ArticleNucleic acids research2011
LLM3D: a log-linear modeling-based method to predict functional gene regulatory interactions from genome-wide expression data.
Article in Nucleic acids research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 23 citations in OpenAlex.
- Article
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- The Jun-dependent axon regeneration gene program: Jun promotes regeneration over plasticity.Human molecular genetics · 2022Article
- Age-related loss of axonal regeneration is reflected by the level of local translation.Experimental neurology · 2021Article
- Myelin basic protein enhances axonal regeneration from neural progenitor cells.Cell & bioscience · 2021Article
- Hominin-specific regulatory elements selectively emerged in oligodendrocytes and are disrupted in autism patients.Nature communications · 2020Article
- Epigenomic signatures underpin the axonal regenerative ability of dorsal root ganglia sensory neurons.Nature neuroscience · 2019Article
- The Dorsal Column Lesion Model of Spinal Cord Injury and Its Use in Deciphering the Neuron-Intrinsic Injury Response.Developmental neurobiology · 2018Review
- Combined chondroitinase and KLF7 expression reduce net retraction of sensory and CST axons from sites of spinal injury.Neurobiology of disease · 2017Article
- Epigenetic profiling reveals a developmental decrease in promoter accessibility during cortical maturationNeuroepigenetics · 2016Article
- What makes a RAG regeneration associated?Frontiers in molecular neuroscience · 2015Review
- Comparison of targeted maximum likelihood and shrinkage estimators of parameters in gene networks.Statistical applications in genetics and molecular biology · 2012Article
- Achieving CNS axon regeneration by manipulating convergent neuro-immune signaling.Cell and tissue research · 2012Review
- A gene network perspective on axonal regeneration.Frontiers in molecular neuroscience · 2011Article
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
All cellular processes are regulated by condition-specific and time-dependent interactions between transcription factors and their target genes. While in simple organisms, e.g. bacteria and yeast, a large amount of experimental data is available to support functional transcription regulatory interactions, in mammalian systems reconstruction of gene regulatory networks still heavily depends on the accurate prediction of transcription factor binding sites. Here, we present a new method, log-linear modeling of 3D contingency tables (LLM3D), to predict functional transcription factor binding sites. LLM3D combines gene expression data, gene ontology annotation and computationally predicted transcription factor binding sites in a single statistical analysis, and offers a methodological improvement over existing enrichment-based methods. We show that LLM3D successfully identifies novel transcriptional regulators of the yeast metabolic cycle, and correctly predicts key regulators of mouse embryonic stem cell self-renewal more accurately than existing enrichment-based methods. Moreover, in a clinically relevant in vivo injury model of mammalian neurons, LLM3D identified peroxisome proliferator-activated receptor γ (PPARγ) as a neuron-intrinsic transcriptional regulator of regenerative axon growth. In conclusion, LLM3D provides a significant improvement over existing methods in predicting functional transcription regulatory interactions in the absence of experimental transcription factor binding data.
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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 Socratic graph.