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A Novel Framework for Predicting In Vivo Toxicities from In Vitro Data Using Optimal Methods for Dense and Sparse Matrix Reordering and Logistic Regression

In this work, we combine the strengths of mixed-integer linear optimization (MILP) and logistic regression for predicting the in vivo toxicity of chemicals using only their measured in vitro assay data. The proposed approach utilizes a biclustering method based on iterative optimal reordering (DiMag...

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Bibliografische gegevens
Hoofdauteurs: DiMaggio, Peter A., Subramani, Ashwin, Judson, Richard S., Floudas, Christodoulos A.
Formaat: Artigo
Taal:Inglês
Gepubliceerd in: Oxford University Press 2010
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Online toegang:https://ncbi.nlm.nih.gov/pmc/articles/PMC2955210/
https://ncbi.nlm.nih.gov/pubmed/20702588
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/toxsci/kfq233
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