ReviewMolecular bioSystems2010
Network inference and network response identification: moving genome-scale data to the next level of biological discovery.
Review in Molecular bioSystems, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
16 citing papers in PubMed.
- Microbial influencers and cotton leaf curl disease (CLCuD) susceptibility: a network perspective.Frontiers in microbiology · 2024Article
- Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge.Frontiers in bioinformatics · 2021Article
- Mapping the microbial interactome: Statistical and experimental approaches for microbiome network inference.Experimental biology and medicine (Maywood, N.J.) · 2019Review
- Slow relaxation during and after perturbation of bistable kinetics of gene expression.European biophysics journal : EBJ · 2019Article
- Network Modeling Unravels Mechanisms of Crosstalk between Ethylene and Salicylate Signaling in Potato.Plant physiology · 2018Article
- Integration of heterogeneous molecular networks to unravel gene-regulation in Mycobacterium tuberculosis.BMC systems biology · 2014Article
- Unraveling the regulatory connections between two controllers of breast cancer cell fate.Nucleic acids research · 2014Article
- Reverse engineering a mouse embryonic stem cell-specific transcriptional network reveals a new modulator of neuronal differentiation.Nucleic acids research · 2013Article
- Dynamic transcription factor networks in epithelial-mesenchymal transition in breast cancer models.PloS one · 2013Article
- Microbial interactions: from networks to models.Nature reviews. Microbiology · 2012Review
- Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks.BMC systems biology · 2012Article
- A network-based, integrative study to identify core biological pathways that drive breast cancer clinical subtypes.British journal of cancer · 2012Article
- Signal correlations in ecological niches can shape the organization and evolution of bacterial gene regulatory networks.Advances in microbial physiology · 2012Review
- Robust detection of hierarchical communities from Escherichia coli gene expression data.PLoS computational biology · 2012Article
- Network reconstruction reveals new links between aging and calorie restriction in yeast.HFSP journal · 2010Article
- Network representations of immune system complexity.Wiley interdisciplinary reviews. Systems biology and medicineReview
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The escalating amount of genome-scale data demands a pragmatic stance from the research community. How can we utilize this deluge of information to better understand biology, cure diseases, or engage cells in bioremediation or biomaterial production for various purposes? A research pipeline moving new sequence, expression and binding data towards practical end goals seems to be necessary. While most individual researchers are not motivated by such well-articulated pragmatic end goals, the scientific community has already self-organized itself to successfully convert genomic data into fundamentally new biological knowledge and practical applications. Here we review two important steps in this workflow: network inference and network response identification, applied to transcriptional regulatory networks. Among network inference methods, we concentrate on relevance networks due to their conceptual simplicity. We classify and discuss network response identification approaches as either data-centric or network-centric. Finally, we conclude with an outlook on what is still missing from these approaches and what may be ahead on the road to biological discovery.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.