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1 d embryonic Grh targets by ChIP-seq and gene expression analysis.
2 pression were uncovered through whole-genome expression analysis.
3 DCoX to perform multi-factor differential co-expression analysis.
4 n tomography, electroneurography, and ELOVL5 expression analysis.
5 RACE) to adjust for the effect of SCNA in co-expression analysis.
6 d the sensitivity of subsequent differential expression analysis.
7         Real-time PCR was performed for gene expression analysis.
8  to standard approaches of differential gene expression analysis.
9 f cell states invisible to conventional gene expression analysis.
10 advance research on large-scale differential expression analysis.
11 luorescence, coimmunoprecipitation, and gene expression analysis.
12 in the evaluation and interpretation of IRF5 expression analysis.
13          Microarrays were performed for gene expression analysis.
14  polypharmacology through systems-based gene expression analysis.
15  propagate this into downstream differential expression analysis.
16 y integrating systems pharmacology with gene expression analysis.
17 his library, yielding sensitive differential expression analysis.
18 using both physiological end points and gene expression analysis.
19 a and provides outputs for differential gene expression analysis.
20 a defined niche coupled with cell cycle gene expression analysis.
21 emokines shown to be upregulated in the gene expression analysis.
22  cell genomic DNA PCR and fluorescent marker expression analysis.
23 ective method for obtaining tissue for PD-L1 expression analysis.
24 unding effect of RNA quality in differential expression analysis.
25 ization of qPCR data is crucial for accurate expression analysis.
26 lexible thin-film sensor for label free gene expression analysis.
27  cancer subtypes as well as isoform specific expression analysis.
28 ew insights to the area of differential gene expression analysis.
29 e runs of balanced sampling for differential expression analysis.
30                Genome-wide differential gene expression analysis (18,863 probes) resulted in a p valu
31                         In differential gene expression analysis, about 70% of the significantly diff
32  within an organism and for comparative gene expression analysis across organisms.
33 ng through the cilium, as determined by gene expression analysis after fluid flow-induced shear stres
34 The proposed network-based differential gene expression analysis algorithm dwgLASSO can achieve bette
35 ation, model reconstruction and differential expression analysis, all delivered through an updated pr
36                                         Gene expression analysis also confirmed the selective down-re
37                                         Gene expression analysis also identified several other signif
38                                         Gene expression analysis also indicated the presence of nonta
39  developed for single-factor differential co-expression analysis and applied in a variety of studies.
40                                   Both PPM1D expression analysis and cDNA sequencing in EBV LCLs of i
41 e ontology enrichment analysis, differential expression analysis and comparison of experiments.
42                                              Expression analysis and conditional deletion studies sho
43                                 Differential expression analysis and Gene ontology enrichment reveale
44                        Furthermore, our gene-expression analysis and genetic mapping results suggest
45                                   Using gene expression analysis and native gel electrophoresis we ch
46 d acute leukemias, largely derived from gene expression analysis and next-generation sequencing that
47                                  Genome-wide expression analysis and numerical simulation experiments
48 netic and pharmacological manipulation, gene expression analysis and time-lapse imaging of zebrafish
49 eing, there does not exists any differential expression analysis approach for RNA-seq data in literat
50 ability measurements as well as intronic RNA expression analysis are consistent with a transcriptiona
51                     It is unclear whether co-expression analysis at the single-cell level will provid
52 ge Bayes Factor approach for Differential Co-expression Analysis (BFDCA) for DC analysis.
53                                 We used gene expression analysis, biochemical methods, transmission e
54 le studies focusing on not only differential expression analysis, but also quantitative transcriptome
55               Conventional differential gene expression analysis by methods such as student's t-test,
56                                              Expression analysis by quantitative reverse transcriptio
57 cules conferred a protective phenotype, gene expression analysis by RNA sequencing found that Roxadus
58  tools for their impact on differential gene expression analysis by RNA-Seq.
59                             However, in vivo expression analysis by RNAscope and immunohistochemistry
60                                         Gene expression analysis by RT-qPCR revealed enrichment of p5
61 ctivation was characterized by targeted gene expression analysis, caspase-1 and NF-kappaB studies, cy
62 ublicly available transcriptomic studies, co-expression analysis combining multiple transcriptomic st
63  robustness to batch effects in differential expression analysis, compared to existing methods.
64                   First, a differential gene expression analysis comparing two distinct biological st
65                                  Global gene expression analysis, data intersection, pathway analysis
66                                 Differential expression analysis (DEA) is one of the main instruments
67                                 Differential expression analysis demonstrated an increased steady-sta
68                                              Expression analysis demonstrated that RIP1, RIP3, and ML
69                                  Genome-wide expression analysis demonstrated that the absence of G9a
70                                Additionally, expression analysis demonstrates that nodule leghemoglob
71                      While differential gene expression analysis did not demonstrate differences in P
72 FA together with RNA-sequencing differential expression analysis establishes the vanillin catabolic p
73                                 Differential expression analysis following nuclear RNA-seq of neutrop
74                We conducted phylogenetic and expression analysis for eight SEP-like GERBERA REGULATOR
75                            Furthermore, gene expression analysis for RBM20-dependent splice variants
76 entification for ChIP-seq, differential gene expression analysis for RNA-seq, nucleosome positioning
77 ation could be used as part of a specific co-expression analysis framework.
78                                          RNA expression analysis from isolated fibre cells reveals th
79                                      CaMYB31 expression analysis from placental tissue of pungent and
80 b servers are valuable and widely used, many expression analysis functions needed by experimental bio
81              This study used bioinformatics, expression analysis, gene ablation, and specific pharmac
82                  We use gene silencing, gene expression analysis, genetic mapping and population geno
83 pts to climb chasms of unsurmountable width; expression analysis guided us to C2 optic-lobe interneur
84                            Differential gene-expression analysis has allowed us to confirm the releva
85                     In the past decade, gene expression analysis has become a universally applied tec
86                                           Co-expression analysis has been employed to predict gene fu
87                                         Gene expression analysis has led to the identification of 254
88                                 Differential expression analysis has long been used for this purpose;
89                              Differential co-expression analysis helps further detect alterations of
90 biopsy specimens were taken for whole-genome expression analysis, histology, and T-cell isolation.
91                             Ribonucleic acid expression analysis identified changes in metabolic path
92                                         Gene expression analysis identified distinct surface markers
93 keratinocytes in AhR-knockout mice, and gene expression analysis identified many barrier-associated g
94                              NanoString gene expression analysis identified multiple HuR-regulated ge
95                              Conclusion Gene expression analysis identified multiple pathways upregul
96                                  Global gene expression analysis identified that PELP1-cyto expressio
97 tified in the C. hongkongensis, Differential expression analysis identified the miRNAs that play impo
98                                         Gene expression analysis identified the potent cell cycle inh
99 rom the following sources: (i) systematic co-expression analysis, (ii) detection of shared selective
100                                         Gene expression analysis, immunohistochemical studies of MM p
101                                          PHD expression analysis in 124 colorectal cancer patients re
102                                              Expression analysis in 18 different cancers and matched
103                     We used genome-wide gene expression analysis in clinical samples to identify miR-
104                                    A RNA-seq expression analysis in different Arachis species showed
105         Here, we performed an extensive gene expression analysis in ER+ breast cancer cell lines, to
106  highlight the potential of single-cell gene-expression analysis in human preimplantation development
107                                         Gene expression analysis in human prostate cancer samples fou
108 g RedFinder algorisms to perform a stability expression analysis in i) normal colon cells, ii) colon
109 e high-throughput tool for hypothesis-driven expression analysis in large numbers of genes (10 to 500
110                                        miRNA expression analysis in NiggV-infected mice showed signif
111 -/-) mice and used quantitative PCR for gene expression analysis in terminally differentiated osteocl
112                              Allele-specific expression analysis in the C. maxima x C. moschata inter
113                                  Immune gene expression analysis in the renal clear cell carcinoma co
114 ghput mapping of the m(6)A transcriptome and expression analysis in the Yhtdc2 mutant testes reveal a
115  affected by VPA, we performed a genome-wide expression analysis in yeast cells grown in the presence
116                              Microarray gene expression analysis indicated downregulation of NF-E2 re
117                                              Expression analysis indicated that BAM2 is more closely
118 hrough the Calvin Benson cycle; accordingly, expression analysis indicated that GLXI is transcription
119                                         Gene expression analysis indicated that the host cell respond
120                                              Expression analysis indicated that the transcript level
121                                              Expression analysis indicates that the OsBZ8 gene is hig
122                            Differential gene expression analysis indicates that there are multiple pa
123  Through fine-mapping, association analysis, expression analysis, insertional mutagenesis and transge
124  integrated features to support differential expression analysis, interactive heatmap production, pri
125                   When disease-specific gene expression analysis is integrated, DGE-NET prioritizes k
126 iferation; however, a comprehensive NOX gene expression analysis is missing for all major model syste
127                                  However, co-expression analysis is often treated as a black box with
128                A critical assumption of gene expression analysis is that mRNA abundances broadly corr
129        A frequent requirement of single cell expression analysis is the identification of novel patte
130                                           Co-expression analysis is widely used to predict gene funct
131                                      By gene expression analysis, KRAS is shown to be associated with
132 l treatments, we performed leaf RNA-seq gene expression analysis, LC-MS metabolomics and total phenol
133                                              Expression analysis, leveraged by a de novo assembled tr
134 s selected by conventional differential gene expression analysis method, the top 10 significant genes
135 dwgLASSO with conventional differential gene expression analysis method.
136 formance than conventional differential gene expression analysis methods by integrating information a
137                      Importantly, using gene expression analysis (N = 60) and immunohistochemistry (N
138                                           By expression analysis, next-generation sequencing, and imm
139 al model of Parkinson's disease through gene expression analysis of >30 batches of grafted human embr
140                                              Expression analysis of >300 surface proteins enabled ide
141                                 Quantitative expression analysis of 2,068 Mtb genes from the predicte
142                              In a postmortem expression analysis of 33 individuals affected with schi
143                                              Expression analysis of amyloid precursor protein transge
144                             Single-cell gene expression analysis of B6 Ifnb(+/+) versus B6 Ifnb(--) T
145                                Further, gene expression analysis of CD34(+) cells derived from fetal
146              Genome-wide DNA methylation and expression analysis of cell line models of acquired AI r
147                                              Expression analysis of components of the elastogenesis m
148  to glandular sepal tips; thus, differential expression analysis of contrasting floral tissue transcr
149                                         Gene expression analysis of dyW-/- E17.5 muscles identified a
150                                              Expression analysis of earleaf samples in a subtropical
151                                         Gene-expression analysis of effector-memory CD8(+) dT demonst
152                           For instance, gene expression analysis of genes associated with the hypotha
153 d a high-throughput (HT) phenotypic and gene expression analysis of HemSCs, and analyzed HemSC-derive
154                            Furthermore, gene expression analysis of human melanoma samples revealed t
155 d maturation was NK cell intrinsic, and gene expression analysis of human NK cell developmental subse
156 f incisor renewal and illustrate how gene co-expression analysis of intact biological systems can pro
157                            Differential gene expression analysis of joint neutrophils showed a switch
158 h based on the differential single-cell gene expression analysis of mesenchymal osteolineage cells cl
159                                              Expression analysis of MiHA-encoding genes showed that s
160                 Our approach to differential expression analysis of minor intron-containing genes is
161                                    Moreover, expression analysis of mRNA from similar tissues and tre
162                             Comparative gene expression analysis of neuronal derivatives from these i
163                                         mRNA expression analysis of proteins involved in establishmen
164                                         Gene expression analysis of rat brains following neonatal inf
165                                              Expression analysis of ravA and viaA genes showed that b
166                                              Expression analysis of rice ARFs and ARLs in different t
167                                 Differential expression analysis of RNA sequencing (RNA-seq) data typ
168  Therefore, we performed a longitudinal gene expression analysis of samples collected from controls a
169                                         Gene expression analysis of sensitive and resistant cell line
170                                 RT-PCR based expression analysis of seven glucosinolate biosynthetic
171                                         Gene expression analysis of several pathogenesis-related gene
172                                         Gene expression analysis of single cell-derived, adapted tetr
173  and phospho-Jun N-terminal kinase, and gene expression analysis of splenic CD19(+) B cells demonstra
174      RNA-seq has been used to perform global expression analysis of the achene and the receptacle at
175                                         Gene expression analysis of the brain implicates altered inna
176                          Epigenetic and gene expression analysis of The Cancer Genome Atlas AML data
177 ed generalized linear model for differential expression analysis of the count-based sequencing data f
178                                         Gene expression analysis of the dorsal hippocampus of PC4(f/f
179                     We report the first gene expression analysis of the human host response to experi
180                                         Gene expression analysis of the hypoxia-interconnected pathwa
181                                         Gene expression analysis of the liver and ileum indicated alt
182                                              Expression analysis of the main responsible genes for Na
183                             Parallel RNA-Seq expression analysis of the plasmid reporter identifies n
184                                 Differential expression analysis of the RNA sequencing datasets revea
185                            A systematic gene expression analysis of the SiOBP repertoire was performe
186                                 Differential expression analysis of transcriptomes from four paired s
187 e improve the interpretation of differential expression analysis of transcriptomic data from human ti
188                                     However, expression analysis of voltage-gated sodium channel alph
189 y Ilk in ureteric cells using a whole-genome expression analysis of whole-kidney mRNA in mice with Il
190                            We performed gene expression analysis on whole peripheral blood RNA sample
191                                    In a gene expression analysis performed to identify novel gene mod
192  profiled using RNASeq and differential gene expression analysis performed.
193  In this study we propose a new differential expression analysis pipeline, dubbed as super-delta, tha
194  data sets and with alternative differential expression analysis pipelines.
195 yers of genomic analysis (e.g., differential expression analysis, principal component analysis, gene
196 ustomizable functions including differential expression analysis, profiling plotting, correlation ana
197                                      Further expression analysis provided the functional annotation o
198                            Differential gene expression analysis provides evidence that root-knot nem
199  0.91, accuracy = 0.90), as compared to gene expression analysis results without TIN correction (sens
200                                 Differential expression analysis revealed 128 proteins that are selec
201                     For all tissues sampled, expression analysis revealed 831, 674 and 648 differenti
202 r in vitro cell culture experiments and gene expression analysis revealed a major defect in the proli
203                       Last, allelic-specific expression analysis revealed a significant but modest im
204                                         Gene expression analysis revealed a significant decrease in t
205                                         Gene expression analysis revealed an upregulation of gene sig
206                             Intriguingly, co-expression analysis revealed blood gene modules highly e
207                                         Gene expression analysis revealed downregulation of ion trans
208                                 Differential expression analysis revealed hundreds of genes modulated
209                   Differential gene and exon expression analysis revealed pervasive alterations in AP
210                        In confirmation, gene expression analysis revealed reduced tyrosine hydroxylas
211                           Proteomic and gene expression analysis revealed significant alterations in
212                                         Gene expression analysis revealed significant differences in
213                                              Expression analysis revealed that a Diedel gene homolog
214                                  Genome-wide expression analysis revealed that a set of cro-miRNAs ar
215                                         Gene expression analysis revealed that all the mate transcrip
216                                         Gene expression analysis revealed that anti-inflammatory cyto
217                                 Importantly, expression analysis revealed that both SNHG6-003 and TAK
218                                              Expression analysis revealed that Bph32 was highly expre
219                                         Gene expression analysis revealed that CD172a(+) and CD172a(-
220                                         Gene expression analysis revealed that CML36 and ACA8 are co-
221                                         Gene expression analysis revealed that CtHsfA2b was heat-indu
222                                  Global gene expression analysis revealed that genes upregulated in R
223                                       Global expression analysis revealed that LIF-independent iOCT4
224                             Genome-wide gene expression analysis revealed that PHF8 overexpression in
225                                              Expression analysis revealed that rs9517723 TT homozygot
226                                  Global mRNA expression analysis revealed that SMCR8 regulates transc
227                            Differential gene expression analysis revealed that strain SN2 displayed s
228                                              Expression analysis revealed that TERMINAL FLOWER1 (TgTF
229 r-initiating cells in vivo Furthermore, gene expression analysis revealed that the epithelium-specifi
230                              Systematic gene expression analysis revealed that the time elapsed, much
231                               An integrative expression analysis revealed that the WNT pathway antago
232                           Cross-species gene expression analysis revealed that these novel MBG3 model
233                Complementation test and gene expression analysis revealed that this non-coding region
234                                              Expression analysis reveals both diverse expression patt
235                     Furthermore, genome-wide expression analysis reveals that Jarid2 is required for
236                               Surpassing the expression analysis scope, our work also includes assess
237                               Moreover, gene expression analysis showed a lack of active transcriptio
238                                         Gene expression analysis showed a relative fold reductions of
239                                     The gene expression analysis showed an anabolic activity of Nandr
240                                         Gene expression analysis showed differential treatment- and r
241                           An allele-specific expression analysis showed overwhelmingly more cis-diver
242                                         Gene expression analysis showed significant upregulation of c
243                                 In vivo gene expression analysis showed that (p)ppGpp positively regu
244                                              Expression analysis showed that Aglianico is able to acc
245                                 Differential expression analysis showed that G. muris infection evoke
246                                         Gene expression analysis showed that MB treatment altered the
247                             Gene and protein expression analysis showed that neuropilin 2 (NRP2), a k
248                          RNA-seq and qRT-PCR expression analysis showed that overexpression of SeCsp
249                                 Whole-genome expression analysis showed the upregulation of genes inv
250                                         Gene expression analysis showed upregulation of T follicular
251                            Furthermore, gene expression analysis shows that the Hox gene Abdominal-B
252                        Most methods for mRNA expression analysis start with the reverse transcription
253                                         Gene expression analysis suggested that CHD4 coregulatory act
254                                              Expression analysis suggests differential expression of
255                                         Gene expression analysis supports a stepwise process of matur
256            Forty-two patients underwent gene expression analysis to assess the functional consequence
257  combined metabolomics, proteomics, and gene expression analysis to characterize the effects of mono-
258 ic screens with large-scale single-cell gene expression analysis to define the heterogeneity within t
259 , signaling modulation, and single-cell gene expression analysis to delineate a developmental traject
260 enome-wide histone modification mapping, and expression analysis to examine colorectal cancer cells l
261             We also employed allele-specific expression analysis to find potential regulatory variant
262 use genome-wide association mapping and gene-expression analysis to map the Mendelian blue locus, whi
263  and perform network-based differential gene expression analysis to select biomarker candidates by co
264 pplications that range from multiplexed gene expression analysis to the sensitive detection of infect
265 ((13)C-MFA), and RNA-sequencing differential expression analysis to uncover the following metabolic t
266 edicated to symbiosis and used cell-specific expression analysis together with protein localization t
267                                 Differential expression analysis uncovered new immune-cell-specific g
268                              Microarray mRNA expression analysis uncovered oxidative stress and DNA d
269  needle kidney biopsy specimens for microRNA expression analysis using deep sequencing.
270                                  However, co-expression analysis using human cancer transcriptomic da
271 CR platforms were directly compared for gene expression analysis using low amounts of purified, synth
272                             Genome-wide gene expression analysis using RNA-seq identified an array of
273                            Differential gene expression analysis using RNA-Seq showed consistent expr
274 trapping, while IsoDE2 performs differential expression analysis using the bootstrap samples generate
275 y to three existing methods for differential expression analysis using three data examples of technic
276                                  Genome-wide expression analysis using transgenic lines suggested tha
277                      Whole genome microarray expression analysis using tumor and OSE-derived cell lin
278                             For differential expression analysis, voom with quality weights marginall
279 ulation of RGC physiology, differential gene expression analysis was conducted to identify transcript
280                                         Gene expression analysis was done with both quantitative real
281                                     The gene expression analysis was limited to 174 individuals with
282                                  Previous co-expression analysis was mainly conducted at bulk tissue
283                                  First, gene expression analysis was performed in mice after hepatic
284 to earlier onset of disease, microarray gene expression analysis was performed on B cells from 4-week
285                                  Global gene expression analysis was performed on HMEC lines expressi
286       sncRNA identification and differential expression analysis was performed with iMir software.
287                       Using a RhoGTPase mRNA expression analysis we identified RhoC as the highest ex
288 Using high resolution global methylation and expression analysis we show that whereas patterns of met
289                                   Using gene expression analysis, we identified mRNAs proportionally
290                            Using genome-wide expression analysis, we observed upregulation of genes i
291                  Based on pathology and gene expression analysis, we report the first successful tran
292 osphoproteomics dataset in concert with gene expression analysis, we selected over 100 kinases potent
293                                By Affymetrix expression analysis, we show that the cytochrome P450 en
294 statistical methods for RNA-seq differential expression analysis were designed for individual genes,
295 learning methodology for robust differential expression analysis, which can be a new avenue to signif
296 osensor, we perform dynamic single cell gene expression analysis while simultaneously characterizing
297                               A differential expression analysis with Cufflinks and Stringtie was the
298                                         Gene expression analysis with linear mixed-effects model show
299 e challenging in the context of differential expression analysis with RNA-seq data.
300                         Results Differential expression analysis yielded a 14-gene radiogenomic signa

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