ライフサイエンスコーパス: PSSM

1 PSSM-based kernel logistic regression achieves the accur

2 PSSMs can capture information about conserved patterns w

3 Given a 3D-PSSM result, an automated procedure constructs arguments

4 ing the unique expertise of the author of 3D-PSSM for distribution to users, an improvement in recall

5 m) and sequence-structure-based (SAM-T02, 3D-PSSM, mGenTHREADER) methods.

6 The 3D-PSSM server is used to obtain the preliminary 3D structu

7 trate the application of argumentation to 3D-PSSM, a protein structure prediction tool.

8 e DNA segments need to be aligned to build a PSSM.

9 We further compared a PSSM method dependent on LASAGNA to an alignment-free TF

10 One popular program for constructing a PSSM and comparing it with a database of sequences is Po

11 y build a position specific scoring model (a PSSM or HMM) that captures the pattern of sequence conse

12 f interest, given as either a consensus or a PSSM.

13 -RNA interface residue predictors that use a PSSM-based encoding of sequence windows outperform class

14 an HMM (Hidden Markov Model) and accordingly PSSM-PSSM or HMM-HMM comparison is used for homolog dete

15 Accordingly, PSSM-based feature descriptors have been successfully ap

16 sed by combining ensemble learning model and PSSM-RT to better handle the imbalance between binding a

17 many more homologs than single searches, but PSSMs can be contaminated when homologous alignments are

18 tion, however, are not generally captured by PSSM search methods.

19 tance of the pair-relationships extracted by PSSM-RT and the results validates the usefulness of PSSM

20 and 51 SI CXCR4(+) sequences) to derive a C-PSSM predictor.

21 The C-PSSM had an estimated specificity of 94% (confidence int

22 The C-PSSM performs as well on subtype C V3 loops as existing

23 OP40 (8353 proteins) for homology detection, PSSM-PSSM and HMM-HMM succeed on 48% and 52% of proteins

24 PDNA-62 and PDNA-224 are used to evaluate PSSM-RT and two existing PSSM encoding methods by five-f

25 re used to evaluate PSSM-RT and two existing PSSM encoding methods by five-fold cross-validation.

26 cogen synthase (GS), as a candidate gene for PSSM.

27 sequence similarity with that obtained from PSSM splice site models.

28 uence with a database of PSI-BLAST-generated PSSMs.

29 ing the inter-column contrast of the implied PSSMs.

30 ysis demonstrated an elevated GS activity in PSSM horses, and haplotype analysis and allele age estim

31 rch program derives the column scores of its PSSMs with the aid of pseudocounts, added to the observe

32 based on position-specific scoring matrices (PSSM).

33 Position specific score matrices (PSSMs) are derived from multiple sequence alignments to

34 orithm and Position Specific Score Matrices (PSSMs) derived from CDD alignments.

35 PSI-BLAST position-specific score matrices (PSSMs) find many more homologs than single searches, but

36 ches using position-specific score matrices (PSSMs) or profiles as queries are more effective at iden

37 rely on position-specific scoring matrices (PSSMs) constructed from aligned binding sites.

38 that use position-specific scoring matrices (PSSMs) to describe protein families.

39 acy than position-specific scoring matrices (PSSMs).

40 form of position-specific scoring matrices (PSSMs).

41 basis of position-specific scoring matrices [PSSM]) can be interpreted as revealing a propensity to u

42 sing a codon position specific score matrix (PSSM) approach.

43 ed to as the Position Specific Score Matrix (PSSM) Relation Transformation (PSSM-RT), to encode resid

44 tive profile position specific score matrix (PSSM)-based search strategy, is more sensitive than BLAS

45 ented as a position-specific scoring matrix (PSSM) based on results from oriented peptide library and

46 onstruct a position-specific scoring matrix (PSSM) for zDHHC17 AR binding, with which we predicted an

47 uch as the position-specific scoring matrix (PSSM) impose biologically unrealistic assumptions such a

48 form of a Position-Specific Scoring Matrix (PSSM) is a widely used and highly informative representa

49 nts of the position specific scoring matrix (PSSM) of proteins, the amino-acid sequence, and a matrix

50 ented as a position-specific scoring matrix (PSSM) or an HMM (Hidden Markov Model) and accordingly PS

51 ed using a position-specific scoring matrix (PSSM), also known as a position weight matrix.

52 s, such as position specific scoring matrix (PSSM), the amino-acid sequence, and secondary structural

53 that use a position-specific scoring matrix (PSSM)-based representation (PSSMSeq) outperform those th

54 PSI-BLAST position-specific scoring matrix (PSSM).

55 e standard position-specific scoring matrix (PSSM).

56 pairwise alignments between the query model (PSSM, HMM) and the subject sequences in the library.

57 Polysaccharide storage myopathy (PSSM) is a novel glycogenosis in horses characterized by

58 ine web server that can generate 21 types of PSSM-based feature descriptors, thereby addressing a cru

59 and the results validates the usefulness of PSSM-RT for encoding DNA-binding residues.

60 te the use of IMPALA to search a database of PSSMs for protein folds, and one for protein domains inv

61 bly faster when run with a large database of PSSMs than is BLAST or PSI-BLAST when run against the co

62 MRFalign outperforms several popular HMM or PSSM-based methods in terms of both alignment accuracy a

63 shall be much more sensitive than HMM-HMM or PSSM-PSSM comparison.

64 ion factors, this representation outperforms PSSMs on between 65 and 89 of the 95 transcription facto

65 For Pfam, PSSMs iteratively constructed from seeds based on HMM co

66 The tool currently stores precomputed PSSM models for 189 TFs and 133 TFs built from TFBSs in

67 atly improved performance over the prevalent PSSM-based method for the detection of eukaryotic motifs

68 mination of alignment errors during psiblast PSSM contamination suggested a simple strategy for drama

69 psiblast PSSMs are built from the query-based multiple sequence a

70 od for low-similarity datasets using reduced PSSM and position-based secondary structural features.

71 a simple strategy for dramatically reducing PSSM contamination.

72 the aligned subject sequence, the resulting PSSM rarely produces alignment over-extensions or alignm

73 then be used to build accurate and sensitive PSSM or HMM models for sequence analysis.

74 the reduced alphabets with size 13 simplify PSSM structures efficiently while reserving its maximal

75 y based representation (IDSeq) or a smoothed PSSM (SmoPSSMSeq); (ii) Structure-based classifiers that

76 tructure-based classifiers that use smoothed PSSM representation (SmoPSSMStr) outperform those that u

77 Performance evaluations indicate that PSSM-RT is more effective than previous methods.

78 The PSSM models are generated from known mammalian binding s

79 This simple step, which tends to anchor the PSSM to the original query sequence and slightly increas

80 ially included, can reduce HOE errors in the PSSM profile.

81 Application of the PSSM scoring method to reconstructed virus phylogenies o

82 and the ability to start a search using the PSSM generated from a previous PSI-BLAST search on a dif

83 nd in practice both motifs that fit well the PSSM model, and those that exhibit strong dependencies b

84 s by comparing the retrieval accuracy of the PSSMs constructed using an iterative procedure to that o

85 LTO), that aligns protein query sequences to PSSMs using rules for placing and scoring gaps that are

86 Score Matrix (PSSM) Relation Transformation (PSSM-RT), to encode residues by utilizing the relationsh

87 ation (SmoPSSMStr) outperform those that use PSSM (PSSMStr) as well as sequence identity based repres