ライフサイエンスコーパス: databank

1 plexes extracted from the Brookhaven Protein DataBank.

2 00+ DBAASP entries have links to the Protein DataBank.

3 eters deposited in the University at Buffalo Databank.

4 parsely represented in the protein structure databank.

5 recovering this potential from the structure databank.

6 des for injury coding in the National Trauma Databank.

7 rials and from a large longitudinal outcomes databank.

8 a series of NMR structures from the Protein Databank.

9 f 2,140 heart failure patients from the Duke Databank.

10 ound as expressed sequence tag clones in the databank.

11 nhibitor complexes obtained from the Protein Databank.

12 ective, observational Pittsburgh Scleroderma Databank.

13 lexible sigmoidoscopy reports to the central databank.

14 present all the structures in the structural databank.

15 ally by examination of the protein structure databank.

16 ern or profile) for a subsequent scan of the databank.

17 s were entered prospectively into a computer databank.

18 present-day ORFs enumerated in the sequence databanks.

19 ied by BLAST searches from available genomic databanks.

20 atism, and Aging Medical Information System) databanks.

21 ented growth of both structural and sequence databanks.

22 ntified from comparisons to sequences in the databanks.

23 Using the large Pittsburgh Scleroderma Databank, 106 patients who had the diagnosis of PHT afte

24 score >15) patients from the National Trauma Databank 2000 to 2012.

25 dical services (GEMS) in the National Trauma Databank (2007-2012).

26 T or ground transport in the National Trauma Databank (2009-2012).

27 In the Duke Databank, 3,517 patients met criteria for inclusion and

28 tations in the Cancer Cell Line Encyclopedia databank, 68% are gynecologic cancer cells.

29 m the reference source obtained from GenBank databank (accession No. X54156).

30 Databank analysis showed that the 137 bp product shared

31 A mapping between chains in the Protein Databank and Enzyme Classification numbers is invaluable

32 PI domain I with human proteins in a protein databank and identified a peptide sharing 88% identity w

33 This study used the Pittsburgh Scleroderma Databank and included patients with diffuse scleroderma

34 explosion of protein sequences entering into databanks and the relatively much slower progress in usi

35 PPI, a classification of PPIs in the Protein Databank, and on the entire yeast genome.

36 mputed ET analyses for the protein structure databank, and on-the-fly analysis of any protein sequenc

37 tive searches of up-to-date protein sequence databanks are carried out via direct links to the MAST s

38 ing the results of a given query against the databank as a new query.

39 With protein sequences entering into databanks at an explosive pace, the early determination

40 prior heart disease from the Vanderbilt DNA databank, BioVU, which accrues subjects from routine pat

41 ly for up to 20 years (average 9 years) at 8 databank centers.

42 The availability of large EST databanks, complete plant-genome sequences and/or induci

43 The University at Buffalo Databank concept assumes transferability of electron den

44 Analysis of an expression databank derived from human glial tumors (n = 77) identi

45 sus yeast); (ii) occurrence in the structure databank (e.g. most common folds in the PDB); (iii) both

46 The Electron Microscopy DataBank (EMDB) is growing rapidly, accumulating biologi

47 he International Civil Aviation Organization databank for both taxi (same as idle) and takeoff engine

48 Using the Duke Databank for Cardiovascular Disease for the years 1995 t

49 s were linked to clinical data from the Duke Databank for Cardiovascular Disease to compare baseline

50 multivariate equation created using the Duke Databank for Cardiovascular Diseases to predict their 5-

51 nd were followed up through 2000 in the Duke Databank for Cardiovascular Diseases.

52 TP, Web and Rsync access to many high-volume databanks from several sites around the world.

53 As databanks grow, sequence classification and prediction o

54 4,713 patient records in the National Trauma Databank in 2007.

55 etrieval System (SRS), a network browser for databanks in molecular biology, integrates and links the

56 etrieval System (SRS), a network browser for databanks in molecular biology, integrates and links the

57 rieval System (SRS) is a network browser for databanks in molecular biology, integrating and linking

58 rieval System (SRS) is a Network Browser for Databanks in Molecular Biology, integrating and linking

59 This finding was replicated in the Duke Databank, in which higher RDW was strongly associated wi

60 al known multimer complexes from the Protein DataBank, including four unbound multimers: three trimer

61 rts with grouping proteins in the structural databank into families based on sequence similarity.

62 rease in new protein sequences entering into databanks, it is vitally important for both basic resear

63 y redundant for two main reasons: 1. various databanks keep redundant sequences with many identical a

64 Large-scale DNA databanks linked to electronic medical record (EMR) syst

65 case-control analyses within a longitudinal databank, matching up to 20 controls for age, sex, and t

66 in helices) between PrP(C) structures and a databank of "normal" proteins shows that the most unusua

67 t an analysis of color statistics in a large databank of natural images curated by human observers fo

68 The databank of normal proteins consists of 58 helical prote

69 A databank of oligosaccharide structures has been construc

70 strate this approach by first constructing a databank of protein structures using a model potential a

71 ting transferable potentials directly from a databank of protein structures.

72 Silks provide a "databank" of well-characterized polymorphic sequences, a

73 Templates from the protein databank (PDB) are often used as initial models that can

74 We use the protein databank (PDB) as the structural database of complexes a

75 e in the number of structures in the Protein Databank (PDB) makes it difficult to find all structures

76 protein structure databases, such as Protein DataBank (PDB), PDB in Europe (PDBe), CATH, SUPERFAMILY

77 allel G-quadruplex structures in the Protein Databank (PDB).

78 nt fraction of the structures in the protein databank (PDB).

79 orithm and structural fragments from Protein Databank (PDB).

80 tegrates tools for network analysis, Protein Databank queries, modeling of predicted protein structur

81 up adding more and more information into the databanks, questions about the accuracy and completeness

82 With the number of sequences entering into databanks rapidly increasing, the importance of developi

83 A search of the soybean databank revealed a homolog (Glyma09g36370) that contain

84 The Clinical Proteomics Programs Databank's ovarian cancer dataset and data from in-house

85 ears were obtained as part of the Pittsburgh Databank's yearly evaluation.

86 was low in patients with no (Traumatic Coma Databank score I -10%) visible intracranial pathology.

87 asis for a subsequent pattern derivation and databank search.

88 elucidated by MS fragmentation and chemical databank searches and eventually confirmed via authentic

89 Sequence databank searches are often performed iteratively, takin

90 Databank searches revealed that closely related homologs

91 Databank searches with the deduced protein sequence for

92 ribution to simulations or to the results of databank searches.

93 The University of Pittsburgh Scleroderma Databank served as the data source.

94 e databases (RSDB) derived from full protein databanks showed that the information content of sequenc

95 This approach assumes that the databank structures correspond to representative configu

96 grained protein models directly from protein databank structures.

97 relevant to extracting FEPs is contained in databanks such as UniProtKB/Swiss-Prot and a manual anal

98 increase of protein sequences entering into databanks, the current method will become a useful autom

99 ion, and drug target data derived from large databanks using a network-based approach that incorporat

100 yzed apple EST sequences available in public databanks using statistical algorithms to identify those

101 tted to 359 hospitals in the National Trauma Databank (version 7.0).

102 de epitope searches of non-redundant and EST databanks via TBLASTN, BLASTP and FASTA, even at E value

103 Using this workflow on the Protein Databank, we find that frustration produces many immedia

104 information known as "Seshat: Global History Databank." We systematically coded data on 414 societies

105 was transmitted electronically to a central databank, where data were merged from multiple sites for

106 to recognise related folds in the structure databank with a specificity comparable to other methods.

107 the three partners in the worldwide Protein DataBank (wwPDB), the consortium entrusted with the coll