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Open AccessJournal ArticleDOI

A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles.

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TLDR
The expanded CMap is reported, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that is shown to be highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts.
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This article is published in Cell.The article was published on 2017-11-30 and is currently open access. It has received 1943 citations till now.

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Omics and Computational Modeling Approaches for the Effective Treatment of Drug-Resistant Cancer Cells.

TL;DR: In this article, the authors highlight the use of multi-omics data for studying drug-resistant cancer cells, and provide perspectives on future research opportunities for combating drug resistant cancer cells.
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A DeepFake framework for prediction of cell type specific transcriptional states induced by drug treatment

TL;DR: DeepCellState, a deep learning autoencoder-based framework, for predicting the induced transcriptional state in a cell type after drug treatment, based on the drug response in another cell type, is developed.
Journal ArticleDOI

Deep Learning Benchmarks on L1000 Gene Expression Data

TL;DR: A method novel to this data modality: graph convolugtional neural networks (GCNNs) are tested, which allow us to incorporate prior biological domain knowledge and find that GCNNs can be highly performant, with large datasets, whereas FF-ANNs consistently perform well.
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Integration of Multiple Data Sources for Gene Network Inference Using Genetic Perturbation Data.

TL;DR: This work presents a Bayesian approach integrating external data sources with knockdown data from human cell lines to infer gene regulatory networks and shows that this integrated method improves the accuracy of inferred gene networks as well as extends some previous Bayesian frameworks both in theory and applications.
Journal ArticleDOI

Screening as a strategy to drive regenerative medicine research

TL;DR: This review provides an overview of screening platforms that are used in regenerative medicine, and discusses their fabrication techniques, and in silico tools to analyze the extensive data sets typically generated by these platforms.
References
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Journal Article

Visualizing Data using t-SNE

TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.
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Gene Expression Omnibus: NCBI gene expression and hybridization array data repository

TL;DR: The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data and provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-power gene expression and genomic hybridization experiments.
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BLAT—The BLAST-Like Alignment Tool

TL;DR: How BLAT was optimized is described, which is more accurate and 500 times faster than popular existing tools for mRNA/DNA alignments and 50 times faster for protein alignments at sensitivity settings typically used when comparing vertebrate sequences.
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Adjusting batch effects in microarray expression data using empirical Bayes methods

TL;DR: This paper proposed parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples.
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