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SL Finder
Proposes a targeted enumeration procedure for identification of synthetic lethal (SL) genes or reactions using genome-scale metabolic models. SL Finder relies on the solution of a bilevel optimization framework that utilizes flux balance analysis to identify all multi-reaction/gene lethals. The user needs to first specify a parameter n, indicating the order of synthetic lethals. This bilevel formulation then identifies the set of n gene/reaction deletions that minimizes the maximum biomass formation potential of the network. If the minimal value of the maximum biomass is found to be below a pre-specified viability threshold (e.g., one percent of maximum biomass) then the corresponding combination of n gene/reaction deletions forms a SL. All alternative SL gene/reaction sets of size n are successively obtained by excluding the previously identified SLs using integer cuts and resolving the bilevel formulation.
Cell line recognition
Cell line recognition and normalization system, supporting corpora and tagged documents. The aim is to create corpora that is suitable for training and evaluating machine learning systems to recognize and normalize established cell line names from text. We created two manually annotated corpora, Gellus and CLL. Gellus is suitable for the training of any machine learning systems in recognizing cell line name mentions while CLL is for evaluating the systems in recognizing the Cellosaurus cell line names.
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