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Bianca Dumitrascu
Researcher at Princeton University
Publications - 31
Citations - 384
Bianca Dumitrascu is an academic researcher from Princeton University. The author has contributed to research in topics: Computer science & Thompson sampling. The author has an hindex of 9, co-authored 25 publications receiving 197 citations. Previous affiliations of Bianca Dumitrascu include University of Cambridge & Massachusetts Institute of Technology.
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netNMF-sc: leveraging gene-gene interactions for imputation and dimensionality reduction in single-cell expression analysis.
TL;DR: NetNMF-sc as mentioned in this paper learns a low-dimensional representation of scRNA-seq transcript counts using network-regularized nonnegative matrix factorization, which encourages pairs of genes with known interactions to be nearby each other in the lowdimensional representation.
Posted Content
Sparse Multi-Output Gaussian Processes for Medical Time Series Prediction
Li-Fang Cheng,Gregory Darnell,Bianca Dumitrascu,Corey Chivers,Michael Draugelis,Kai Li,Barbara E. Engelhardt +6 more
TL;DR: This work proposes MedGP, a statistical framework that incorporates 24 clinical and lab covariates and supports a rich reference data set from which relationships between observed covariates may be inferred and exploited for high-quality inference of patient state over time.
Journal ArticleDOI
Optimal marker gene selection for cell type discrimination in single cell analyses
TL;DR: In this article, the authors proposed a method for supervised genetic marker selection using linear programming and provided a Python package scGeneFit that implements this approach, which selects gene markers that jointly optimize cell label recovery using label-aware compressive classification methods.
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PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits
TL;DR: Polya-Gamma augmented Thompson sampling (PG-TS) as mentioned in this paper is a Bayesian formulation of contextual bandits with near-optimal performance, where a learner decides among sequential actions or arms given their respective contexts to maximize binary rewards.
Posted ContentDOI
Optimal gene selection for cell type discrimination in single cell analyses
TL;DR: Given single cell RNA-seq data and a set of cellular labels to discriminate, scGene-Fit selects gene transcript markers that jointly optimize cell label recovery using label-aware compressive classification methods, resulting in a substantially more robust and less redundant set of markers.