Methods for computational gene prediction:
Inferring the precise locations and splicing patterns of genes in DNA is a difficult but important task, with broad applications to biomedicine. The mathematical and statistical techniques that have been applied to this problem are surveyed and organized into a logical framework based on the theory...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2007
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| Schlagworte: | |
| Online-Zugang: | BSB01 FHN01 Volltext |
| Zusammenfassung: | Inferring the precise locations and splicing patterns of genes in DNA is a difficult but important task, with broad applications to biomedicine. The mathematical and statistical techniques that have been applied to this problem are surveyed and organized into a logical framework based on the theory of parsing. Both established approaches and methods at the forefront of current research are discussed. Numerous case studies of existing software systems are provided, in addition to detailed examples that work through the actual implementation of effective gene-predictors using hidden Markov models and other machine-learning techniques. Background material on probability theory, discrete mathematics, computer science, and molecular biology is provided, making the book accessible to students and researchers from across the life and computational sciences. This book is ideal for use in a first course in bioinformatics at graduate or advanced undergraduate level, and for anyone wanting to keep pace with this rapidly-advancing field |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 05 Oct 2015) |
| Beschreibung: | 1 online resource (xvii, 430 pages) |
| ISBN: | 9780511811135 |
| DOI: | 10.1017/CBO9780511811135 |
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| 520 | |a Inferring the precise locations and splicing patterns of genes in DNA is a difficult but important task, with broad applications to biomedicine. The mathematical and statistical techniques that have been applied to this problem are surveyed and organized into a logical framework based on the theory of parsing. Both established approaches and methods at the forefront of current research are discussed. Numerous case studies of existing software systems are provided, in addition to detailed examples that work through the actual implementation of effective gene-predictors using hidden Markov models and other machine-learning techniques. Background material on probability theory, discrete mathematics, computer science, and molecular biology is provided, making the book accessible to students and researchers from across the life and computational sciences. This book is ideal for use in a first course in bioinformatics at graduate or advanced undergraduate level, and for anyone wanting to keep pace with this rapidly-advancing field | ||
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Datensatz im Suchindex
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| author | Majoros, William H. |
| author_facet | Majoros, William H. |
| author_role | aut |
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| contents | 1. Introduction -- 2. Mathematical preliminaries -- 3. Overview of gene prediction -- 4. Gene finder evaluation -- 5. A toy Exon finder -- 6. Hidden Markov models -- 7. Signal and content sensors -- 8. Generalized hidden Markov models -- 9. Comparative gene finding -- 10. Machine Learning methods -- 11. Tips and tricks -- 12. Advanced topics |
| ctrlnum | (ZDB-20-CBO)CR9780511811135 (OCoLC)859643049 (DE-599)BVBBV043943817 |
| dewey-full | 572.860285 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 572 - Biochemistry |
| dewey-raw | 572.860285 |
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| dewey-tens | 570 - Biology |
| discipline | Biologie |
| doi_str_mv | 10.1017/CBO9780511811135 |
| format | Electronic eBook |
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| isbn | 9780511811135 |
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| spelling | Majoros, William H. Verfasser aut Methods for computational gene prediction William H. Majoros Cambridge Cambridge University Press 2007 1 online resource (xvii, 430 pages) txt rdacontent c rdamedia cr rdacarrier Title from publisher's bibliographic system (viewed on 05 Oct 2015) 1. Introduction -- 2. Mathematical preliminaries -- 3. Overview of gene prediction -- 4. Gene finder evaluation -- 5. A toy Exon finder -- 6. Hidden Markov models -- 7. Signal and content sensors -- 8. Generalized hidden Markov models -- 9. Comparative gene finding -- 10. Machine Learning methods -- 11. Tips and tricks -- 12. Advanced topics Inferring the precise locations and splicing patterns of genes in DNA is a difficult but important task, with broad applications to biomedicine. The mathematical and statistical techniques that have been applied to this problem are surveyed and organized into a logical framework based on the theory of parsing. Both established approaches and methods at the forefront of current research are discussed. Numerous case studies of existing software systems are provided, in addition to detailed examples that work through the actual implementation of effective gene-predictors using hidden Markov models and other machine-learning techniques. Background material on probability theory, discrete mathematics, computer science, and molecular biology is provided, making the book accessible to students and researchers from across the life and computational sciences. This book is ideal for use in a first course in bioinformatics at graduate or advanced undergraduate level, and for anyone wanting to keep pace with this rapidly-advancing field Datenverarbeitung Mathematik Genomics / Data processing Bioinformatics Molecular genetics / Data processing Molecular genetics / Data processing / Case studies Molecular genetics / Mathematics Molecular genetics / Mathematics / Case studies Bioinformatik (DE-588)4611085-9 gnd rswk-swf Genanalyse (DE-588)4200230-8 gnd rswk-swf Molekulare Bioinformatik (DE-588)4531334-9 gnd rswk-swf (DE-588)4522595-3 Fallstudiensammlung gnd-content Genanalyse (DE-588)4200230-8 s Bioinformatik (DE-588)4611085-9 s 1\p DE-604 Molekulare Bioinformatik (DE-588)4531334-9 s 2\p DE-604 Erscheint auch als Druckausgabe 978-0-521-70694-0 Erscheint auch als Druckausgabe 978-0-521-87751-0 https://doi.org/10.1017/CBO9780511811135 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Majoros, William H. Methods for computational gene prediction 1. Introduction -- 2. Mathematical preliminaries -- 3. Overview of gene prediction -- 4. Gene finder evaluation -- 5. A toy Exon finder -- 6. Hidden Markov models -- 7. Signal and content sensors -- 8. Generalized hidden Markov models -- 9. Comparative gene finding -- 10. Machine Learning methods -- 11. Tips and tricks -- 12. Advanced topics Datenverarbeitung Mathematik Genomics / Data processing Bioinformatics Molecular genetics / Data processing Molecular genetics / Data processing / Case studies Molecular genetics / Mathematics Molecular genetics / Mathematics / Case studies Bioinformatik (DE-588)4611085-9 gnd Genanalyse (DE-588)4200230-8 gnd Molekulare Bioinformatik (DE-588)4531334-9 gnd |
| subject_GND | (DE-588)4611085-9 (DE-588)4200230-8 (DE-588)4531334-9 (DE-588)4522595-3 |
| title | Methods for computational gene prediction |
| title_auth | Methods for computational gene prediction |
| title_exact_search | Methods for computational gene prediction |
| title_full | Methods for computational gene prediction William H. Majoros |
| title_fullStr | Methods for computational gene prediction William H. Majoros |
| title_full_unstemmed | Methods for computational gene prediction William H. Majoros |
| title_short | Methods for computational gene prediction |
| title_sort | methods for computational gene prediction |
| topic | Datenverarbeitung Mathematik Genomics / Data processing Bioinformatics Molecular genetics / Data processing Molecular genetics / Data processing / Case studies Molecular genetics / Mathematics Molecular genetics / Mathematics / Case studies Bioinformatik (DE-588)4611085-9 gnd Genanalyse (DE-588)4200230-8 gnd Molekulare Bioinformatik (DE-588)4531334-9 gnd |
| topic_facet | Datenverarbeitung Mathematik Genomics / Data processing Bioinformatics Molecular genetics / Data processing Molecular genetics / Data processing / Case studies Molecular genetics / Mathematics Molecular genetics / Mathematics / Case studies Bioinformatik Genanalyse Molekulare Bioinformatik Fallstudiensammlung |
| url | https://doi.org/10.1017/CBO9780511811135 |
| work_keys_str_mv | AT majoroswilliamh methodsforcomputationalgeneprediction |