TaBooN -- Boolean Network Synthesis Based on Tabu Search

• URL: http://arxiv.org/abs/2009.03587v1
• Date: Tue, 8 Sep 2020 08:56:14 GMT
• Title: TaBooN -- Boolean Network Synthesis Based on Tabu Search
• Authors: Sara Sadat Aghamiri, Franck Delaplace
• Abstract summary: Omics-technologies revolutionized the investigation of biology by producing molecular data in multiple dimensions and scale. Biological network is composed of nodes referring to the components such as genes or proteins, and the edges/arcs formalizing interactions between them.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent developments in Omics-technologies revolutionized the investigation of biology by producing molecular data in multiple dimensions and scale. This breakthrough in biology raises the crucial issue of their interpretation based on modelling. In this undertaking, network provides a suitable framework for modelling the interactions between molecules. Basically a Biological network is composed of nodes referring to the components such as genes or proteins, and the edges/arcs formalizing interactions between them. The evolution of the interactions is then modelled by the definition of a dynamical system. Among the different categories of network, the Boolean network offers a reliable qualitative framework for the modelling. Automatically synthesizing a Boolean network from experimental data therefore remains a necessary but challenging issue. In this study, we present taboon, an original work-flow for synthesizing Boolean Networks from biological data. The methodology uses the data in the form of Boolean profiles for inferring all the potential local formula inference. They combine to form the model space from which the most truthful model with regards to biological knowledge and experiments must be found. In the taboon work-flow the selection of the fittest model is achieved by a Tabu-search algorithm. taboon is an automated method for Boolean Network inference from experimental data that can also assist to evaluate and optimize the dynamic behaviour of the biological networks providing a reliable platform for further modelling and predictions.

Related papers

• Causal Representation Learning from Multimodal Biological Observations [57.00712157758845]
  We aim to develop flexible identification conditions for multimodal data. We establish identifiability guarantees for each latent component, extending the subspace identification results from prior work. Our key theoretical ingredient is the structural sparsity of the causal connections among distinct modalities.
  arXiv  Detail & Related papers  (2024-11-10T16:40:27Z)
• Don't Cut Corners: Exact Conditions for Modularity in Biologically Inspired Representations [52.48094670415497]
  We develop a theory of when biologically inspired representations modularise with respect to source variables (sources) We derive necessary and sufficient conditions on a sample of sources that determine whether the neurons in an optimal biologically-inspired linear autoencoder modularise. Our theory applies to any dataset, extending far beyond the case of statistical independence studied in previous work.
  arXiv  Detail & Related papers  (2024-10-08T17:41:37Z)
• PhyloGFN: Phylogenetic inference with generative flow networks [57.104166650526416]
  We introduce the framework of generative flow networks (GFlowNets) to tackle two core problems in phylogenetics: parsimony-based and phylogenetic inference. Because GFlowNets are well-suited for sampling complex structures, they are a natural choice for exploring and sampling from the multimodal posterior distribution over tree topologies. We demonstrate that our amortized posterior sampler, PhyloGFN, produces diverse and high-quality evolutionary hypotheses on real benchmark datasets.
  arXiv  Detail & Related papers  (2023-10-12T23:46:08Z)
• A Generative Modeling Framework for Inferring Families of Biomechanical Constitutive Laws in Data-Sparse Regimes [0.15658704610960567]
  We propose a novel approach to efficiently infer families of relationships in data-sparse regimes. Inspired by the concept of functional priors, we develop a generative network (GAN) that incorporates a neural operator as the generator and a fully-connected network as the adversarial discriminator.
  arXiv  Detail & Related papers  (2023-05-04T22:07:27Z)
• Predicting Biomedical Interactions with Probabilistic Model Selection for Graph Neural Networks [5.156812030122437]
  Current biological networks are noisy, sparse, and incomplete. Experimental identification of such interactions is both time-consuming and expensive. Deep graph neural networks have shown their effectiveness in modeling graph-structured data and achieved good performance in biomedical interaction prediction. Our proposed method enables the graph convolutional networks to dynamically adapt their depths to accommodate an increasing number of interactions.
  arXiv  Detail & Related papers  (2022-11-22T20:44:28Z)
• EINNs: Epidemiologically-Informed Neural Networks [75.34199997857341]
  We introduce a new class of physics-informed neural networks-EINN-crafted for epidemic forecasting. We investigate how to leverage both the theoretical flexibility provided by mechanistic models as well as the data-driven expressability afforded by AI models.
  arXiv  Detail & Related papers  (2022-02-21T18:59:03Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• Generalized Shape Metrics on Neural Representations [26.78835065137714]
  We provide a family of metric spaces that quantify representational dissimilarity. We modify existing representational similarity measures based on canonical correlation analysis to satisfy the triangle inequality. We identify relationships between neural representations that are interpretable in terms of anatomical features and model performance.
  arXiv  Detail & Related papers  (2021-10-27T19:48:55Z)
• Inference of cell dynamics on perturbation data using adjoint sensitivity [4.606583317143614]
  Data-driven dynamic models of cell biology can be used to predict cell response to unseen perturbations. Recent work had demonstrated the derivation of interpretable models with explicit interaction terms. This work aims to extend the range of applicability of this model inference approach to a diversity of biological systems.
  arXiv  Detail & Related papers  (2021-04-13T19:15:56Z)
• Design of Experiments for Verifying Biomolecular Networks [12.788443087394239]
  A growing trend in molecular and synthetic biology is to use mechanistic (non machine learning) models to design biomolecular networks. These networks need to be validated by experimental results to ensure the theoretical network correctly models the true system. We propose a design of experiments approach for validating these networks efficiently.
  arXiv  Detail & Related papers  (2020-11-20T13:39:45Z)
• Network Diffusions via Neural Mean-Field Dynamics [52.091487866968286]
  We propose a novel learning framework for inference and estimation problems of diffusion on networks. Our framework is derived from the Mori-Zwanzig formalism to obtain an exact evolution of the node infection probabilities. Our approach is versatile and robust to variations of the underlying diffusion network models.
  arXiv  Detail & Related papers  (2020-06-16T18:45:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.