ライフサイエンスコーパス: atlas

1 as provided by the Allen Brain Connectivity Atlas.

2 blish a desiccation-tolerance transcriptomic atlas.

3 and analysis using a probabilistic brainstem atlas.

4 om independent patients in The Cancer Genome Atlas.

5 ll tumor types studied via The Cancer Genome Atlas.

6 eplicated in NTL data from The Cancer Genome Atlas.

7 sion locations were mapped to a common brain atlas.

8 5 International Diabetes Federation Diabetes Atlas.

9 , 2015) were obtained from The Cancer Genome Atlas.

10 n a scale-invariant, interactive mouse brain atlas.

11 3 cancer types profiled by The Cancer Genome Atlas.

12 in other cancer types from The Cancer Genome Atlas.

13 d in a validation set from The Cancer Genome Atlas.

14 mor subtypes obtained from The Cancer Genome Atlas.

15 ive scale by projects such as the Human Cell Atlas.

16 inoma patient cohorts from The Cancer Genome Atlas.

17 n independent samples from The Cancer Genome Atlas.

18 molecular data types from The Cancer Genome Atlas.

19 er using patient data from The Cancer Genome Atlas.

20 munofluorescence data from the Human Protein Atlas.

21 omically discriminated using a probabilistic atlas.

22 e, and lesions were mapped to a common brain atlas.

23 se structures using an existing histological atlas.

24 ons in mutants, and to map them onto a brain atlas.

25 enomic datasets, including The Cancer Genome Atlas.

26 scriptome by using data from the Allen Brain Atlas.

27 or data sets obtained from The Cancer Genome Atlas.

28 nning 19 cancer types from The Cancer Genome Atlas.

29 g on almost 200 cases from The Cancer Genome Atlas.

30 gative (TNBC) cancers from The Cancer Genome Atlas.

31 atic mutations reported in The Cancer Genome Atlas.

32 ty in patient tumours from The Cancer Genome Atlas.

33 tory analyses of all networks from the Power atlas.

34 ic data from Allen Human Brain and BrainSpan atlases.

35 markedly from common gyral-based structural atlases.

36 ving the way for systematic charting of cell atlases.

37 n a validation cohort from The Cancer Genome Atlas (113 patients with colorectal tumors, 178 endometr

38 Utilising the 'Irish DNA Atlas', a cohort (n = 194) of Irish individuals with fou

39 The high-resolution transcriptome atlas allowed us to distinguish between CAM-related and

40 ene expression analysis of The Cancer Genome Atlas AML data set reveals that GLI3 expression is silen

41 The Cancer Genome Atlas analysis confirmed that patients with a favorable

42 We evaluated the use of this atlas and additional individual fetal brain MRI atlases

43 The atlas and associated files can also be used for planning

44 inct cancer data sets from The Cancer Genome Atlas and discuss how predictions from these algorithms

45 can be browsed and downloaded at Expression Atlas and Ensembl.

46 f ChIP-Seq datasets from the Human Epigenome Atlas and FANTOM CAGE to demonstrate its wide applicabil

47 m miRNA sequencing data of The Cancer Genome Atlas and identified 19 adenosine-to-inosine (A-to-I) RN

48 e atlas with data from the Allen Human Brain atlas and identified receptor- and transporter-specific

49 The exceptions were the atlas and mid-thoracic vertebrae, which remained at the

50 The atlas and nlTPMs, associated with previously computed T1

51 es of 32 cancer types from The Cancer Genome Atlas and other independent patient cohorts.

52 Analyses of the The Cancer Genome Atlas and REMBRANDT databases confirmed upregulation of

53 ung cancer using data from the cancer genome atlas and the 1000 genomes project.

54 We highlight the importance of the atlas and the platform through the identification of dup

55 -associated genes provided by the Expression Atlas and upload and analyze their Omics datasets.

56 351 localized ccRCCs from The Cancer Genome Atlas and validated lncRNA-based recurrence classificati

57 isons by co-registering functional reference atlases and in vivo two-photon fluorescence microscopy d

58 se), kidney tissue expression (Human Protein Atlas) and literature mining.

59 mbine them with those from The Cancer Genome Atlas, and perform a comparative analysis between Asian

60 en Institute for Brain Science transcriptome atlas, and regional white matter connectivity loss at th

61 er Cell Line Encyclopedia, The Cancer Genome Atlas, and the Clinical Lung Cancer Genome Project.

62 es were extracted from the Allen Human Brain Atlas, and their average profile across the cortex was c

63 ages from non-malignant human tissue, glioma atlases, and murine glioma models.

64 Atlas- and voxel-based analyses were performed.

65 fied using a standardized region-of-interest atlas applied to the spatially normalized gray matter im

66 uroscience research, a brain template and an atlas are necessary.

67 for each cancer tissue in The Cancer Genome Atlas as ready-to-analyze MultiAssayExperiment objects a

68 To assess the patient-dependent accuracy of atlas-based attenuation correction (ATAC) for brain posi

69 o an uncorrected (UC) ZTAC (ZTACUC) and a CT atlas-based attenuation correction (ATAC).

70 ation accuracy to that from CTAC by means of atlas-based bone compensation.

71 In this work we explored if an atlas-based comparison approach reveals shape difference

72 The atlas-based parcellations present some known limitations

73 Atlas-based quantification and texture analysis revealed

74 n to the voxel-intensity histogram within an atlas-based white matter region and using the center and

75 d: whole cerebellum, cerebellar gray matter, atlas-based white matter, and subject-specific white mat

76 when patient bone was used for AC instead of atlas bone, the overall difference of PET with ATACpatie

77 's cross-talk pattern from The Cancer Genome Atlas breast cancer database.

78 ortantly, interrogation of the Cancer Genome Atlas breast invasive carcinoma data set indicates that

79 f subcellular protein distribution, the Cell Atlas, built by integrating transcriptomics and antibody

80 We illustrate the application of this atlas by calculating DTI tractography based structural c

81 This atlas can be adapted to different types of expression da

82 e present a detailed Brachypodium expression atlas, capturing gene expression in its major organs at

83 tient-specific anatomic differences from the atlas causes bone attenuation differences and misclassif

84 renal cancer subtypes from The Cancer Genome Atlas: clear cell renal cell carcinoma (ccRCC, also know

85 Results Patients in The Cancer Genome Atlas cohort with EZH2-high gene expression were 1.5 tim

86 colorectal carcinomas from the Cancer Genome Atlas collection to determine whether expression of PPP1

87 The atlas comparison revealed central gland hypertrophy in t

88 An atlas comparison was performed via per-voxel statistical

89 ions, we built a large-scale gene expression atlas composed of 62,547 messenger RNAs (mRNAs), 17,862

90 parate cohort derived from The Cancer Genome Atlas Consortium (n = 127).

91 A novel, automated method based on an atlas constructed from ultra-high resolution, post-morte

92 C. bursa-pastoris genome and a transcriptome atlas covering a broad sample of organs and developmenta

93 we report the generation of a transcriptome atlas covering most phases in the life cycle of the mode

94 Here we first analyzed The Cancer Genome Atlas data and determined that the PTPRT promoter is fre

95 tudies and the analyses of The Cancer Genome Atlas data demonstrate improved results compared with ex

96 its clinical utility with The Cancer Genome Atlas data demonstrated that our algorithm offers more c

97 RMS for the Digital Hand Atlas data set was 0.73 years, compared with 0.61 years

98 tween blacks and whites in The Cancer Genome Atlas data set.

99 ternational Consortium and The Cancer Genome Atlas data, copy number and expression agreement in Canc

100 evaluation of human TCGA (The Cancer Genome Atlas) data for colorectal cancer outcomes.

101 Mining breast cancer TCGA (The Cancer Genome Atlas) data sets, we demonstrate that hnRNPF negatively

102 ors (TFs), our analysis of The Cancer Genome Atlas database (TCGA) found that patients with lower exp

103 tivated genes derived from The Cancer Genome Atlas database for human glioma.

104 Further, an analysis of The Cancer Genome Atlas database indicated that this personalized medicine

105 Data analysis from The Cancer Genome Atlas database revealed that p38delta is highly expresse

106 of human breast tumors in The Cancer Genome Atlas database showed that although RASA1 mutations are

107 ession was analyzed within The Cancer Genome Atlas database.

108 patients with TNBC and in The Cancer Genome Atlas database.

109 Now, our TCGA (The Cancer Genome Atlas) database analyses illustrate a correlation betwee

110 computational methods and The Cancer Genome Atlas dataset analysis to identify novel miRNAs that tar

111 By leveraging The Cancer Genome Atlas dataset, we identified lipid metabolism as the met

112 fusion genes identified in The Cancer Genome Atlas datasets.

113 Power spectra of 1-s long data segments for atlas-defined brain areas were clustered into spectral p

114 scaled below 42 HU for ZTACSEC For ATAC, the atlas deformed to MR in-phase was segmented to air, inne

115 The atlas demonstrates why some widely used auxin herbicides

116 rred at a 3% to 4% rate in The Cancer Genome Atlas-derived and in-house cohorts of patients with sero

117 e 1000 Genomes Project and The Cancer Genome Atlas, establishing damage as a pervasive cause of seque

118 Furthermore, brain atlases extend analysis of functional magnetic resonance

119 generate a cellular resolution 3D expression atlas for an entire animal.

120 usly uncharacterized and important molecular atlas for exploring region-specific astroglial functions

121 into the United States Food Access Research Atlas for FD status.

122 cs tools: (i) an integrative gene expression atlas for four model legumes that include 550 array hybr

123 n RNA sequencing data from The Cancer Genome Atlas for seven solid cancers.

124 as and additional individual fetal brain MRI atlases for completely automatic multi-atlas segmentatio

125 for nematode biology and foreshadow similar atlases for other organisms.

126 and were used to construct statistical shape atlases for the PCa+, Bx- and Cl- prostates.

127 y that uses network alignment for validating atlas-free parcellation brain connectomes.

128 hat NA algorithms may be applied in cases of atlas-free parcellation for a fully network-driven compa

129 ly, it has been recently proposed to perform atlas-free random brain parcellation into nodes and alig

130 new algorithm to construct a spatiotemporal atlas from MRI of 81 normal fetuses scanned between 19 a

131 lysis of RNA-seq data from The Cancer Genome Atlas further demonstrated that concordant upregulation

132 -induced gene signature to The Cancer Genome Atlas glioblastoma dataset to explore the association of

133 Cancer Cohort (n=40); and The Cancer Genome Atlas Glioblastoma Patient Cohort (n=98).

134 Our comprehensive human and mouse islet GPCR atlas has demonstrated that species differences do exist

135 Although The Cancer Genome Atlas has sequenced primary tumour types obtained from s

136 nctional architecture, yet current reference atlases have major limitations such as lack of whole-bra

137 Analyses of the Cancer Genome Atlas HCC RNA-sequencing data were performed by using In

138 Data in The Cancer Genome Atlas HNSCC database showed a significant inverse correl

139 in reaction; and data from The Cancer Genome Atlas HNSCC project were analyzed.

140 The Human Protein Atlas (HPA) enables the simultaneous characterization of

141 alteration frequencies to The Cancer Genome Atlas identified some differences and suggested an enric

142 specification and differentiation genes, our atlas identifies and molecularly characterizes 605 bilat

143 gnature genes derived from the Human Protein Atlas in the EoE transcriptome and in EPC2 esophageal ep

144 ger-term effort to generate whole-brain cell atlases in species including mice and humans.

145 al subdivisions, presented in the form of an atlas including confocal sections and 3D digital models

146 Regions of interest from the JHU ICBM atlas, including uncinate fasciculus and sagittal stratu

147 on normal-tumor pairs from The Cancer Genome Atlas indicated that cancer-specific expression-associat

148 ransformation of the PET-derived human brain atlas into a protein density map of the serotonin (5-hyd

149 cancer types/subtypes from The Cancer Genome Atlas into over 170,000 carefully curated nonredundant p

150 The atlas is available online as a reference for anatomy and

151 The atlas is created from molecular and structural high-reso

152 An atlas is then applied in order to define anatomically me

153 A digital single-cell atlas mapping the distribution of hormone metabolic and

154 ng wild adult Barbary macaques in the Middle Atlas Mountains, Morocco, as a case study.

155 By analyzing The Cancer Genome Atlas mRNA expression data for HGS ovarian cancer patien

156 ent cohorts: a cohort from The Cancer Genome Atlas (n = 532), a cohort from University of Texas South

157 oma subtypes with those of The Cancer Genome Atlas Network and found consistency between the two stud

158 characterization of CRC by The Cancer Genome Atlas Network detected the overexpression of the insulin

159 The Nervous System Disease NcRNAome Atlas (NSDNA) is a manually curated database that provid

160 of D. melanogaster, yielding a comprehensive atlas of 62,000 polyadenylated ends.

161 e database, EnhancerAtlas, which contains an atlas of 2,534,123 enhancers for 105 cell/tissue types.

162 ript collections to generate a comprehensive atlas of 27,919 human lncRNA genes with high-confidence

163 ar field analyses to create a pharmacophoric atlas of AUX1 substrates.

164 We built a quality high-resolution 3D atlas of average in vivo sheep brains linked to a refere

165 Our findings provide an anatomic atlas of B-cell clonal lineages, their properties and ti

166 dicted BCs, the Integrated Microbial Genomes Atlas of Biosynthetic gene Clusters (IMG-ABC).

167 Single-nucleus methylomes expand the atlas of brain cell types and identify regulatory elemen

168 r Capture Hi-C to generate a high-resolution atlas of chromosomal interactions involving 22,000 gene

169 ood cells, we provide a detailed immune cell atlas of early lung tumors.

170 a high-resolution, multidimensional, in vivo atlas of four of the human brain's 5-HT receptors (5-HT1

171 Our findings provide the first comprehensive atlas of functional topology across different phenotypes

172 A transcriptome atlas of gene expression was constructed from 47 RNA-seq

173 healthcare resource data from the Dartmouth Atlas of Health Care (2006), and Medicare fee-for-servic

174 Included is an atlas of high-definition images of CTCs from various can

175 In addition, we apply LINSIGHT to an atlas of human enhancers and show that the fitness conse

176 magnetic resonance imaging-based human brain atlas of important serotonin receptors and the transport

177 have generated a spatiotemporal expressional atlas of Met and gce throughout development.

178 tissue-specific and evolutionarily conserved atlas of miRNA expression and function.

179 We thus provide a broad atlas of miRNA expression and promoters in primary mamma

180 project, we created an integrated expression atlas of miRNAs and their promoters by deep-sequencing 4

181 risation of shellac by HPLC-ESI-Q-ToF and an atlas of MS/MS spectra of shellac components, this work

182 This expansive, high-resolution atlas of multi-omics changes yields insights into cell-t

183 morsphere systems, we generate a large-scale atlas of mutant-IDH1-induced epigenomic reprogramming.

184 el for electromagnetic field simulations, an atlas of peripheral nerves, and a neurodynamic model to

185 thermore, this study provided a quantitative atlas of poly(A) usage.

186 way for the construction of a comprehensive atlas of public mouse and human immune repertoires with

187 -specific epigenetic data to build a genomic atlas of single-nucleotide polymorphism (SNP) heritabili

188 This new in vivo neuroimaging atlas of the 5-HT system not only provides insight in th

189 Here, we introduce a three-dimensional atlas of the cat cerebral cortex based on established cy

190 An anatomical atlas of the central adult fly brain was recently descri

191 construction of an unbiased four-dimensional atlas of the developing fetal brain by integrating symme

192 plotted within a framework formed by an MRI atlas of the gerbil brain.

193 rvival, thereby presenting an in-depth human atlas of the immune tumor microenvironment in this disea

194 For a three-dimensional atlas of the insect nervous system, hundreds of volume c

195 has been used to generate a gene expression atlas of the major plant tissues (i.e. leaf, root, stem,

196 uce a single-cell resolution gene expression atlas of the newborn mouse kidney, an interesting time i

197 Here we present an updated atlas of the Prx superfamily identified using a novel me

198 here is a need for an improved digital brain atlas of the spatiotemporal maturation of the fetal brai

199 Finally, we use an atlas of transcription data in a mammalian circadian sys

200 An automated method (based on an ex vivo atlas of ultra-high-resolution hippocampal tissue) was u

201 ncers, isomiRs and our resulting 'Pan-cancer Atlas' of isomiR expression could serve as a suitable fr

202 gene expression data from The Cancer Genome Atlas, Oncomine, PrognoScan, and a hepatocellular carcin

203 an emerging model for grasses, no expression atlas or gene coexpression network is available.

204 atasets, such as that from The Cancer Genome Atlas or to upload their own.

205 Eight radiologists from the Cancer Genome Atlas Ovarian Cancer Imaging Research Group developed an

206 PK and HIF-1 signatures to The Cancer Genome Atlas patient transcriptomics data of multiple cancer ty

207 ional concerted effort to create a Precancer Atlas (PCA), integrating multi-omics and immunity - basi

208 (CTAC), PET with ATAC (air and bone from an atlas), PET with ATACpatientBone (air and tissue from th

209 Brain atlases play an important role in effectively communicat

210 ol using RNA-seq data from The Cancer Genome Atlas program's Kidney Clear Cell Carcinoma project.

211 ing affinity reagents from the Human Protein Atlas project and multiplexed immuoassays, we extensivel

212 We combined data from the Malaria Atlas Project and the Global Burden of Disease Study to

213 ting results obtained from The Cancer Genome Atlas project data.

214 s from two cancer types in the Cancer Genome Atlas project: glioblastoma multiforme and ovarian serou

215 f existing RNA-seq data into transcriptional atlas projects.

216 Expression Atlas provides information about gene and protein expres

217 The WHO Child and Adolescent Mental Health Atlas, published in 2005, reported that child and adoles

218 nment procedure to assign functional data to atlas regions and correlate activity between regions to

219 n MRI atlases for completely automatic multi-atlas segmentation of fetal brain MRI.

220 hnique that incorporates two different multi-atlas segmentation propagation and fusion techniques: Th

221 tion with data mining from the Cancer Genome Atlas showed that the neutrophil chemokine CXCL1 gene wa

222 0,737 genes present in the Allen Human Brain Atlas showed the set of top 100 strongest correlating ge

223 ies and real datasets from The Cancer Genome Atlas showed the significant improvement of DMRMark over

224 ains, or groups of related concepts, and our atlas shows which domains are represented in each area.

225 or accessing and analyzing The Cancer Genome Atlas, TARGET, and other important references such as GE

226 rtal miRNA-Seq dataset and The Cancer Genome Atlas (TCGA) (n = 1052).

227 n independent cohorts from The Cancer Genome Atlas (TCGA) (n = 414) and EMBL-EBI (n = 53), CLEAR scor

228 ata of ovarian cancer from The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Conso

229 bundance, all available in The Cancer Genome Atlas (TCGA) and developed iDriver, a non-parametric Bay

230 reast cancer patients from The Cancer Genome Atlas (TCGA) and found multiple recurrent gene fusions i

231 ta in 23 cancer types from The Cancer Genome Atlas (TCGA) and identified 1295 mutation clusters.

232 s supported by analysis of The Cancer Genome Atlas (TCGA) and NCBI GEO data sets, which demonstrated

233 SCLC, NSCLC cell lines and The Cancer Genome Atlas (TCGA) and reveal a markedly elevated expression o

234 Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and the recently added datasets from REposi

235 NA expression results from The Cancer Genome Atlas (TCGA) breast cancer data set (n = 1215) to calcul

236 vasive ductal carcinoma in The Cancer Genome Atlas (TCGA) Breast Invasive Carcinoma (BRCA) cohort.

237 CC were identified through The Cancer Genome Atlas (TCGA) clear cell kidney (KIRC) dataset (419 white

238 The Cancer Genome Atlas (TCGA) data have been increasingly collected.

239 m a pan-cancer analysis of The Cancer Genome Atlas (TCGA) data set and observe that bi-allelic pathog

240 The Cancer Geneome Atlas (TCGA) data was analyzed for HDAC, PI3K, HER2, and

241 alysis has been applied to The Cancer Genome Atlas (TCGA) data.

242 gene expression data from The Cancer Genome Atlas (TCGA) demonstrate reduced expression of cytotoxic

243 lts were filtered by using The Cancer Genome Atlas (TCGA) expression data in CRC, whereby we generate

244 g RNA-sequencing data from The Cancer Genome Atlas (TCGA) for >9,100 tumors across 30 cancer types,

245 ed in gliomas according to The Cancer Genome Atlas (TCGA) GBM database.

246 ulated with data sets from The Cancer Genome Atlas (TCGA) is provided to allow customized query.

247 Compared to a cohort of The Cancer Genome Atlas (TCGA) patients with HER2-positive non-IBC, HER2-p

248 om 26 cancers sequenced in the Cancer Genome Atlas (TCGA) Project to estimate the subset of cancers (

249 inoma cancer subtypes from The Cancer Genome Atlas (TCGA) project.

250 Comparison of tumors from The Cancer Genome Atlas (TCGA) reveals that head and neck squamous cell ca

251 adding two new modules of The Cancer Genome Atlas (TCGA) RNA-Seq analysis and PubMed abstract mining

252 10 271 tumor datasets from The Cancer Genome Atlas (TCGA) to evaluate whether isomiRs can distinguish

253 oss eleven cancer types in The Cancer Genome Atlas (TCGA) to identify cancer-predisposing CNV regions

254 estion, 266 melanomas from The Cancer Genome Atlas (TCGA) were categorized by the presence or absence

255 ibus, the Broad Institute, The Cancer Genome Atlas (TCGA), and the European Genome-Phenome Archive.

256 With the completion of The Cancer Genome Atlas (TCGA), there is opportunity for systematic analys

257 to BCa patient profiles in The Cancer Genome Atlas (TCGA).

258 reast cancer CNV data from the Cancer Genome Atlas (TCGA).

259 d in many manuscripts from The Cancer Genome Atlas (TCGA).

260 eal subclasses reported by The Cancer Genome Atlas (TCGA).

261 rcinoma (ccRCC) cases from The Cancer Genome Atlas (TCGA).

262 enomics data sets, such as The Cancer Genome Atlas (TCGA).

263 dy using protein data from The Cancer Genome Atlas (TCGA).

264 using the data released by the Cancer Genome Atlas (TCGA).

265 jacent breast samples from The Cancer Genome Atlas (TCGA).

266 cross 33 cancer types from The Cancer Genome Atlas (TCGA).

267 ifferent cancer types from The Cancer Genome Atlas (TCGA).

268 n a subset of tumours from the Cancer Genome Atlas (TCGA).

269 st cancer acquired through The Cancer Genome Atlas (TCGA).

270 A third cohort from The Cancer Genome Atlas (TCGA: n = 335 patients) confirmed the poor progno

271 pression from the Cancer Genome and Proteome Atlases (TCGA and TCPA) to characterize proteins and pro

272 cross 21 cancers from the 'The Cancer Genome Atlas' (TCGA) database.

273 Our Brachypodium expression atlas thus provides a powerful resource to reveal functi

274 ta available for tumors in The Cancer Genome Atlas to analyze TP53 splice variant expression.

275 sue Expression project and The Cancer Genome Atlas to comprehensively analyze the transcriptomes of h

276 analysis on datasets from The Cancer Genome Atlas to elucidate how the expression patterns of mRNAs

277 ers interface with the EMBL-EBI's Expression Atlas to enable the projection of baseline and different

278 del, we have developed the Tomato Expression Atlas to facilitate effective data analysis, allowing th

279 d a pan-cancer analysis of The Cancer Genome Atlas to identify transcriptional networks regulated by

280 s possibility, we used data from Allen Brain Atlas to investigate variability in gene expression prof

281 targets and this enabled us to transform the atlas to represent protein densities (in picomoles per m

282 an connectivity research is the use of brain atlases to compare findings across individuals and studi

283 drome, cross-referenced to the Human Protein Atlas, to identify commonly dysregulated pathways and bi

284 ma multiforme samples from The Cancer Genome Atlas using different analysis pipelines, and compared b

285 We recently created a "cell death atlas," using the detection of activated caspase-3 (AC3)

286 ted using AAL (Automated Anatomical Labeling atlas) volumes of interest (VOIs) for parietal, temporal

287 ons from the publicly available Digital Hand Atlas was compared with published reports of an existing

288 ng data from 190 tumors in the Cancer Genome Atlas, we correlated immune cell gene expression profile

289 ia database retrieval from The Cancer Genome Atlas, we could highlight the importance of the miR-23a/

290 llen Institute's human brain transcriptional atlas, we demonstrate that genes particularly enriched i

291 Using the Allen Institute's transcriptional atlas, we further established the colocalization of mu-o

292 Using this gene expression atlas, we inferred details of molecular processes that a

293 em gene expression data from the Allen Brain Atlas, we investigated the impact of transcription facto

294 bioinformatic resource, The Cancer Proteome Atlas, which contains two separate web applications.

295 ain by comparing the 5-HT density across the atlas with data from the Allen Human Brain atlas and ide

296 23,677 profiles into a comprehensive quality atlas with fine classification for users.

297 ith ATACpatientBone (air and tissue from the atlas with patient bone), and PET with ATACboneless (air

298 and genetic studies of a human liver biopsy atlas with the aim of identifying putative therapeutic t

299 cently described [12]; however, combining an atlas with whole-brain calcium imaging has yet to be per

300 T with ATACboneless (air and tissue from the atlas without bone).