ライフサイエンスコーパス: cancer genomics

1 ts in both risk groups, which is violated in cancer genomics.

2 isASE has potential for wide applications in cancer genomics.

3 t across human cancers is a key challenge in cancer genomics.

4 ulations within tumors is a key challenge in cancer genomics.

5 up unprecedented opportunities to transform cancer genomics.

6 ntal problem for statistical methods used in cancer genomics.

7 results may help to guide the next stage of cancer genomics.

8 use the CGC to investigate key questions in cancer genomics.

9 m, passenger mutations is a key challenge in cancer genomics.

10 y relevant features for survival analysis in cancer genomics.

11 l translocations is an important question in cancer genomics.

12 e SVs, focusing on applications in human and cancer genomics.

13 r diverse fields, including gene therapy and cancer genomics.

14 ly, we will also discuss how the advances in cancer genomics and cancer modeling will influence each

15 ed tools to integrate, visualize and analyze cancer genomics and clinical data.

16 Here, we review recent advances in cancer genomics and discuss what the new findings have t

17 article highlights the technology underlying cancer genomics and examines the early results of genome

18 These data have implications for cancer genomics and for the targeted therapy of cancer.

19 ation changes are integral to all aspects of cancer genomics and have been shown to have important as

20 use of in situ single cell-based methods in cancer genomics and imply that chemotherapy before HER2-

21 ill likely continue to advance translational cancer genomics and precision cancer medicine.

22 cers represent a unique case with respect to cancer genomics and precision medicine, as the mutation

23 aspirates, together with recent advances in cancer genomics and single-cell molecular analysis, have

24 lacement therapy and risk for breast cancer, cancer genomics and targeted therapies, short- and long-

25 efforts are revolutionizing our knowledge of cancer genomics and tumor biology.

26 ational drug discovery, medical informatics, cancer genomics, and systems biology.

27 provide an overview of the current state of cancer genomics, appraise the current portals and tools

28 ovided practical knowledge on structural and cancer genomics approaches, as well as an exclusive plat

29 encing has been widely used for personal and cancer genomics as well as in various research areas.

30 GEPIA fills in the gap between cancer genomics big data and the delivery of integrated

31 The UCSC Cancer Genomics Browser comprises a suite of web-based t

32 The Cancer Genomics Browser currently hosts 575 public datas

33 The UCSC Cancer Genomics Browser is a set of web-based tools to d

34 The UCSC Cancer Genomics Browser is a web-based application that

35 The Seven Bridges Cancer Genomics Cloud (CGC; www.cancergenomicscloud.org)

36 The ISB Cancer Genomics Cloud (ISB-CGC) is one of three pilot pr

37 ted versions in CWL and on the Seven Bridges Cancer Genomics Cloud platform can be obtained by contac

38 n Bridges Genomics implementation of the NCI Cancer Genomics Cloud, which provides graphical interfac

39 The Global Cancer Genomics Consortium (GCGC) is an international co

40 en tested the predictions of our model using cancer genomics data and confirmed that many passengers

41 lso applied the human interactome network to cancer genomics data and identified several interaction

42 ed tools to display, investigate and analyse cancer genomics data and its associated clinical informa

43 r hosts a growing body of publicly available cancer genomics data from a variety of cancer types, inc

44 When applied to any cancer genomics data set, the algorithm can nominate onc

45 impute drug response in very large clinical cancer genomics data sets, such as The Cancer Genome Atl

46 t for this model in cancer age-incidence and cancer genomics data that also allow us to estimate the

47 We demonstrate this problem for cancer genomics data where the standard log-rank test le

48 The algorithm is fast, broadly applicable to cancer genomics data, is of immediate use for prioritizi

49 isualization tools enable the exploration of cancer genomics data, most biologists prefer simplified,

50 onfounding effects of technical artifacts in cancer genomics data, our study emphasizes the need to s

51 ta that enhances the interpretability of the cancer genomics data.

52 rative (or systems) analysis of the combined cancer genomics database.

53 We test the method on all available TCGA cancer genomics datasets, and detect multiple previously

54 ces of BiRewire by applying it to large real cancer genomics datasets.

55 Cancer genomics demonstrates that these few driver mutat

56 Diagnostic and therapeutic advances based on cancer genomics developed during a time when it was poss

57 Cancer genomics efforts have identified genes and regula

58 as well as an exclusive platform for focused cancer genomics endeavors.

59 ous mutations are rarely investigated in the cancer genomics field.

60 tools have, along with the rapid progress of cancer genomics, generated an increasingly complex under

61 technology has produced a transformation in cancer genomics, generating large data sets that can be

62 tumors or cultured samples are superior for cancer genomics has been a longstanding subject of debat

63 come more important because federally funded cancer genomics has been centralized under The Cancer Ge

64 rical view of how increased understanding of cancer genomics has been translated to the clinic and di

65 Cancer genomics has focused on the discovery of mutation

66 Advances in cancer genomics have identified numerous recurrent mutat

67 of the importance of evolutionary theory to cancer genomics have led to a proliferation of phylogene

68 tions of protein-coding genes are a focus of cancer genomics; however, the impact of oncogenes on exp

69 The next opportunity is to embed cancer genomics in clinical context so that patient-cent

70 ir tumor genetics with the goal of utilizing cancer genomics in the clinical management of pancreatic

71 Successful examples of translating cancer genomics into therapeutics and diagnostics reinfo

72 Still, cancer genomics is in its infancy.

73 The new era of cancer genomics is providing us with extensive knowledge

74 The field of cancer genomics is rapidly evolving and has led to the d

75 One of the most important recent findings in cancer genomics is the identification of novel driver mu

76 An important goal of cancer genomics is to identify systematically cancer-cau

77 A major challenge in cancer genomics is uncovering genes with an active role

78 is evident that the translation of emerging cancer genomics knowledge into clinical applications can

79 ng technologies has transformed the field of cancer genomics, leading to the identification of geneti

80 These results identify the convergence of cancer genomics, mitochondrial priming and GCT evolution

81 rees from Poliovirus, Burkholderia and human cancer genomics NGS datasets.

82 pathway analysis has been a powerful tool in cancer genomics, our knowledge of oncogenic pathway modu

83 the unprecedented conjunction of large-scale cancer genomics profiling of tumor samples in The Cancer

84 and case breast cancer datasets from the NCI cancer genomics program - The Cancer Genome Atlas (TCGA)

85 Based on this analysis, we suggest that cancer genomics projects such as The Cancer Genome Atlas

86 porate additional analyses and new data from cancer genomics projects.

87 In cancer genomics research, one important problem is that

88 y cancer drivers and guide the next stage of cancer genomics research.

89 e" on the effect of sample type selection on cancer genomics research.

90 vo RNAi screens and illustrate how combining cancer genomics, RNA interference, and mosaic mouse mode

91 , we review the key historical milestones in cancer genomics since the completion of the genome, and

92 Here, we review cancer genomics software and the insights that have been

93 ES) is widely utilized both in translational cancer genomics studies and in the setting of precision

94 r heterogeneity, tumor samples collected for cancer genomics studies are often heavily diluted with n

95 patterns and driver pathways in large-scale cancer genomics studies, and it may also be used for oth

96 e advantages of ExaLT on data from published cancer genomics studies, finding significant differences

97 Despite large-scale cancer genomics studies, key somatic mutations driving c

98 iety of cancers as identified in large-scale cancer genomics studies, provide new interfaces for expl

99 NPs and have been used widely in linkage and cancer genomics studies.

100 ting somatic CNV and SV detection methods in cancer genomics studies.

101 This article discusses several areas within cancer genomics that are being transformed by the applic

102 or Ags can be identified by directly linking cancer genomics to cancer immunology and immunotherapy.

103 the current challenges for applying prostate cancer genomics to clinical management, this review will

104 ve adequate background knowledge of clinical cancer genomics to design meaningful radiogenomics proje

105 s, and issues involved in the translation of cancer genomics to the clinic are discussed.

106 ation of a cancer dependency map, connecting cancer genomics to therapeutic dependencies.

107 ly structured will primarily fund efforts in cancer genomics with longer-term goals of advancing prec

108 A sequencing has revolutionized the study of cancer genomics with numerous discoveries that are relev