Publications by authors named "Alexandra Keenan"

Post-stroke cognitive impairment, depression, and fatigue are common, persistent, and disabling. This review summarizes current knowledge on the pathophysiology, assessment, and management of these debilitating neuropsychiatric sequelae of stroke. We briefly review evolving knowledge on the neural mechanisms and risk factors for each condition.

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Utilization of high-quality clinical practice guidelines has the potential to positively impact health outcomes. This study aimed to assess the quality and content concordance of national and international recommendations on hypertensive disorders of pregnancy (HDPs). Searches were conducted of the MEDLINE database and reference lists generated from national and international agencies.

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Identifying the transcription factors (TFs) responsible for observed changes in gene expression is an important step in understanding gene regulatory networks. ChIP-X Enrichment Analysis 3 (ChEA3) is a transcription factor enrichment analysis tool that ranks TFs associated with user-submitted gene sets. The ChEA3 background database contains a collection of gene set libraries generated from multiple sources including TF-gene co-expression from RNA-seq studies, TF-target associations from ChIP-seq experiments, and TF-gene co-occurrence computed from crowd-submitted gene lists.

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The frequency by which genes are studied correlates with the prior knowledge accumulated about them. This leads to an imbalance in research attention where some genes are highly investigated while others are ignored. Geneshot is a search engine developed to illuminate this gap and to promote attention to the under-studied genome.

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High-throughput experiments produce increasingly large datasets that are difficult to analyze and integrate. While most data integration approaches focus on aligning metadata, data integration can be achieved by abstracting experimental results into gene sets. Such gene sets can be made available for reuse through gene set enrichment analysis tools such as Enrichr.

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Elucidating brain cell type specific gene expression patterns is critical towards a better understanding of how cell-cell communications may influence brain functions and dysfunctions. We set out to compare and contrast five human and murine cell type-specific transcriptome-wide RNA expression data sets that were generated within the past several years. We defined three measures of brain cell type-relative expression including specificity, enrichment, and absolute expression and identified corresponding consensus brain cell "signatures," which were well conserved across data sets.

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While gene expression data at the mRNA level can be globally and accurately measured, profiling the activity of cell signaling pathways is currently much more difficult. eXpression2Kinases (X2K) computationally predicts involvement of upstream cell signaling pathways, given a signature of differentially expressed genes. X2K first computes enrichment for transcription factors likely to regulate the expression of the differentially expressed genes.

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RNA sequencing (RNA-seq) is the leading technology for genome-wide transcript quantification. However, publicly available RNA-seq data is currently provided mostly in raw form, a significant barrier for global and integrative retrospective analyses. ARCHS4 is a web resource that makes the majority of published RNA-seq data from human and mouse available at the gene and transcript levels.

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Motivation: As part of the NIH Library of Integrated Network-based Cellular Signatures program, hundreds of thousands of transcriptomic signatures were generated with the L1000 technology, profiling the response of human cell lines to over 20 000 small molecule compounds. This effort is a promising approach toward revealing the mechanisms-of-action (MOA) for marketed drugs and other less studied potential therapeutic compounds.

Results: L1000 fireworks display (L1000FWD) is a web application that provides interactive visualization of over 16 000 drug and small-molecule induced gene expression signatures.

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The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program that catalogs how human cells globally respond to chemical, genetic, and disease perturbations. Resources generated by LINCS include experimental and computational methods, visualization tools, molecular and imaging data, and signatures. By assembling an integrated picture of the range of responses of human cells exposed to many perturbations, the LINCS program aims to better understand human disease and to advance the development of new therapies.

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The parasitic protozoan, Leishmania, survives in harsh environments within its mammalian and sand fly hosts. Secreted proteins likely play critical roles in the parasite's interactions with its environment. As a preliminary identification of the spectrum of potential excreted/secreted (ES) proteins of Leishmania infantum chagasi (Lic), a causative agent of visceral leishmaniasis, we used standard algorithms to screen the annotated L.

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