links and many new ones that were previously suggested by correlation-based observational studies. Notably, we identify multiple risk factors for cardiovascular disease, including red blood cell distribution width.

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“…Bayesian Networks (BNs)based dependency modeling is an established computational biology tool that has been rapidly gaining acceptance in big biological data analysis (Branciamore et al 2018;Cooper et al 2015;Gogoshin, Boerwinkle, and Rodin 2017;Jiang, Barmada, and Visweswaran. 2010;Lan et al 2016;Neapolitan, Xue, and Jiang 2014;Needham et al 2007;Pe'er 2005;Piatetsky-Shapiro and Tamayo 2003;Qi, Li, and Cheng 2014;Rodin et al 2005;Rodin et al 2012;Sedgewick et al 2019;Sherif, Zayed, and Fakhr 2015;Wang, Audenaert, and Michoel 2019;Yin et al 2015;Zeng, Jiang, and Neapolitan 2016;Ziebarth, Bhattacharya, and Cui 2013;Zhang and Shi 2017;Zhang et al 2017;Zhang et al 2014). Comprehensive treatments of BN methodology, and probabilistic networks (PNs) in general, can be found in numerous textbooks (Pearl 1988;Pearl 2009;Russell and Norvig.…”

Section: Introductionmentioning

confidence: 99%

“…In view of these developments, the ability to objectively assess the performance of BN modeling is of the utmost importance. Currently, there are three principal venues for accomplishing this: (i) using well-established (in machine learning community) predefined benchmark models/datasets, such as Alarm or Asia, (ii) using various specialized biologically-oriented benchmark datasets, both real and simulated, such as DREAM (Eicher et al 2019;Wang, Audenaert, and Michoel 2019;Xing et al 2017), and (iii) developing more-or-less realistic simulation frameworks (Andrews, Ramsey, and Cooper 2018;Han et al 2017;Ellis and Wong 2008;Isci et al 2011;Tasaki et al 2015;Zhang et al 2019). The first approach is necessarily limited and does not generalize to the modern high-dimensional biological data.…”

Section: Introductionmentioning

confidence: 99%

“…The first approach is necessarily limited and does not generalize to the modern high-dimensional biological data. The later two strategies have their obvious pros and cons; a useful discussion can be found in (Wang, Audenaert, and Michoel 2019). In this study, we concentrate on the third approach, namely constructing robust, generalizable, and mathematically rigorous simulation frameworks.…”

Section: Introductionmentioning

confidence: 99%

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Synthetic data generation with probabilistic Bayesian Networks

Gogoshin

Branciamore

Rodin

2020

Preprint

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Bayesian Network (BN) modeling is a prominent and increasingly popular computational systems biology method.It aims at constructing probabilistic networks from the big heterogeneous biological data that reflect the underlying networks of biological relationships. Currently, there is a variety of strategies for evaluating the BN methodology performance, ranging from utilizing the artificial benchmark datasets and models, to the specialized biological benchmark datasets, to the simulation studies that generate synthetic data from the predefined network models. The latter is arguably the most comprehensive approach; however, existing implementations are typically limited by their reliance on the SEM (structural equation modeling) framework, with many explicit and implicit assumptions that might not be realistic in a typical biological data analysis scenario. In this study, we develop an alternative, purely probabilistic, simulation framework that is a more appropriate fit with the real biological data and biological network models. In conjunction, we also expand on our current understanding of the theoretical notions of causality and dependence / conditional independence in BNs and Markov Blankets within.

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

Cited by 21 publications

References 61 publications

Controllable SiO_x Nanorod Memristive Neuron for Probabilistic Bayesian Inference

Controllable SiO_x Nanorod Memristive Neuron for Probabilistic Bayesian Inference

Graphical analysis for phenome-wide causal discovery in genotyped population-scale biobanks

Synthetic data generation with probabilistic Bayesian Networks

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

Cited by 21 publications

References 61 publications

Controllable SiOx Nanorod Memristive Neuron for Probabilistic Bayesian Inference

Controllable SiOx Nanorod Memristive Neuron for Probabilistic Bayesian Inference

Graphical analysis for phenome-wide causal discovery in genotyped population-scale biobanks

Synthetic data generation with probabilistic Bayesian Networks

Contact Info

Product

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Controllable SiO_x Nanorod Memristive Neuron for Probabilistic Bayesian Inference

Controllable SiO_x Nanorod Memristive Neuron for Probabilistic Bayesian Inference