Results 31 to 40 of about 7,468,732 (318)

Gene networks inference using dynamic Bayesian networks [PDF]

Bioinformatics, 2003
Abstract This article deals with the identification of gene regulatory networks from experimental data using a statistical machine learning approach. A stochastic model of gene interactions capable of handling missing variables is proposed.
Bruno-Edouard, Perrin, Liva, Ralaivola, Aurélien, Mazurie, Samuele, Bottani, Jacques, Mallet, Florence, d'Alché-Buc   +5 more
openaire   +3 more sources

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

Frontiers in Genetics, 2019
Studying the impact of genetic variation on gene regulatory networks is essential to understand the biological mechanisms by which genetic variation causes variation in phenotypes. Bayesian networks provide an elegant statistical approach for multi-trait
Lingfei Wang, Lingfei Wang, Lingfei Wang, Pieter Audenaert, Pieter Audenaert, Tom Michoel, Tom Michoel   +6 more
doaj   +1 more source

Network Embedding Across Multiple Tissues and Data Modalities Elucidates the Context of Host Factors Important for COVID-19 Infection

Frontiers in Genetics, 2022
COVID-19 is a heterogeneous disease caused by SARS-CoV-2. Aside from infections of the lungs, the disease can spread throughout the body and damage many other tissues, leading to multiorgan failure in severe cases. The highly variable symptom severity is
Yue Hu, Yue Hu, Ghalia Rehawi, Ghalia Rehawi, Lambert Moyon, Nathalie Gerstner, Nathalie Gerstner, Christoph Ogris, Janine Knauer-Arloth, Janine Knauer-Arloth, Florian Bittner, Annalisa Marsico, Nikola S. Mueller, Nikola S. Mueller   +13 more
doaj   +1 more source

Evaluation of a Bayesian inference network for ligand-based virtual screening [PDF]

, 2009
Background Bayesian inference networks enable the computation of the probability that an event will occur. They have been used previously to rank textual documents in order of decreasing relevance to a user-defined query. Here, we modify the approach to
A Abdo, A Bender, AG Maldonado, AN Jain, AR Leach, AR Leach, Beining Chen, Christoph Mueller, CX Zhai, D Metzler, EJ Gardiner, EM Voorhees, G Salton, GW Bemis, H Eckert, H Turtle, J Bajorath, J Hert, J Hert, J-F Truchon, JA Grant, JD Holliday, JP Callan, JP Callan, JR Fischer, K Spärck Jones, K Spärck Jones, N Nikolova, P Prathipati, P Willett, P Willett, P Willett, P Willett, P Willett, Peter Willett, RC Glen, RD Brown, RP Sheridan, RP Sheridan, S Siegel, SJ Edgar, T Lengauer, T Strohman, TI Oprea, WR Greiff, X Chen   +45 more
core   +3 more sources

SCENIC: Single-cell regulatory network inference and clustering

Nature Methods, 2017
We present SCENIC, a computational method for simultaneous gene regulatory network reconstruction and cell-state identification from single-cell RNA-seq data (http://scenic.aertslab.org).
S. Aibar, C. González-Blas, Thomas Moerman, V. A. Huynh-Thu, Hana Imrichová, Gert J. Hulselmans, F. Rambow, J. Marine, P. Geurts, J. Aerts, Joost van den Oord, Z. Atak, J. Wouters, S. Aerts   +13 more
semanticscholar   +1 more source

Inference of a Boolean Network From Causal Logic Implications

Frontiers in Genetics, 2022
Biological systems contain a large number of molecules that have diverse interactions. A fruitful path to understanding these systems is to represent them with interaction networks, and then describe flow processes in the network with a dynamic model ...
Parul Maheshwari, Sarah M. Assmann, Reka Albert, Reka Albert   +3 more
doaj   +1 more source

Quantifying the ‘end of history’ through a Bayesian Markov-chain approach

Royal Society Open Science, 2022
Political regimes have been changing throughout human history. After the apparent triumph of liberal democracies at the end of the twentieth century, Francis Fukuyama and others have been arguing that humankind is approaching an ‘end of history’ (EoH) in
Florian Klimm
doaj   +1 more source

Inferring Weighted Directed Association Networks from Multivariate Time Series with the Small-Shuffle Symbolic Transfer Entropy Spectrum Method

Entropy, 2016
Complex network methodology is very useful for complex system exploration. However, the relationships among variables in complex systems are usually not clear.
Yanzhu Hu, Huiyang Zhao, Xinbo Ai
doaj   +1 more source

Accelerating Neural Network Inference on FPGA-Based Platforms—A Survey

Electronics, 2021
The breakthrough of deep learning has started a technological revolution in various areas such as object identification, image/video recognition and semantic segmentation.
Ran Wu, Xinmin Guo, Jian Du, Junbao Li
semanticscholar   +1 more source

Control Meets Inference: Using Network Control to Uncover the Behaviour of Opponents

Entropy, 2022
Using observational data to infer the coupling structure or parameters in dynamical systems is important in many real-world applications. In this paper, we propose a framework of strategically influencing a dynamical process that generates observations ...
Zhongqi Cai, Enrico Gerding, Markus Brede   +2 more
doaj   +1 more source