Review of causal discovery methods based on graphical models
A fundamental task in various disciplines of science, including biology, is to find underlying
causal relations and make use of them. Causal relations can be seen if interventions are …
causal relations and make use of them. Causal relations can be seen if interventions are …
A survey of Bayesian Network structure learning
Abstract Bayesian Networks (BNs) have become increasingly popular over the last few
decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology …
decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology …
Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
We present a systematic evaluation of state-of-the-art algorithms for inferring gene
regulatory networks from single-cell transcriptional data. As the ground truth for assessing …
regulatory networks from single-cell transcriptional data. As the ground truth for assessing …
A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics
J Schäfer, K Strimmer - Statistical applications in genetics and …, 2005 - degruyter.com
Inferring large-scale covariance matrices from sparse genomic data is an ubiquitous
problem in bioinformatics. Clearly, the widely used standard covariance and correlation …
problem in bioinformatics. Clearly, the widely used standard covariance and correlation …
Disentangling direct from indirect relationships in association networks
Networks are vital tools for understanding and modeling interactions in complex systems in
science and engineering, and direct and indirect interactions are pervasive in all types of …
science and engineering, and direct and indirect interactions are pervasive in all types of …
Revealing strengths and weaknesses of methods for gene network inference
Numerous methods have been developed for inferring gene regulatory networks from
expression data, however, both their absolute and comparative performance remain poorly …
expression data, however, both their absolute and comparative performance remain poorly …
Molecular networks in Network Medicine: Development and applications
Network Medicine applies network science approaches to investigate disease
pathogenesis. Many different analytical methods have been used to infer relevant molecular …
pathogenesis. Many different analytical methods have been used to infer relevant molecular …
An empirical Bayes approach to inferring large-scale gene association networks
J Schäfer, K Strimmer - Bioinformatics, 2005 - academic.oup.com
Motivation: Genetic networks are often described statistically using graphical models (eg
Bayesian networks). However, inferring the network structure offers a serious challenge in …
Bayesian networks). However, inferring the network structure offers a serious challenge in …
Industrial process monitoring in the big data/industry 4.0 era: From detection, to diagnosis, to prognosis
MS Reis, G Gins - Processes, 2017 - mdpi.com
We provide a critical outlook of the evolution of Industrial Process Monitoring (IPM) since its
introduction almost 100 years ago. Several evolution trends that have been structuring IPM …
introduction almost 100 years ago. Several evolution trends that have been structuring IPM …
The properties of high-dimensional data spaces: implications for exploring gene and protein expression data
High-throughput genomic and proteomic technologies are widely used in cancer research to
build better predictive models of diagnosis, prognosis and therapy, to identify and …
build better predictive models of diagnosis, prognosis and therapy, to identify and …