Principles and challenges of modeling temporal and spatial omics data
Studies with temporal or spatial resolution are crucial to understand the molecular dynamics
and spatial dependencies underlying a biological process or system. With advances in high …
and spatial dependencies underlying a biological process or system. With advances in high …
Studying and modelling dynamic biological processes using time-series gene expression data
Biological processes are often dynamic, thus researchers must monitor their activity at
multiple time points. The most abundant source of information regarding such dynamic …
multiple time points. The most abundant source of information regarding such dynamic …
Estimating Granger causality from Fourier and wavelet transforms of time series data
Experiments in many fields of science and engineering yield data in the form of time series.
The Fourier and wavelet transform-based nonparametric methods are used widely to study …
The Fourier and wavelet transform-based nonparametric methods are used widely to study …
TimeDelay-ARACNE: Reverse engineering of gene networks from time-course data by an information theoretic approach
P Zoppoli, S Morganella, M Ceccarelli - BMC bioinformatics, 2010 - Springer
Background One of main aims of Molecular Biology is the gain of knowledge about how
molecular components interact each other and to understand gene function regulations …
molecular components interact each other and to understand gene function regulations …
Discovering graphical Granger causality using the truncating lasso penalty
A Shojaie, G Michailidis - Bioinformatics, 2010 - academic.oup.com
Motivation: Components of biological systems interact with each other in order to carry out
vital cell functions. Such information can be used to improve estimation and inference, and …
vital cell functions. Such information can be used to improve estimation and inference, and …
[HTML][HTML] A review of causal inference for biomedical informatics
S Kleinberg, G Hripcsak - Journal of biomedical informatics, 2011 - Elsevier
Causality is an important concept throughout the health sciences and is particularly vital for
informatics work such as finding adverse drug events or risk factors for disease using …
informatics work such as finding adverse drug events or risk factors for disease using …
Transcriptome data are insufficient to control false discoveries in regulatory network inference
Inference of causal transcriptional regulatory networks (TRNs) from transcriptomic data
suffers notoriously from false positives. Approaches to control the false discovery rate (FDR) …
suffers notoriously from false positives. Approaches to control the false discovery rate (FDR) …
Grouped graphical Granger modeling for gene expression regulatory networks discovery
We consider the problem of discovering gene regulatory networks from time-series
microarray data. Recently, graphical Granger modeling has gained considerable attention …
microarray data. Recently, graphical Granger modeling has gained considerable attention …
Modeling gene expression regulatory networks with the sparse vector autoregressive model
Background To understand the molecular mechanisms underlying important biological
processes, a detailed description of the gene products networks involved is required. In …
processes, a detailed description of the gene products networks involved is required. In …
Network inference with Granger causality ensembles on single-cell transcriptomics
Cellular gene expression changes throughout a dynamic biological process, such as
differentiation. Pseudotimes estimate cells' progress along a dynamic process based on …
differentiation. Pseudotimes estimate cells' progress along a dynamic process based on …