戻る
「早戻しボタン」を押すと検索画面に戻ります。 [閉じる]

コーパス検索結果 (１語後でソート)

通し番号をクリックするとPubMedの該当ページを表示します
1 n studies and publicly available datasets in cancer genomics.
2 m, passenger mutations is a key challenge in cancer genomics.
3 y relevant features for survival analysis in cancer genomics.
4 l translocations is an important question in cancer genomics.
5 e SVs, focusing on applications in human and cancer genomics.
6 r diverse fields, including gene therapy and cancer genomics.
7 ublicly available through the cBioPortal for Cancer Genomics.
8  tissues is one of the biggest challenges in cancer genomics.
9  a cohort of tumors is a challenging task in cancer genomics.
10 put to many important downstream analyses in cancer genomics.
11  enable more detailed downstream analyses in cancer genomics.
12 thms limits the implementation and uptake of cancer genomics.
13  cancer causation through large-scale global cancer genomics.
14 r Research Project GENIE, and cBioPortal for Cancer Genomics.
15 mes in clinical samples using cBioPortal for Cancer Genomics.
16 cross the breadth of Mendelian disorders and cancer genomics.
17  use the CGC to investigate key questions in cancer genomics.
18 ranslational effects of genetic mutations in cancer genomics.
19 ts in both risk groups, which is violated in cancer genomics.
20 isASE has potential for wide applications in cancer genomics.
21 t across human cancers is a key challenge in cancer genomics.
22 ulations within tumors is a key challenge in cancer genomics.
23  up unprecedented opportunities to transform cancer genomics.
24 ntal problem for statistical methods used in cancer genomics.
25  results may help to guide the next stage of cancer genomics.
26  enable discovery and prediction in clinical cancer genomics(3-5).
27                                           In cancer genomics, a key challenge is the fast generation
28 rly resolved and cannot be revealed by human cancer genomics alone.
29 tic variant detection is an integral part of cancer genomics analysis.
30 ly, we will also discuss how the advances in cancer genomics and cancer modeling will influence each
31 scipline that aims to bridge the gap between cancer genomics and classical immunology.
32 ed tools to integrate, visualize and analyze cancer genomics and clinical data.
33           Here, we review recent advances in cancer genomics and discuss what the new findings have t
34 tion, and connects ecDNA biology with modern cancer genomics and epigenetics.
35                       Ongoing discoveries in cancer genomics and epigenomics have revolutionized clin
36 article highlights the technology underlying cancer genomics and examines the early results of genome
37             These data have implications for cancer genomics and for the targeted therapy of cancer.
38 ation changes are integral to all aspects of cancer genomics and have been shown to have important as
39 oficiency, large computational resources and cancer genomics and immunology knowledge.
40  use of in situ single cell-based methods in cancer genomics and imply that chemotherapy before HER2-
41                       Its performance across cancer genomics and multi-view image data highlights its
42 ly understood despite their critical role in cancer genomics and neurological disease studies.
43 e visualization of longitudinal clinical and cancer genomics and other molecular data in patient coho
44 ill likely continue to advance translational cancer genomics and precision cancer medicine.
45 cers represent a unique case with respect to cancer genomics and precision medicine, as the mutation
46 to diversifying the knowledge base of breast cancer genomics and provides insights into the disease e
47  aspirates, together with recent advances in cancer genomics and single-cell molecular analysis, have
48 lacement therapy and risk for breast cancer, cancer genomics and targeted therapies, short- and long-
49 efforts are revolutionizing our knowledge of cancer genomics and tumor biology.
50 ational drug discovery, medical informatics, cancer genomics, and systems biology.
51  provide an overview of the current state of cancer genomics, appraise the current portals and tools
52  genetic landscape of most cancer types, and cancer genomics approaches are driving new insights into
53  map healthy organs and previous large-scale cancer genomics approaches focused on bulk sequencing at
54 ovided practical knowledge on structural and cancer genomics approaches, as well as an exclusive plat
55 encing has been widely used for personal and cancer genomics as well as in various research areas.
56 mproved understanding of population-specific cancer genomics, as well as translation of those finding
57               GEPIA fills in the gap between cancer genomics big data and the delivery of integrated
58                                     The UCSC Cancer Genomics Browser comprises a suite of web-based t
59                                          The Cancer Genomics Browser currently hosts 575 public datas
60                                     The UCSC Cancer Genomics Browser is a set of web-based tools to d
61                                     The UCSC Cancer Genomics Browser is a web-based application that
62                                      The ISB Cancer Genomics Cloud (ISB-CGC) is one of three pilot pr
63 ted versions in CWL and on the Seven Bridges Cancer Genomics Cloud platform can be obtained by contac
64 ncer Gateway in the Cloud, and Seven Bridges Cancer Genomics Cloud) provide access and availability t
65 n Bridges Genomics implementation of the NCI Cancer Genomics Cloud, which provides graphical interfac
66 cross more than 12,000 donors from two large cancer genomics cohorts.
67      Whole-genome sequencing has brought the cancer genomics community into new territory.
68 s of the pipeline are made accessible to the cancer genomics community.
69                                              Cancer genomics consortia have identified somatic driver
70                                   The Global Cancer Genomics Consortium (GCGC) is an international co
71                                 The study of cancer genomics continually matures as the number of pat
72 en tested the predictions of our model using cancer genomics data and confirmed that many passengers
73 t using drug-response data, multidimensional cancer genomics data and genome-wide association study d
74 lso applied the human interactome network to cancer genomics data and identified several interaction
75 ed tools to display, investigate and analyse cancer genomics data and its associated clinical informa
76                   Furthermore, we found that cancer genomics data do not support the proposed role of
77  pharmacogenomics data resources such as pan-cancer genomics data for cancer cell lines (CCLs) and tu
78 r hosts a growing body of publicly available cancer genomics data from a variety of cancer types, inc
79                          When applied to any cancer genomics data set, the algorithm can nominate onc
80  impute drug response in very large clinical cancer genomics data sets, such as The Cancer Genome Atl
81 t for this model in cancer age-incidence and cancer genomics data that also allow us to estimate the
82              We demonstrate this problem for cancer genomics data where the standard log-rank test le
83 The algorithm is fast, broadly applicable to cancer genomics data, is of immediate use for prioritizi
84 isualization tools enable the exploration of cancer genomics data, most biologists prefer simplified,
85 onfounding effects of technical artifacts in cancer genomics data, our study emphasizes the need to s
86                     Employing population and cancer genomics data, structural analyses, molecular dyn
87 ta that enhances the interpretability of the cancer genomics data.
88 act of both mechanisms has been confirmed in cancer genomics data.
89 ing bioinformatics approaches and one mining cancer genomics data.
90 rative (or systems) analysis of the combined cancer genomics database.
91                                      Current cancer genomics databases have accumulated millions of s
92 ing costs in DNA sequencing technology, rich cancer genomics datasets with many spatial sequencing sa
93     We test the method on all available TCGA cancer genomics datasets, and detect multiple previously
94                 Through analyses of existing cancer genomics datasets, we find aberrant sH2A upregula
95 ancer mechanisms from large multidimensional cancer genomics datasets.
96 ces of BiRewire by applying it to large real cancer genomics datasets.
97                                              Cancer genomics demonstrates that these few driver mutat
98 Diagnostic and therapeutic advances based on cancer genomics developed during a time when it was poss
99                           In this new era in cancer genomics, discoveries from studies conducted on a
100                                              Cancer genomics efforts have identified genes and regula
101 as well as an exclusive platform for focused cancer genomics endeavors.
102                          Keywords: Oncology, Cancer Genomics, Epignomics, Radiogenomics, Imaging Mark
103 fy and extract the most important details of cancer genomics experiments from biomedical texts.
104                             Our knowledge of cancer genomics exploded in last several years, providin
105 ous mutations are rarely investigated in the cancer genomics field.
106 tools have, along with the rapid progress of cancer genomics, generated an increasingly complex under
107  technology has produced a transformation in cancer genomics, generating large data sets that can be
108  tumors or cultured samples are superior for cancer genomics has been a longstanding subject of debat
109 come more important because federally funded cancer genomics has been centralized under The Cancer Ge
110 rical view of how increased understanding of cancer genomics has been translated to the clinic and di
111                                              Cancer genomics has focused on the discovery of mutation
112 se of normal tissue data for the analysis of cancer genomics has primarily focused on the paired anal
113                                              Cancer genomics has revealed many genes and core molecul
114                         Although advances in cancer genomics have dramatically enhanced our understan
115                                  Advances in cancer genomics have identified numerous recurrent mutat
116  of the importance of evolutionary theory to cancer genomics have led to a proliferation of phylogene
117 tions of protein-coding genes are a focus of cancer genomics; however, the impact of oncogenes on exp
118 ognosis and advance our understanding of how cancer genomics impacts patient outcomes.
119             The next opportunity is to embed cancer genomics in clinical context so that patient-cent
120 ify relationships between cancer imaging and cancer genomics in LGGs.
121 ir tumor genetics with the goal of utilizing cancer genomics in the clinical management of pancreatic
122                           We analyzed breast cancer genomics in the search for potential drug targets
123 ver quality, utility, and safety of LDTs for cancer genomics, including tests marketed directly to co
124                         An important goal of cancer genomics initiatives is to provide the research c
125 cer is required to translate the findings of cancer genomics into therapeutic improvement.
126           Successful examples of translating cancer genomics into therapeutics and diagnostics reinfo
127                                       Still, cancer genomics is in its infancy.
128                               The new era of cancer genomics is providing us with extensive knowledge
129                                 The field of cancer genomics is rapidly evolving and has led to the d
130 One of the most important recent findings in cancer genomics is the identification of novel driver mu
131                              A major goal of cancer genomics is to identify all genes that play criti
132                         An important goal of cancer genomics is to identify systematically cancer-cau
133                            A central goal of cancer genomics is to identify, in each patient, all the
134                         A major challenge in cancer genomics is uncovering genes with an active role
135  is evident that the translation of emerging cancer genomics knowledge into clinical applications can
136 ng technologies has transformed the field of cancer genomics, leading to the identification of geneti
137    These results identify the convergence of cancer genomics, mitochondrial priming and GCT evolution
138 rees from Poliovirus, Burkholderia and human cancer genomics NGS datasets.
139 pathway analysis has been a powerful tool in cancer genomics, our knowledge of oncogenic pathway modu
140    Given the complexity and diversity of the cancer genomics profiles, it is challenging to identify
141 the unprecedented conjunction of large-scale cancer genomics profiling of tumor samples in The Cancer
142 and case breast cancer datasets from the NCI cancer genomics program - The Cancer Genome Atlas (TCGA)
143                       The recent large-scale cancer genomics projects have revealed multi-omics data
144      Based on this analysis, we suggest that cancer genomics projects such as The Cancer Genome Atlas
145 porate additional analyses and new data from cancer genomics projects.
146                          Despite advances in cancer genomics, radiotherapy is still prescribed on the
147                         Current knowledge of cancer genomics remains biased against noncoding mutatio
148                                              Cancer genomics research aims to advance personalized on
149                                A key task in cancer genomics research is to identify cancer driver ge
150                                           In cancer genomics research, one important problem is that
151 e" on the effect of sample type selection on cancer genomics research.
152 cer genes remains a significant challenge in cancer genomics research.
153 lationships and provides a valuable tool for cancer genomics research.
154 nes is a critical yet challenging problem in cancer genomics research.
155 y cancer drivers and guide the next stage of cancer genomics research.
156 bally distinct patients, we generate a large cancer genomics resource for sub-Saharan Africa, identif
157 vo RNAi screens and illustrate how combining cancer genomics, RNA interference, and mosaic mouse mode
158 t institutions performing NGS sequencing for cancer genomics should incorporate the step of merging M
159 , we review the key historical milestones in cancer genomics since the completion of the genome, and
160                              Here, we review cancer genomics software and the insights that have been
161                              However, recent cancer genomics studies also point to the existence of c
162 ES) is widely utilized both in translational cancer genomics studies and in the setting of precision
163 r heterogeneity, tumor samples collected for cancer genomics studies are often heavily diluted with n
164                                     Previous cancer genomics studies focused on searching for novel o
165                                              Cancer genomics studies frequently aim to identify genes
166                                              Cancer genomics studies have identified thousands of put
167 s a significant technical challenge for most cancer genomics studies performed at less than 100x mean
168  patterns and driver pathways in large-scale cancer genomics studies, and it may also be used for oth
169 e advantages of ExaLT on data from published cancer genomics studies, finding significant differences
170                          Despite large-scale cancer genomics studies, key somatic mutations driving c
171 iety of cancers as identified in large-scale cancer genomics studies, provide new interfaces for expl
172 NPs and have been used widely in linkage and cancer genomics studies.
173 ting somatic CNV and SV detection methods in cancer genomics studies.
174  how many signatures should be expected in a cancer genomics study.
175 heterogeneity in several key applications in cancer genomics such as immunogenicity, metastasis, and
176  This article discusses several areas within cancer genomics that are being transformed by the applic
177 ghlight key discoveries and resources within cancer genomics that were previously inaccessible with p
178 or Ags can be identified by directly linking cancer genomics to cancer immunology and immunotherapy.
179 the current challenges for applying prostate cancer genomics to clinical management, this review will
180 ve adequate background knowledge of clinical cancer genomics to design meaningful radiogenomics proje
181 rm initially designed for use in research on cancer genomics to enable its use in research on SARS-Co
182 eloped here, potentially democratising whole cancer genomics to many.
183 s, and issues involved in the translation of cancer genomics to the clinic are discussed.
184 ation of a cancer dependency map, connecting cancer genomics to therapeutic dependencies.
185 ly structured will primarily fund efforts in cancer genomics with longer-term goals of advancing prec
186 A sequencing has revolutionized the study of cancer genomics with numerous discoveries that are relev
187 provide insight into the landscape of breast cancer genomics with the genomic characterization of tum
188 mutational signatures is becoming routine in cancer genomics, with implications for pathogenesis, cla
189    Short-read sequencing is the workhorse of cancer genomics yet is thought to miss many structural v

 
Page Top