Literature Lab™ has been part of cutting-edge research. 

"Literature Lab is an easy to use automated functional analysis and data mining tool uniquely suited to uncover associations and co-occurrences among diseases, pathways, chemical actions, etc. in the literature. It was of great use to us in a recent study to uncover shared genetic etiology among allergic diseases”

Tesfaye Mersha, Assistant Professor, University of Cincinnati, Department of Pediatrics.


Altman MC et al. A Novel Repertoire of Blood Transcriptome Modules Based on Co-expression Patterns Across Sixteen Disease and Physiological States. Presently on bioRixiv.org, to be published in 2019.

Kraja A et al. (2018) Associations of Mitochondrial and Nuclear Mitochondrial Variants and Genes with Seven Metabolic Traits. American Journal of Human Genetics 2018.12.001

Singhania A et al. (2018) A modular transcriptional signature identifies phenotypic heterogeneity of human tuberculosis infection. Nature Communications 9, Article Number 2308

Feitosa MF et al. (2018) Novel genetic associations for blood pressure identified via gene-alcohol interaction in up to 570K individuals across multiple ancestries. PLOS One 13(6): e0198166

Sung YJ et al. (2018) A Large-Scale Multi-ancestry Genome-wide Study Accounting for Smoking Behavior Identifies Multiple Significant Loci for Blood Pressure. American Journal of human genetics 102(3):375-400

Gautier JF et al. (2017) Kidney Dysfunction in Adult Offspring Exposed In Utero to Type 1 Diabetes Is Associated with Alterations in Genome-Wide DNA Methylation. PLOS One 10(8): e0134654

D Chaussabel et al. (2015). A narrow repertoire of transcriptional modules responsive to pyogenic bacteria is impaired in patients carrying loss-of-function mutations in MYD88 or IRAK4. Nature Immunology. 15(12): 1134 - 1142

Liu C, Kraja AT et al. (2016). Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci. Nature Genetics. 48(10): 1162-70

Gupta J, Johansson E, et al. (2016). Resolving the etiology of atopic disorders by using genetic analysis of racial ancestry. Journal of Allergy and Clinical Immunology. 138(3): 676-99.

Ghosh D, Ding L, et al. (2015). Multiple Transcriptome Data Analysis Reveals Biologically Relevant Atopic Dermatitis Signature Genes and Pathways. PLOS One. 10(12): e0144316.

A.T. Kraja, et al. (2014). Pleiotropic genes for metabolic syndrome and inflammation. Molecular Genetics of Metabolism. 112(4): 317-338.

Febbo P, Mulligan M, et al. (2007). Literature Lab: a method of automated literature interrogation to infer biology from microarray analysis. BMC Genomics. 8:461.