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A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles
We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs, and disease states are connected by virtue of common gene-expression signatures. Here, we report more than aExpand
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The Drug Repurposing Hub: a next-generation drug library and information resource
To the Editor: Drug repurposing, the application of an existing therapeutic to a new disease indication, holds promise of rapid clinical impact at a lower cost than de novo drug development. So far,Expand
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Distinct time scales in cortical discrimination of natural sounds in songbirds.
Understanding how single cortical neurons discriminate between sensory stimuli is fundamental to providing a link between cortical neural responses and perception. The discrimination of sensoryExpand
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Tight Coordination of Protein Translation and HSF1 Activation Supports the Anabolic Malignant State
Introduction Ribosome biogenesis is commonly up-regulated to satisfy the increased anabolic demands associated with malignant transformation and tumor growth. Many different oncogenic signalingExpand
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A melanocyte lineage program confers resistance to MAP kinase pathway inhibition
Malignant melanomas harbouring point mutations (Val600Glu) in the serine/threonine-protein kinase BRAF (BRAF(V600E)) depend on RAF–MEK–ERK signalling for tumour cell growth. RAF and MEK inhibitorsExpand
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A Next Generation Connectivity Map: L1000 Platform And The First 1,000,000 Profiles
We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs and disease states are connected by virtue of common gene-expression signatures. Here, we report more than aExpand
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Cortical Discrimination of Complex Natural Stimuli: Can Single Neurons Match Behavior?
A central finding in many cortical areas is that single neurons can match behavioral performance in the discrimination of sensory stimuli. However, whether this is true for natural behaviorsExpand
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Gene expression inference with deep learning
TLDR
We present a deep learning method (abbreviated as D-GEX) to infer the expression of target genes from expression of landmark genes and compare its performance to those from other methods. Expand
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Gene expression inference with deep learning
TLDR
We present a deep learning method (abbreviated as D-GEX) to infer the expression of target genes from expression of landmark genes and compare its performance to those from other methods. Expand
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High-throughput Phenotyping of Lung Cancer Somatic Mutations.
Recent genome sequencing efforts have identified millions of somatic mutations in cancer. However, the functional impact of most variants is poorly understood. Here we characterize 194 somaticExpand
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