Thea Norman

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It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data(More)
We describe a method to decipher the complex inter-relationships between metabolite production trends and gene expression events, and show how information gleaned from such studies can be applied to yield improved production strains. Genomic fragment microarrays were constructed for the Aspergillus terreus genome, and transcriptional profiles were generated(More)
The detection of somatic mutations from cancer genome sequences is key to understanding the genetic basis of disease progression, patient survival and response to therapy. Benchmarking is needed for tool assessment and improvement but is complicated by a lack of gold standards, by extensive resource requirements and by difficulties in sharing personal(More)
Fibroblast growth factor 21 (FGF21) mitigates many of the pathogenic features of type 2 diabetes, despite a short circulating half-life. PEGylation is a proven approach to prolonging the duration of action while enhancing biophysical solubility and stability. However, in the absence of a specific protein PEGylation site, chemical conjugation is inherently(More)
The ability to computationally predict the effects of toxic compounds on humans could help address the deficiencies of current chemical safety testing. Here, we report the results from a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations. We measured the cytotoxicity(More)
Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high(More)
Acute Myeloid Leukemia (AML) is a fatal hematological cancer. The genetic abnormalities underlying AML are extremely heterogeneous among patients, making prognosis and treatment selection very difficult. While clinical proteomics data has the potential to improve prognosis accuracy, thus far, the quantitative means to do so have yet to be developed. Here we(More)
Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in(More)
Data analysis requires new approaches in many domains for evaluating tools and techniques , particularly when the data sets grow large and more complex. Evaluation–as–a– service (EaaS) was coined as a term to represent evaluation approaches based on APIs, virtual machines or source code submission, different from the common paradigm of evaluating techniques(More)
in the author list. This has now been corrected in the HTML; the PDF version of the paper was correct from the time of publication. This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise(More)