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|Alternative names||Time Series Network Identification, TSNI-Integral|
|Interface||Command line interface|
|Restrictions to use||Academic or non-commercial use|
|Input data||A matrix that contains the experimental expression values.|
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- person_outline Diego di Bernardo
- person_outline Caterina Missero
Publications for Time Series Network Identification
DTNI: a novel toxicogenomics data analysis tool for identifying the molecular mechanisms underlying the adverse effects of toxic compounds
[…] 2) an indirect gene–gene interaction (through one or more intermediates); (3) both genes affected by one or more of the studied compounds.The ROC curves for the simulations show that DTNI outperforms TSNI in inferring a network induced by a toxicant. Moreover, DTNI allows including data from multiple perturbations (compounds), while only one perturbation can be included within TSNI. Including mult […]
Inferring Broad Regulatory Biology from Time Course Data: Have We Reached an Upper Bound under Constraints Typical of In Vivo Studies?
[…] t typically available from in vivo time course studies and that also support the resolution required to simulate treatment kinetics. Based on our survey of methods, we found ODE-based algorithms like TSNI , TSNI integral  and the one proposed in Yeung et al. , most suitable. The algorithm proposed in  and TSNI use similar techniques of Singular Value Decomposition (SVD) and Principal Compo […]
CaSPIAN: A Causal Compressive Sensing Algorithm for Discovering Directed Interactions in Gene Networks
[…] to galactose), measured via quantitative real-time PCR (q-PCR) every minutes for up to hours, within five experiments. The performance of two additional algorithms utilizing sparsity and causality, TSNI and BANJO , were evaluated in using the same dataset. TSNI is an algorithm based on modeling networks via ordinary differential equations (ODE), while BANJO is an algorithm based on Bayesian ne […]
Network based elucidation of drug response: from modulators to targets
[…] ts following inducible activation of the transcription factor using microarrays. TSNI was then applied to the collected GEPs to identify the direct targets of the transcription factor. More recently, TSNI was applied to identify the transcriptional target of Id proteins following inducible deletion of Id genes in murine Neuron Stem Cells .Other methods using time-course gene expression profiles […]
Integrating external biological knowledge in the construction of regulatory networks from time series expression data
[…] applied perturbations. ODE-based methods can be broadly classified into two categories, depending on whether the gene expressions are measured at steady state [-] or over time [-]. As an example, the TSNI (Time Series Network Identification) algorithm used ODEs to model time series expression data subject to an external perturbation . To handle the dimensionality challenge (i.e. the number of ob […]
Reconstructing genome wide regulatory network of E. coli using transcriptome data and predicted transcription factor activities
[…] s transcriptome data to develop dynamic models . These include network identification by multiple regression , microarray network identification  and multi-scale time-correlation estimation . time-series network identification , directed information-based CLR . Dynamic Bayesian network models use a Bayesian Framework to reconstruct gene regulatory networks [,].Time-series based algorit […]
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