Synthetic data generation with probabilistic Bayesian Networks

<abstract><p>Bayesian Network (BN) modeling is a prominent and increasingly popular computational systems biology method. It aims to construct network graphs from the large heterogeneous biological datasets that reflect the underlying biological relationships. Currently, a variety of strategies exist for evaluating BN methodology performance, ranging from utilizing artificial benchmark datasets and models, to specialized biological benchmark datasets, to simulation studies that generate synthetic data from predefined network models. The last is arguably the most comprehensive approach; however, existing implementations often rely on explicit and implicit assumptions that may be unrealistic in a typical biological data analysis scenario, or are poorly equipped for automated arbitrary model generation. In this study, we develop a purely probabilistic simulation framework that addresses the demands of statistically sound simulations studies in an unbiased fashion. Additionally, we expand on our current understanding of the theoretical notions of causality and dependence / conditional independence in BNs and the Markov Blankets within.</p></abstract>

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Synthetic data generation with probabilistic Bayesian Networks

10.1101/2020.06.14.151084 ◽

2020 ◽

Author(s):

Grigoriy Gogoshin ◽

Sergio Branciamore ◽

Andrei S. Rodin

Keyword(s):

Structural Equation ◽

Synthetic Data ◽

Network Models ◽

Biological Data ◽

Equation Modeling ◽

Data Generation ◽

Computational Systems Biology ◽

Modeling Framework ◽

Probabilistic Networks ◽

Benchmark Datasets

AbstractBayesian Network (BN) modeling is a prominent and increasingly popular computational systems biology method. It aims to construct probabilistic networks from the large heterogeneous biological datasets that reflect the underlying networks of biological relationships. Currently, a variety of strategies exist for evaluating BN methodology performance, ranging from utilizing artificial benchmark datasets and models, to specialized biological benchmark datasets, to simulation studies that generate synthetic data from 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, which includes many explicit and implicit assumptions that may be unrealistic in a typical biological data analysis scenario. In this study, we develop an alternative, purely probabilistic, simulation framework that more appropriately fits with 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 the Markov Blankets within.

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Development of synthetic data generation algorithms for training neural network models for detecting objects in an image

CPT2020 The 8th International Scientific Conference on Computing in Physics and Technology Proceedings ◽

10.30987/conferencearticle_5fd755bfe84ee1.62713026 ◽

2020 ◽

Author(s):

Vladimir Berzin ◽

Mikhail Sudeykin

Keyword(s):

Synthetic Data ◽

Network Models ◽

Stable Operation ◽

Data Generation ◽

Training Set ◽

Neural Network Models ◽

Training Models ◽

Synthetic Data Generation ◽

Natural Data ◽

Training Neural Network

The paper is devoted to the development of synthetic data generation algorithms for training models of object detectors in the image. Modern SOTA architectures based on convolutional neural networks, as well as methods for their training, are considered as target models. The features that a training set based on synthetic data must have for the stable operation of the model on a set of natural data are revealed. The proposed methods and principles for generating such data are described. As an accompanying practical example, the problem of detecting commodity items on the shelves of grocery supermarkets is considered, in the context of which the implemented algorithms were tested.

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A rainfall-runoff probabilistic simulation program: 1. Synthetic data generation

Environmental Software ◽

10.1016/s0266-9838(96)00013-5 ◽

1996 ◽

Vol 11 (4) ◽

pp. 235-242 ◽

Cited By ~ 10

Author(s):

T.V. Hromadka

Keyword(s):

Synthetic Data ◽

Simulation Program ◽

Data Generation ◽

Rainfall Runoff ◽

Probabilistic Simulation ◽

Synthetic Data Generation

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Synthetic data generation for optical flow evaluation in the neurosurgical domain

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2021-1015 ◽

2021 ◽

Vol 7 (1) ◽

pp. 67-71

Author(s):

Markus Philipp ◽

Neal Bacher ◽

Jonas Nienhaus ◽

Lars Hauptmann ◽

Laura Lang ◽

...

Keyword(s):

Optical Flow ◽

Synthetic Data ◽

Video Data ◽

Computer Assisted ◽

Data Generation ◽

Optical Flow Method ◽

Synthetic Data Generation ◽

Benchmark Datasets ◽

Background Motion ◽

Future Work

Abstract Towards computer-assisted neurosurgery, scene understanding algorithms for microscope video data are required. Previous work utilizes optical flow to extract spatiotemporal context from neurosurgical video sequences. However, to select an appropriate optical flow method, we need to analyze which algorithm yields the highest accuracy for the neurosurgical domain. Currently, there are no benchmark datasets available for neurosurgery. In our work, we present an approach to generate synthetic data for optical flow evaluation on the neurosurgical domain. We simulate image sequences and thereby take into account domainspecific visual conditions such as surgical instrument motion. Then, we evaluate two optical flow algorithms, Farneback and PWC-Net, on our synthetic data. Qualitative and quantitative assessments confirm that our data can be used to evaluate optical flow for the neurosurgical domain. Future work will concentrate on extending the method by modeling additional effects in neurosurgery such as elastic background motion.

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