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You searched for subject:(Protein Sequence Statistics). Showing records 1 – 4 of 4 total matches.

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The Ohio State University

1. Sullivan, Brandon Joseph. Engineering Proteins from Sequence Statistics: Identifying and Understanding the Roles of Conservation and Correlation in Triosephosphate Isomerase.

Degree: PhD, Biochemistry Program, Ohio State, 2011, The Ohio State University

  The structure, function and dynamics of proteins are determined by the physical and chemical properties of their amino acids. Unfortunately, the information encapsulated within… (more)

Subjects/Keywords: Biochemistry; Bioinformatics; Biophysics; Molecular Biology; Protein Engineering; Protein Sequence Statistics; Triosephosphate Isomerase

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Sullivan, B. J. (2011). Engineering Proteins from Sequence Statistics: Identifying and Understanding the Roles of Conservation and Correlation in Triosephosphate Isomerase. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1325106135

Chicago Manual of Style (16th Edition):

Sullivan, Brandon Joseph. “Engineering Proteins from Sequence Statistics: Identifying and Understanding the Roles of Conservation and Correlation in Triosephosphate Isomerase.” 2011. Doctoral Dissertation, The Ohio State University. Accessed August 06, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1325106135.

MLA Handbook (7th Edition):

Sullivan, Brandon Joseph. “Engineering Proteins from Sequence Statistics: Identifying and Understanding the Roles of Conservation and Correlation in Triosephosphate Isomerase.” 2011. Web. 06 Aug 2020.

Vancouver:

Sullivan BJ. Engineering Proteins from Sequence Statistics: Identifying and Understanding the Roles of Conservation and Correlation in Triosephosphate Isomerase. [Internet] [Doctoral dissertation]. The Ohio State University; 2011. [cited 2020 Aug 06]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1325106135.

Council of Science Editors:

Sullivan BJ. Engineering Proteins from Sequence Statistics: Identifying and Understanding the Roles of Conservation and Correlation in Triosephosphate Isomerase. [Doctoral Dissertation]. The Ohio State University; 2011. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1325106135


Florida International University

2. Ahrens, Joseph Boehm. Computational Analysis of Large-Scale Trends and Dynamics in Eukaryotic Protein Family Evolution.

Degree: PhD, Biology, 2019, Florida International University

  The myriad protein-coding genes found in present-day eukaryotes arose from a combination of speciation and gene duplication events, spanning more than one billion years… (more)

Subjects/Keywords: protein; sequence; evolution; eukaryotes; rates; disorder; structure; orthology; paralogy; Applied Statistics; Bioinformatics; Biostatistics; Computational Biology; Evolution; Genomics; Molecular Biology; Molecular Genetics; Statistical Models; Theory and Algorithms

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APA (6th Edition):

Ahrens, J. B. (2019). Computational Analysis of Large-Scale Trends and Dynamics in Eukaryotic Protein Family Evolution. (Doctoral Dissertation). Florida International University. Retrieved from https://digitalcommons.fiu.edu/etd/4039 ; FIDC007082

Chicago Manual of Style (16th Edition):

Ahrens, Joseph Boehm. “Computational Analysis of Large-Scale Trends and Dynamics in Eukaryotic Protein Family Evolution.” 2019. Doctoral Dissertation, Florida International University. Accessed August 06, 2020. https://digitalcommons.fiu.edu/etd/4039 ; FIDC007082.

MLA Handbook (7th Edition):

Ahrens, Joseph Boehm. “Computational Analysis of Large-Scale Trends and Dynamics in Eukaryotic Protein Family Evolution.” 2019. Web. 06 Aug 2020.

Vancouver:

Ahrens JB. Computational Analysis of Large-Scale Trends and Dynamics in Eukaryotic Protein Family Evolution. [Internet] [Doctoral dissertation]. Florida International University; 2019. [cited 2020 Aug 06]. Available from: https://digitalcommons.fiu.edu/etd/4039 ; FIDC007082.

Council of Science Editors:

Ahrens JB. Computational Analysis of Large-Scale Trends and Dynamics in Eukaryotic Protein Family Evolution. [Doctoral Dissertation]. Florida International University; 2019. Available from: https://digitalcommons.fiu.edu/etd/4039 ; FIDC007082

3. Wang, Bo. Novel statistical methods for evaluation of metabolic biomarkers applied to human cancer cell lines.

Degree: PhD, Chemistry, 2014, Miami University

 Metabonomics is a novel tool to investigate diseases and therapeutic treatments. This dissertation describes novel statistical methods for metabonomics based on nuclear magnetic resonance (NMR)… (more)

Subjects/Keywords: Chemistry; Biochemistry; Biostatistics; Bioanalytical methods; Bioassays; Biological samples; Chemometrics; Statistics; NMR; Metabolomics; Metabonomics; Principal components analysis; Protein sequence analysis; Cancer biology; Metabolic regulation; Biomarker validation

…84 Figure 4.5 Scores plot from the PCA of the 1414 protein sequence subset of Pfam04327… …protein sequence analysis. 1.2 Metabonomics instrumentation NMR and HPLC-MS are widely used… …applied to analysis of cancer cell metabolism and to analysis of protein sequence families. The… …in protein sequence analysis [110]. Sequence analysis of large protein families… …different sizes of samples with good performance, and provides a useful tool in protein sequence… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wang, B. (2014). Novel statistical methods for evaluation of metabolic biomarkers applied to human cancer cell lines. (Doctoral Dissertation). Miami University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=miami1399046331

Chicago Manual of Style (16th Edition):

Wang, Bo. “Novel statistical methods for evaluation of metabolic biomarkers applied to human cancer cell lines.” 2014. Doctoral Dissertation, Miami University. Accessed August 06, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=miami1399046331.

MLA Handbook (7th Edition):

Wang, Bo. “Novel statistical methods for evaluation of metabolic biomarkers applied to human cancer cell lines.” 2014. Web. 06 Aug 2020.

Vancouver:

Wang B. Novel statistical methods for evaluation of metabolic biomarkers applied to human cancer cell lines. [Internet] [Doctoral dissertation]. Miami University; 2014. [cited 2020 Aug 06]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=miami1399046331.

Council of Science Editors:

Wang B. Novel statistical methods for evaluation of metabolic biomarkers applied to human cancer cell lines. [Doctoral Dissertation]. Miami University; 2014. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=miami1399046331

4. Morozov, Vyacheslav. Computational Methods for Inferring Transcription Factor Binding Sites .

Degree: 2012, University of Ottawa

 Position weight matrices (PWMs) have become a tool of choice for the identification of transcription factor binding sites in DNA sequences. PWMs are compiled from… (more)

Subjects/Keywords: machine learning; transcriptional regulatory sites; transcription factor; protein binding; PWM; PSSM; DNA sequence; optimization; statistics; binding site; prediction; computational methods; Matthews correlation; Drosophila melanogaster; binding motif; weight matrix; DNA sequence analysis

…from the DNA to the machinery of protein synthesis. Transcription factors are often… …classified based on the sequence similarity and hence the tertiary structure of their DNA-binding… …domains [71]. Each TF as a molecule has a characteristic three-dimensional protein… …of the protein organized into a certain geometric form. Structural motifs are used to… …which a TF can have multiple sequence specificities and bind a number of different recognition… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Morozov, V. (2012). Computational Methods for Inferring Transcription Factor Binding Sites . (Thesis). University of Ottawa. Retrieved from http://hdl.handle.net/10393/23382

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Morozov, Vyacheslav. “Computational Methods for Inferring Transcription Factor Binding Sites .” 2012. Thesis, University of Ottawa. Accessed August 06, 2020. http://hdl.handle.net/10393/23382.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Morozov, Vyacheslav. “Computational Methods for Inferring Transcription Factor Binding Sites .” 2012. Web. 06 Aug 2020.

Vancouver:

Morozov V. Computational Methods for Inferring Transcription Factor Binding Sites . [Internet] [Thesis]. University of Ottawa; 2012. [cited 2020 Aug 06]. Available from: http://hdl.handle.net/10393/23382.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Morozov V. Computational Methods for Inferring Transcription Factor Binding Sites . [Thesis]. University of Ottawa; 2012. Available from: http://hdl.handle.net/10393/23382

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

.