Integrating Systems Biology Sources Illuminates Drug Action
CLINICAL PHARMACOLOGY & THERAPEUTICS
2014; 95 (6): 663-669
Reconstruction of the Mouse Otocyst and Early Neuroblast Lineage at Single-Cell Resolution
2014; 157 (4): 964-978
There are significant gaps in our understanding of the pathways by which drugs act. This incomplete knowledge limits our ability to use mechanistic molecular information rationally to repurpose drugs, understand their side effects, and predict their interactions with other drugs. Here, we present DrugRouter, a novel method for generating drug-specific pathways of action by linking target genes, disease genes, and pharmacogenes using gene interaction networks. We construct pathways for more than a hundred drugs and show that the genes included in our pathways (i) co-occur with the query drug in the literature, (ii) significantly overlap or are adjacent to known drug-response pathways, and (iii) are adjacent to genes that are hits in genome-wide association studies assessing drug response. Finally, these computed pathways suggest novel drug-repositioning opportunities (e.g., statins for follicular thyroid cancer), gene-side effect associations, and gene-drug interactions. Thus, DrugRouter generates hypotheses about drug actions using systems biology data.
View details for DOI 10.1038/clpt.2014.51
View details for Web of Science ID 000336415300030
View details for PubMedID 24577151
A method for inferring medical diagnoses from patient similarities.
2013; 11: 194-?
The otocyst harbors progenitors for most cell types of the mature inner ear. Developmental lineage analyses and gene expression studies suggest that distinct progenitor populations are compartmentalized to discrete axial domains in the early otocyst. Here, we conducted highly parallel quantitative RT-PCR measurements on 382 individual cells from the developing otocyst and neuroblast lineages to assay 96 genes representing established otic markers, signaling-pathway-associated transcripts, and novel otic-specific genes. By applying multivariate cluster, principal component, and network analyses to the data matrix, we were able to readily distinguish the delaminating neuroblasts and to describe progressive states of gene expression in this population at single-cell resolution. It further established a three-dimensional model of the otocyst in which each individual cell can be precisely mapped into spatial expression domains. Our bioinformatic modeling revealed spatial dynamics of different signaling pathways active during early neuroblast development and prosensory domain specification. PAPERFLICK:
View details for DOI 10.1016/j.cell.2014.03.036
View details for Web of Science ID 000335765500022
Clinical decision support systems assist physicians in interpreting complex patient data. However, they typically operate on a per-patient basis and do not exploit the extensive latent medical knowledge in electronic health records (EHRs). The emergence of large EHR systems offers the opportunity to integrate population information actively into these tools.Here, we assess the ability of a large corpus of electronic records to predict individual discharge diagnoses. We present a method that exploits similarities between patients along multiple dimensions to predict the eventual discharge diagnoses.Using demographic, initial blood and electrocardiography measurements, as well as medical history of hospitalized patients from two independent hospitals, we obtained high performance in cross-validation (area under the curve >0.88) and correctly predicted at least one diagnosis among the top ten predictions for more than 84% of the patients tested. Importantly, our method provides accurate predictions (>0.86 precision in cross validation) for major disease categories, including infectious and parasitic diseases, endocrine and metabolic diseases and diseases of the circulatory systems. Our performance applies to both chronic and acute diagnoses.Our results suggest that one can harness the wealth of population-based information embedded in electronic health records for patient-specific predictive tasks.
View details for DOI 10.1186/1741-7015-11-194
View details for PubMedID 24004670