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 Pred, which utilizes features extracted from PSSM and residue specific contact-energy to help train a

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

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

30 am position-specific scoring matrix (Bi-gram PSSM) are employed to extract protein sequence features,

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

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

33 The optimal feature set, including PSSM-related features, achieved a Pearson correlation co

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

35 based on Position-Specific Scoring Matrices (PSSM) and contact maps to generate detailed and accurate

36 scores, Position Specific Scoring Matrices (PSSM) and physicochemical properties, and developed a ma

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

38 ices and position-specific scoring matrices (PSSM).

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

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

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

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

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

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

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

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

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

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

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

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

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

52 ttern or a position-specific scoring matrix (PSSM) encoding quantitative preferences.

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

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

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

56 The position-specific scoring matrix (PSSM) method is an excellent method for describing the e

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

58 g from the position-specific scoring matrix (PSSM) of proteins, those derived from the amino-acid seq

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

60 called the position-specific scoring matrix (PSSM) profile, alone and the accuracies of such methods

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

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

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

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

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

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

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

68 We meticulously assess the performance of PSSM-Sumo through a tenfold cross-validation approach, e

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

70 ysical features outperforms exclusive use of PSSM-based evolutionary features in predicting activatio

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

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

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

74 ably or better than algorithms using only on PSSM-based evolutionary features.

75 l results demonstrate that solely relying on PSSM input, the proposed method not only surpasses the o

76 e of homology searching against databases or PSSM matrices.

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

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

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

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

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

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

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

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

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

86 a simple strategy for dramatically reducing PSSM contamination.

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

88 iding window PSSM-PSSM comparison and scores PSSM similarity using a randomisation-based probabilisti

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

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

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

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

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

94 Comparative analyses reveal that PSSM-Sumo achieves an exceptional average prediction acc

95 The PSSM models are generated from known mammalian binding s

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

97 ire sufficient relevant information from the PSSM.

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

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

100 ework trains 41 baseline models based on the PSSM.

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

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

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

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

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

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

107 Q1 variants as normal or dysfunctional using PSSM-based evolutionary and/or biophysical descriptors.

108 if-binding determinants using sliding window PSSM-PSSM comparison and scores PSSM similarity using a