Statistical inference and reverse engineering of gene regulatory networks from observational expression data
In this paper, we present a systematic and conceptual overview of methods for inferring
gene regulatory networks from observational gene expression data. Further, we discuss two …
gene regulatory networks from observational gene expression data. Further, we discuss two …
Autoregressive models for gene regulatory network inference: Sparsity, stability and causality issues
G Michailidis, F d'Alché-Buc - Mathematical biosciences, 2013 - Elsevier
Reconstructing gene regulatory networks from high-throughput measurements represents a
key problem in functional genomics. It also represents a canonical learning problem and …
key problem in functional genomics. It also represents a canonical learning problem and …
GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods
Motivation: Over the last decade, numerous methods have been developed for inference of
regulatory networks from gene expression data. However, accurate and systematic …
regulatory networks from gene expression data. However, accurate and systematic …
dynGENIE3: dynamical GENIE3 for the inference of gene networks from time series expression data
VA Huynh-Thu, P Geurts - Scientific reports, 2018 - nature.com
The elucidation of gene regulatory networks is one of the major challenges of systems
biology. Measurements about genes that are exploited by network inference methods are …
biology. Measurements about genes that are exploited by network inference methods are …
SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles
N Papili Gao, SMM Ud-Dean, O Gandrillon… - …, 2018 - academic.oup.com
Motivation Single cell transcriptional profiling opens up a new avenue in studying the
functional role of cell-to-cell variability in physiological processes. The analysis of single cell …
functional role of cell-to-cell variability in physiological processes. The analysis of single cell …
Network deconvolution as a general method to distinguish direct dependencies in networks
Recognizing direct relationships between variables connected in a network is a pervasive
problem in biological, social and information sciences as correlation-based networks …
problem in biological, social and information sciences as correlation-based networks …
Integrative random forest for gene regulatory network inference
Motivation: Gene regulatory network (GRN) inference based on genomic data is one of the
most actively pursued computational biological problems. Because different types of …
most actively pursued computational biological problems. Because different types of …
A categorical semantics for causal structure
A Kissinger, S Uijlen - Logical Methods in Computer Science, 2019 - lmcs.episciences.org
We present a categorical construction for modelling causal structures within a general class
of process theories that include the theory of classical probabilistic processes as well as …
of process theories that include the theory of classical probabilistic processes as well as …
Gene regulatory network inference from sparsely sampled noisy data
The complexity of biological systems is encoded in gene regulatory networks. Unravelling
this intricate web is a fundamental step in understanding the mechanisms of life and …
this intricate web is a fundamental step in understanding the mechanisms of life and …
Fast Bayesian inference for gene regulatory networks using ScanBMA
Background Genome-wide time-series data provide a rich set of information for discovering
gene regulatory relationships. As genome-wide data for mammalian systems are being …
gene regulatory relationships. As genome-wide data for mammalian systems are being …