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Kara L. Davis

Researcher at Stanford University

Publications -  98
Citations -  9648

Kara L. Davis is an academic researcher from Stanford University. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 20, co-authored 66 publications receiving 6382 citations. Previous affiliations of Kara L. Davis include Lucile Packard Children's Hospital & Thomas Jefferson University.

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Tisagenlecleucel in Children and Young Adults with B-Cell Lymphoblastic Leukemia

TL;DR: In this global study of CAR T‐cell therapy, a single infusion of tisagenlecleucel provided durable remission with long‐term persistence in pediatric and young adult patients with relapsed or refractory B‐cell ALL, with transient high‐grade toxic effects.
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Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis.

TL;DR: Using hematopoietic progenitors, a signaling-based measure of cellular phenotype was defined, which led to isolation of a gene expression signature that was predictive of survival in independent cohorts, yielding insights into AML pathophysiology.
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viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia

TL;DR: In this article, the authors present viSNE, a tool that allows one to map high-dimensional cytometry data onto two dimensions, yet conserve the highdimensional structure of the data by using all pairwise distances in high dimension to determine each cell's location in the plot.
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Single-Cell Trajectory Detection Uncovers Progression and Regulatory Coordination in Human B Cell Development

TL;DR: This study provides a comprehensive analysis of human B lymphopoiesis, laying a foundation to apply this approach to other tissues and "corrupted" developmental processes including cancer.
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Automated mapping of phenotype space with single-cell data

TL;DR: X-shift is presented, an algorithm that processes data sets using fast k-nearest-neighbor estimation of cell event density and arranges populations by marker-based classification and enables automated cell-subset clustering and access to biological insights that 'prior knowledge' might prevent the researcher from discovering.