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1 largest source of variation in metazoan data compendia.
2 EX, and Clinical Pharmacology to the list of compendia.
3 dications to indications listed in specified compendia.
4 ts, synthetic data and various metazoan data compendia.
5 ns in both simulated datasets and microarray compendia.
6 on for future development of cancer-specific compendia.
7 ting and interpreting large-scale scATAC-seq compendia.
8 tools to large-scale multispecies expression compendia-1700 data sets with over 300,000 samples from
9                           Genome-wide CRISPR compendia across most common human cell lines offer the
10 are program to refer to 3 existing published compendia, American Medical Association Drug Evaluations
11 ff-label uses recognized by established drug compendia and peer-reviewed literature.
12 eneBridge tools together with the expression compendia are available as an open resource, which will
13 asing calls to revise the list of acceptable compendia, as authorized in the statute.
14                            For 1 indication, compendia citations did not increase between 2006 and 20
15                                              Compendia cited little of the available evidence, often
16  assessment, public and private payers, drug compendia, clinical research entities, statisticians, ac
17 ysis and visualization of expression data in compendia compiled from multiple laboratories.
18                                These massive compendia comprise billions of measurements and provide
19               All versions of the normalized compendia constructed for each species are maintained an
20 n between 2006 and 2008, and completeness of compendia content and citations were assessed.
21 e it difficult to determine whether and when compendia content was updated.
22                                              Compendia differed in evidence cited, terminology, detai
23 or the 14 off-label indications studied, the compendia differed in the indications included and wheth
24                          Oncologists rely on compendia for up-to-date access to evidence and reimburs
25 es (>20,000 genes) and very large microarray compendia (>10,000 conditions).
26 ependent component analysis (ICA) of RNA-seq compendia has shown to be a powerful method for inferrin
27 nce efforts to exploit growing drug response compendia in order to advance personalized therapy.
28 methods that leverage the heterogeneous data compendia in their entirety.
29 e components in diverse published expression compendia, including normal tissues and tumor samples.
30 chniques applied to enormous gene expression compendia into the hands of bench biologists.
31                                      Current compendia lack transparency, cite little current evidenc
32 ppropriate treatment, as reflected in timely compendia listings and reports of studies in the medical
33              Moreover, to leverage the large compendia of available omics data as a reference, we fur
34 RNA sequencing studies have begun to provide compendia of cell expression profiles(1-9), it has been
35 inants of protein-DNA specificity from large compendia of DNA-binding specificities and inferring the
36 tudies, have been extensively used to create compendia of genes that are preferentially expressed in
37 ly it to learn sequence predictors for large compendia of human and mouse data.
38 ogies are enabling the generation of massive compendia of human genome sequence data and associated m
39 sion levels of known relevant genes in large compendia of microarray data.
40         To aid in the analysis of such large compendia of microarray experiments, we present Microarr
41                                     Existing compendia of non-coding RNA (ncRNA) are incomplete, in p
42 linical Validation of Drug Indications Using Compendia of Public Gene Expression Data".
43                                       Public compendia of sequencing data are now measured in petabyt
44            To this end, we compare two large compendia of transcriptional profiles of human and mouse
45            Machine learning applied to large compendia of transcriptomic data has enabled the decompo
46 nriched for known driver genes from multiple compendia of validated oncogenes and tumor suppressors,
47 ds of patients and their clinical providers, compendia, payers, and policy makers, recommendations ar
48                        The creation of these compendia provided a systemic view of an organelle previ
49                                          The compendia's stated methods varied greatly from their act
50                         Data Assessment: The compendia's stated methods, literature related to off-la
51 on learning methods on large transcriptomics compendia, such as GTEx and TCGA, to boost the performan
52 ision that explicitly prohibits inclusion of compendia that do not have a publicly transparent proces
53 hile other fields have used large sequencing compendia to extract insights requiring otherwise imprac
54 TFs ChIP-Sequencing data and gene expression compendia to reconstruct TRNs in a genome-wide perspecti
55 rgets of genetic perturbations in microarray compendia, with up to a 24% improvement in sensitivity o