Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks

By D. di Bernardo, Michael J. Thompson, T. Gardner, Sarah E. Chobot, Erin L Eastwood, Andrew P. Wojtovich, S. Elliott, S. Schaus, James J. Collins. Nature Biotechnology, 2005

391 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/89717cd3cc32565227e144b4a577679d7c78c592

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, methodology): “…discriminating the primary targets from secondarily affected network nodes help in identifying control nodes directing network dynamics (Gardner et al., 2003; di Bernardo et al., 2005; Hallén et al., 2006; Lamb et al., 2006; Xing & Gardner, 2006; Lehár et al., 2007; Madhamshettiwar et al., 2012).”
Gene expression signature-based chemical genomic prediction identifies a novel class of HSP90 pathway modulators. (2006, positive): “Recent work has applied unbiased gene expression-based approaches to prediction of chemical activity and targets in bacteria and yeast ( di Bernardo et al., 2005; Gardner et al., 2003; Parsons et al., 2004).”
Gene regulatory network inference: Data integration in dynamic models - A review (2009, methodology): “For instance, k-means and fuzzy c-means (Granzow et al., 2001; Dougherty et al., 2002) were used in conjunction with GRN inference (Guthke et al., 2005).”
How to infer gene networks from expression profiles (2007, methodology): “The MNI algorithm (di Bernardo et al, 2005) is based on equation (7) as well, and uses steady-state data like NIR, but importantly, each microarray experiment can result from any kind of perturbation, that is, we do not require knowledge of biu.”
A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches. (2009, positive): “Successful applications of these approaches have been demonstrated in bacteria, yeast, and, recently, in mammalian systems (Basso et al., 2005; Della Gatta et al., 2008; di Bernardo et al., 2005; Faith et al., 2007; Gardner et al., 2003).”
Next-Generation Machine Learning for Biological Networks. (2018, positive): “Such approaches have been highly successful in the characterization of drug mechanism of action (di Bernardo et al., 2005; Bisikirska et al., 2016; Costello et al., 2014) or drivers of disease states (e.g., Akavia et al., 2010; Mezlini and Goldenberg, 2017).”
Statistical strategies for avoiding false discoveries in metabolomics and related experiments (2007, background): “…that most interventions in pathways have their greatest effect on pathway elements that are nearest to the site of intervention , such that expression profiles may indeed be used to infer them (di Bernardo et al., 2005), although this is not a watertight principle (Westerhoff and Kell, 1987).)”
Discovery of drug mode of action and drug repositioning from transcriptional responses (2010, neutral): “We remark that, differently from other methods, whose aim is to identify the specific drug substrates (2, 6), our approach also groups together compounds interacting with distinct members of the same pathway.”