Bayesian Factor Analysis for Binary and Ordinal Phenotypes with Missingness
This paper introduces FABOr, a Bayesian framework for matrix factorization of binary and ordinal phenotypes, showing improved imputation accuracy on real-world data.
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New York Genome CenterORCID 0000-0003-4345-6465Unclaimed ORCID profile · follow to get notified of new preprints
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This paper introduces FABOr, a Bayesian framework for matrix factorization of binary and ordinal phenotypes, showing improved imputation accuracy on real-world data.
Adrien T. Stanley, Se Joon Choi, Anika K. Frank…·David L. Sulzer
The paper shows that sound-safety association enhances striatal neuron responses to learned sounds through postsynaptic changes in direct pathway SPNs.
D. J. Vidyadhara, Mahalakshmi Somayaji, Nigel Wade…·Sreeganga S. Chandra
This study shows that auxilin deficiency causes Parkinson's disease by disrupting dopamine transporter function and synaptic vesicle sorting, leading to dopamine homeostasis disruption and neurodegeneration.
N Shashaank, Mahalakshmi Somayaji, Mattia Miotto…·David L. Sulzer
The paper presents three computational models to analyze fast-scan cyclic voltammetry data from mice, quantifying presynaptic dopamine kinetics and the role of synucleins in neurotransmission.
Stefano Cataldi, Clay Lacefield, N Shashaank…·David L. Sulzer
The paper shows that decreased DMS direct pathway neuronal activity, dependent on dopamine, is required for motor coordination learning, while DLS activity remains stable and correlates with skill maintenance.