DISCOVERING PATIENT PHENOTYPES USING GENERALIZED LOW RANK MODELS.

The practice of medicine is predicated on discovering commonalities or distinguishing characteristics among patients to inform corresponding treatment. Given a patient grouping (hereafter referred to as a phenotype), clinicians can implement a treatment pathway accounting for the underlying cause of disease in that phenotype. Traditionally, phenotypes have been discovered by intuition, experience in practice, and advancements in basic science, but these approaches are often heuristic, labor intensive, and can take decades to produce actionable knowledge.

Incorporation of flexible objectives and time-linked simulation with flux balance analysis.

We present two modifications of the flux balance analysis (FBA) metabolic modeling framework which relax implicit assumptions of the biomass reaction. Our flexible flux balance analysis (flexFBA) objective removes the fixed proportion between reactants, and can therefore produce a subset of biomass reactants. Our time-linked flux balance analysis (tFBA) simulation removes the fixed proportion between reactants and byproducts, and can therefore describe transitions between metabolic steady states.

Analyzing patterns of drug use in clinical notes for patient safety.

Doctors prescribe drugs for indications that are not FDA approved. Research indicates that 21% of prescriptions filled are for off-label indications. Of those, more than 73% lack supporting scientific evidence.