Functional analysis of the most informative genes provides mechanistic insights and palpable hypotheses regarding their role in each environmental or genetic context. We then apply an ensemble of various machine learning algorithms to infer environmental and cellular information such as strain, growth phase, medium, oxygen level, antibiotic and carbon source. Here, we have constructed an extensive transcriptome compendium of Escherichia coli that we have further enriched via an iterative learning approach. It is yet unclear, however, how much information can be efficiently extracted and how it can be used to classify new samples with respect to their environmental and genetic characteristics. The transcriptional profile of an organism contains clues about the environmental context in which it has evolved and currently lives, its behavior and cellular state. This work demonstrates the degree at which genome-scale transcriptional information can be predictive of latent, heterogeneous and seemingly disparate phenotypic and environmental characteristics, with far-reaching applications. Further experimental phenotypic-to-genotypic mapping that we conducted for knock-out mutants argues for the information content of top-ranked genes. Functional analysis of the respective genetic signatures implicates a wide spectrum of Gene Ontology terms and KEGG pathways with condition-specific information content, including iron transport, transferases, and enterobactin synthesis. Contrary to expectations, only 59% of the top informative genes were also identified as differentially expressed under the respective conditions. Interestingly, this performance can be significantly boosted when environmental and strain characteristics are simultaneously considered, as a composite classifier that captures the inter-dependencies of three characteristics (medium, phase and strain) achieved 10.6% (☑.0%) higher performance than any individual models. Results show that gene expression is an excellent predictor of environmental structure, with multi-class ensemble models achieving balanced accuracy between 70.0% (☓.5%) to 98.3% (☒.3%) for the various characteristics. We then constructed an ensemble method to predict environmental and cellular state, including strain, growth phase, medium, oxygen level, antibiotic and carbon source presence. To investigate this relationship, we created an extensive normalized gene expression compendium for the bacterium Escherichia coli that was further enriched with meta-information through an iterative learning procedure. A tantalizing question in cellular physiology is whether the cellular state and environmental conditions can be inferred by the expression signature of an organism.