scholarly journals Substitution scoring matrices for proteins ‐ An overview

2020 ◽  
Vol 29 (11) ◽  
pp. 2150-2163
Author(s):  
Rakesh Trivedi ◽  
Hampapathalu Adimurthy Nagarajaram
Keyword(s):  
Scoring Matrices

scholarly journals Cyberdependency and School Performance among Students in 3rd Grade of Bingerville High School

2020 ◽  
Vol 3 (3) ◽  
pp. p47
Author(s):  
DJELLE Opely Patrice-Aime
Keyword(s):  
High School ◽  
Social Networks ◽  
Academic Performance ◽  
School Performance ◽  
Female Students ◽  
The Internet ◽  
Scoring Matrices ◽  
School Learning ◽  
Use Of The Internet ◽  
3Rd Grade

This study examines the link between cyberdependency and school performance among students in the 3rd grade of the Mamie Houphouët Fêtai High School in Bingerville. It covers a sample of one hundred and ninety (190) female students between the ages of 14 and 17. Students’ addiction to the Internet and social networks is measured using a questionnaire based on Vavassori et al. (2002) and Young’s Internet Addiction Test in its French version validated by Khazaal (2008). As for academic performance, they are verified using the end-of-term scoring matrices. The results, obtained using student T and Anova, show that students in the third grade using the Internet as teaching tools have higher academic performance than their peers who use it as entertaining instruments. All these different results are explained by the models of Zuckerman (1969) and Viau (1994). Ultimately, this study will inform and raise awareness among students, educational system actors and parents about the risks of excessive use of the Internet and social networks on school learning.

Fast Search Algorithms for Position Specific Scoring Matrices

2007 ◽  
pp. 239-250 ◽  
Author(s):  
Cinzia Pizzi ◽  
Pasi Rastas ◽  
Esko Ukkonen
Keyword(s):  
Search Algorithms ◽  
Fast Search ◽  
Scoring Matrices ◽  
Fast Search Algorithms

Sequence alignment of citrate synthase proteins using a multiple sequence alignment algorithm and multiple scoring matrices

1989 ◽  
Vol 2 (8) ◽  
pp. 597-604 ◽  
Author(s):  
Christina M. Henneke ◽  
Michael J. Danson ◽  
David W. Hough ◽  
David J. Osguthorpe
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Citrate Synthase ◽  
Alignment Algorithm ◽  
Multiple Sequence ◽  
Sequence Alignment Algorithm ◽  
Scoring Matrices

Scoring Matrices

2012 ◽  
pp. 101-122
Keyword(s):  
Scoring Matrices

scholarly journals Bigram-PGK: phosphoglycerylation prediction using the technique of bigram probabilities of position specific scoring matrix

2019 ◽  
Vol 20 (S2) ◽  
Author(s):  
Abel Chandra ◽  
Alok Sharma ◽  
Abdollah Dehzangi ◽  
Daichi Shigemizu ◽  
Tatsuhiko Tsunoda
Keyword(s):  
Amino Acids ◽  
Cell Biology ◽  
Covalent Modification ◽  
Evolutionary Information ◽  
Support Vector ◽  
Computational Techniques ◽  
Post Translational Modification ◽  
Statistical Measures ◽  
Scoring Matrices ◽  
Lysine Residues

Abstract Background The biological process known as post-translational modification (PTM) is a condition whereby proteomes are modified that affects normal cell biology, and hence the pathogenesis. A number of PTMs have been discovered in the recent years and lysine phosphoglycerylation is one of the fairly recent developments. Even with a large number of proteins being sequenced in the post-genomic era, the identification of phosphoglycerylation remains a big challenge due to factors such as cost, time consumption and inefficiency involved in the experimental efforts. To overcome this issue, computational techniques have emerged to accurately identify phosphoglycerylated lysine residues. However, the computational techniques proposed so far hold limitations to correctly predict this covalent modification. Results We propose a new predictor in this paper called Bigram-PGK which uses evolutionary information of amino acids to try and predict phosphoglycerylated sites. The benchmark dataset which contains experimentally labelled sites is employed for this purpose and profile bigram occurrences is calculated from position specific scoring matrices of amino acids in the protein sequences. The statistical measures of this work, such as sensitivity, specificity, precision, accuracy, Mathews correlation coefficient and area under ROC curve have been reported to be 0.9642, 0.8973, 0.8253, 0.9193, 0.8330, 0.9306, respectively. Conclusions The proposed predictor, based on the feature of evolutionary information and support vector machine classifier, has shown great potential to effectively predict phosphoglycerylated and non-phosphoglycerylated lysine residues when compared against the existing predictors. The data and software of this work can be acquired from https://github.com/abelavit/Bigram-PGK.

Improved sensitivity of nucleic acid database searches using application-specific scoring matrices

Methods ◽  
1991 ◽  
Vol 3 (1) ◽  
pp. 66-70 ◽  
Author(s):  
D STATES ◽  
W GISH ◽  
S ALTSCHUL
Keyword(s):  
Nucleic Acid ◽  
Scoring Matrices ◽  
Application Specific

scholarly journals Using substitution probabilities to improve position-specific scoring matrices

1996 ◽  
Vol 12 (2) ◽  
pp. 135-143 ◽  
Author(s):  
Jorja G. Henikoff ◽  
Steven Henikoff
Keyword(s):  
Scoring Matrices

scholarly journals Fast index based algorithms and software for matching position specific scoring matrices

2006 ◽  
Vol 7 (1) ◽  
Author(s):  
Michael Beckstette ◽  
Robert Homann ◽  
Robert Giegerich ◽  
Stefan Kurtz
Keyword(s):  
Scoring Matrices

scholarly journals The value of position-specific scoring matrices for assessment of protein allegenicity

2008 ◽  
Vol 9 (Suppl 12) ◽  
pp. S21 ◽  
Author(s):  
Shen Lim ◽  
Joo Tong ◽  
Fook Chew ◽  
Martti T Tammi
Keyword(s):  
Scoring Matrices
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