Bioinformatics : the machine learning approach
An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and appl...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Baldi, Pierre [verfasserIn] Brunak, Søren [mitwirkender] |
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| Format: |
E-Book |
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| Sprache: |
Englisch |
| Erschienen: |
Cambridge, Massachusetts: MIT Press ; c2001 |
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| Ausgabe: |
2nd ed. |
| Systematik: |
|
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| Anmerkung: |
"A Bradford book.". - Includes bibliographical references. - Description based on PDF viewed 12/23/2015 |
|---|---|
| Umfang: |
1 PDF (xxi, 452 pages) ; illustrations. |
| Beschreibung: |
Mode of access: World Wide Web. |
| Weitere Ausgabe: |
Erscheint auch als Druck-Ausgabe Baldi, Pierre, 1957 -: Bioinformatics - Cambridge, Mass. [u.a.] : MIT Press, 2001 |
|---|
| Reihe: |
Adaptive computation and machine learning series |
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| Links: | |
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| ISBN: |
978-0-262-25570-7 |
| Katalog-ID: |
1727351207 |
|---|
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| 520 | |a An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models--and to automate the process as much as possible.In this book Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology.This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised. | ||
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| allfields |
9780262255707 ebook 978-0-262-25570-7 (DE-627)1727351207 (DE-599)KEP055117392 (OCoLC)704753891 (ZBM)0992.92024 (MITPRESS)6267217 (EBP)055117392 DE-627 ger DE-627 rda eng WC 7700 SEPA rvk (DE-625)rvk/148144: *92C40 msc 92-02 msc 92B05 msc 68T05 msc 42.11 bkl 54.72 bkl 42.13 bkl Baldi, Pierre verfasserin aut Bioinformatics the machine learning approach Pierre Baldi, Søren Brunak 2nd ed. Cambridge, Massachusetts MIT Press c2001 1 PDF (xxi, 452 pages) illustrations. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Adaptive computation and machine learning series "A Bradford book.". - Includes bibliographical references. - Description based on PDF viewed 12/23/2015 An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models--and to automate the process as much as possible.In this book Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology.This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised. Mode of access: World Wide Web. Bioinformatics Molecular biology ; Computer simulation Molecular biology ; Mathematical models Neural networks (Computer science) Machine learning Markov processes Computational Biology ; methods Artificial Intelligence Markov Chains Models, Theoretical Neural Networks (Computer) s (DE-588)4611085-9 (DE-627)326351531 (DE-576)214246019 Bioinformatik gnd s (DE-588)4039983-7 (DE-627)106225812 (DE-576)209037520 Molekularbiologie gnd s (DE-588)4148259-1 (DE-627)105570354 (DE-576)209763132 Computersimulation gnd s (DE-588)4226127-2 (DE-627)104455810 (DE-576)210311614 Neuronales Netz gnd s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd (DE-627) Brunak, Søren mitwirkender ctb 9780262025065 Erscheint auch als Druck-Ausgabe 9780262025065 Erscheint auch als Druck-Ausgabe Baldi, Pierre, 1957 - Bioinformatics 2. ed Cambridge, Mass. [u.a.] : MIT Press, 2001 XXI, 452 S (DE-627)326009906 (DE-576)095327800 026202506X https://ieeexplore.ieee.org/book/6267217 X:MITPRESS Verlag lizenzpflichtig https://zbmath.org/?q=an:0992.92024 B:ZBM 2021-04-12 Verlag Zentralblatt MATH Inhaltstext ZDB-37-IEM 2012 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP SSG-OPC-MAT GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2015 ISIL_DE-93 WC 7700 Allgemeines Biologie Methoden Biometrie, Biomathematik, Biostatistik, Bioinformatik, Biokybernetik Bioinformatik Allgemeines (DE-627)1271966077 (DE-625)rvk/148144: (DE-576)201966077 42.11 Biomathematik Biokybernetik SEPA (DE-627)106421425 54.72 Künstliche Intelligenz SEPA (DE-627)10641240X 42.13 Molekularbiologie SEPA (DE-627)106410458 BO 045F 572.8/01/13 22 01 0018 384847722X olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 395724272X olr-MIT i z 23-07-21 100 01 3100 4472469839 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. Kopien, Downloads) sind nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 30-01-24 370 01 4370 4011224154 olr-ebook mitieee Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 01-12-21 2015 01 DE-93 3740753587 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/6267217 22 01 0018 Nur für Angehörige der Universität Hamburg: Volltextzugang von außerhalb des Campus http://emedien.sub.uni-hamburg.de/han/ieee/ieeexplore.ieee.org/book/6267217 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/6267217 100 01 3100 https://ieeexplore.ieee.org/book/6267217 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/6267217 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/6267217 2015 01 DE-93 https://ieeexplore.ieee.org/book/6267217 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 370 01 4370 2021.12.01 |
| spelling |
9780262255707 ebook 978-0-262-25570-7 (DE-627)1727351207 (DE-599)KEP055117392 (OCoLC)704753891 (ZBM)0992.92024 (MITPRESS)6267217 (EBP)055117392 DE-627 ger DE-627 rda eng WC 7700 SEPA rvk (DE-625)rvk/148144: *92C40 msc 92-02 msc 92B05 msc 68T05 msc 42.11 bkl 54.72 bkl 42.13 bkl Baldi, Pierre verfasserin aut Bioinformatics the machine learning approach Pierre Baldi, Søren Brunak 2nd ed. Cambridge, Massachusetts MIT Press c2001 1 PDF (xxi, 452 pages) illustrations. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Adaptive computation and machine learning series "A Bradford book.". - Includes bibliographical references. - Description based on PDF viewed 12/23/2015 An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models--and to automate the process as much as possible.In this book Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology.This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised. Mode of access: World Wide Web. Bioinformatics Molecular biology ; Computer simulation Molecular biology ; Mathematical models Neural networks (Computer science) Machine learning Markov processes Computational Biology ; methods Artificial Intelligence Markov Chains Models, Theoretical Neural Networks (Computer) s (DE-588)4611085-9 (DE-627)326351531 (DE-576)214246019 Bioinformatik gnd s (DE-588)4039983-7 (DE-627)106225812 (DE-576)209037520 Molekularbiologie gnd s (DE-588)4148259-1 (DE-627)105570354 (DE-576)209763132 Computersimulation gnd s (DE-588)4226127-2 (DE-627)104455810 (DE-576)210311614 Neuronales Netz gnd s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd (DE-627) Brunak, Søren mitwirkender ctb 9780262025065 Erscheint auch als Druck-Ausgabe 9780262025065 Erscheint auch als Druck-Ausgabe Baldi, Pierre, 1957 - Bioinformatics 2. ed Cambridge, Mass. [u.a.] : MIT Press, 2001 XXI, 452 S (DE-627)326009906 (DE-576)095327800 026202506X https://ieeexplore.ieee.org/book/6267217 X:MITPRESS Verlag lizenzpflichtig https://zbmath.org/?q=an:0992.92024 B:ZBM 2021-04-12 Verlag Zentralblatt MATH Inhaltstext ZDB-37-IEM 2012 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP SSG-OPC-MAT GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2015 ISIL_DE-93 WC 7700 Allgemeines Biologie Methoden Biometrie, Biomathematik, Biostatistik, Bioinformatik, Biokybernetik Bioinformatik Allgemeines (DE-627)1271966077 (DE-625)rvk/148144: (DE-576)201966077 42.11 Biomathematik Biokybernetik SEPA (DE-627)106421425 54.72 Künstliche Intelligenz SEPA (DE-627)10641240X 42.13 Molekularbiologie SEPA (DE-627)106410458 BO 045F 572.8/01/13 22 01 0018 384847722X olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 395724272X olr-MIT i z 23-07-21 100 01 3100 4472469839 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. Kopien, Downloads) sind nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 30-01-24 370 01 4370 4011224154 olr-ebook mitieee Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 01-12-21 2015 01 DE-93 3740753587 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/6267217 22 01 0018 Nur für Angehörige der Universität Hamburg: Volltextzugang von außerhalb des Campus http://emedien.sub.uni-hamburg.de/han/ieee/ieeexplore.ieee.org/book/6267217 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/6267217 100 01 3100 https://ieeexplore.ieee.org/book/6267217 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/6267217 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/6267217 2015 01 DE-93 https://ieeexplore.ieee.org/book/6267217 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 370 01 4370 2021.12.01 |
| allfields_unstemmed |
9780262255707 ebook 978-0-262-25570-7 (DE-627)1727351207 (DE-599)KEP055117392 (OCoLC)704753891 (ZBM)0992.92024 (MITPRESS)6267217 (EBP)055117392 DE-627 ger DE-627 rda eng WC 7700 SEPA rvk (DE-625)rvk/148144: *92C40 msc 92-02 msc 92B05 msc 68T05 msc 42.11 bkl 54.72 bkl 42.13 bkl Baldi, Pierre verfasserin aut Bioinformatics the machine learning approach Pierre Baldi, Søren Brunak 2nd ed. Cambridge, Massachusetts MIT Press c2001 1 PDF (xxi, 452 pages) illustrations. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Adaptive computation and machine learning series "A Bradford book.". - Includes bibliographical references. - Description based on PDF viewed 12/23/2015 An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models--and to automate the process as much as possible.In this book Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology.This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised. Mode of access: World Wide Web. Bioinformatics Molecular biology ; Computer simulation Molecular biology ; Mathematical models Neural networks (Computer science) Machine learning Markov processes Computational Biology ; methods Artificial Intelligence Markov Chains Models, Theoretical Neural Networks (Computer) s (DE-588)4611085-9 (DE-627)326351531 (DE-576)214246019 Bioinformatik gnd s (DE-588)4039983-7 (DE-627)106225812 (DE-576)209037520 Molekularbiologie gnd s (DE-588)4148259-1 (DE-627)105570354 (DE-576)209763132 Computersimulation gnd s (DE-588)4226127-2 (DE-627)104455810 (DE-576)210311614 Neuronales Netz gnd s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd (DE-627) Brunak, Søren mitwirkender ctb 9780262025065 Erscheint auch als Druck-Ausgabe 9780262025065 Erscheint auch als Druck-Ausgabe Baldi, Pierre, 1957 - Bioinformatics 2. ed Cambridge, Mass. [u.a.] : MIT Press, 2001 XXI, 452 S (DE-627)326009906 (DE-576)095327800 026202506X https://ieeexplore.ieee.org/book/6267217 X:MITPRESS Verlag lizenzpflichtig https://zbmath.org/?q=an:0992.92024 B:ZBM 2021-04-12 Verlag Zentralblatt MATH Inhaltstext ZDB-37-IEM 2012 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP SSG-OPC-MAT GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2015 ISIL_DE-93 WC 7700 Allgemeines Biologie Methoden Biometrie, Biomathematik, Biostatistik, Bioinformatik, Biokybernetik Bioinformatik Allgemeines (DE-627)1271966077 (DE-625)rvk/148144: (DE-576)201966077 42.11 Biomathematik Biokybernetik SEPA (DE-627)106421425 54.72 Künstliche Intelligenz SEPA (DE-627)10641240X 42.13 Molekularbiologie SEPA (DE-627)106410458 BO 045F 572.8/01/13 22 01 0018 384847722X olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 395724272X olr-MIT i z 23-07-21 100 01 3100 4472469839 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. Kopien, Downloads) sind nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 30-01-24 370 01 4370 4011224154 olr-ebook mitieee Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 01-12-21 2015 01 DE-93 3740753587 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/6267217 22 01 0018 Nur für Angehörige der Universität Hamburg: Volltextzugang von außerhalb des Campus http://emedien.sub.uni-hamburg.de/han/ieee/ieeexplore.ieee.org/book/6267217 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/6267217 100 01 3100 https://ieeexplore.ieee.org/book/6267217 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/6267217 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/6267217 2015 01 DE-93 https://ieeexplore.ieee.org/book/6267217 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 370 01 4370 2021.12.01 |
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9780262255707 ebook 978-0-262-25570-7 (DE-627)1727351207 (DE-599)KEP055117392 (OCoLC)704753891 (ZBM)0992.92024 (MITPRESS)6267217 (EBP)055117392 DE-627 ger DE-627 rda eng WC 7700 SEPA rvk (DE-625)rvk/148144: *92C40 msc 92-02 msc 92B05 msc 68T05 msc 42.11 bkl 54.72 bkl 42.13 bkl Baldi, Pierre verfasserin aut Bioinformatics the machine learning approach Pierre Baldi, Søren Brunak 2nd ed. Cambridge, Massachusetts MIT Press c2001 1 PDF (xxi, 452 pages) illustrations. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Adaptive computation and machine learning series "A Bradford book.". - Includes bibliographical references. - Description based on PDF viewed 12/23/2015 An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models--and to automate the process as much as possible.In this book Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology.This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised. Mode of access: World Wide Web. Bioinformatics Molecular biology ; Computer simulation Molecular biology ; Mathematical models Neural networks (Computer science) Machine learning Markov processes Computational Biology ; methods Artificial Intelligence Markov Chains Models, Theoretical Neural Networks (Computer) s (DE-588)4611085-9 (DE-627)326351531 (DE-576)214246019 Bioinformatik gnd s (DE-588)4039983-7 (DE-627)106225812 (DE-576)209037520 Molekularbiologie gnd s (DE-588)4148259-1 (DE-627)105570354 (DE-576)209763132 Computersimulation gnd s (DE-588)4226127-2 (DE-627)104455810 (DE-576)210311614 Neuronales Netz gnd s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd (DE-627) Brunak, Søren mitwirkender ctb 9780262025065 Erscheint auch als Druck-Ausgabe 9780262025065 Erscheint auch als Druck-Ausgabe Baldi, Pierre, 1957 - Bioinformatics 2. ed Cambridge, Mass. [u.a.] : MIT Press, 2001 XXI, 452 S (DE-627)326009906 (DE-576)095327800 026202506X https://ieeexplore.ieee.org/book/6267217 X:MITPRESS Verlag lizenzpflichtig https://zbmath.org/?q=an:0992.92024 B:ZBM 2021-04-12 Verlag Zentralblatt MATH Inhaltstext ZDB-37-IEM 2012 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP SSG-OPC-MAT GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2015 ISIL_DE-93 WC 7700 Allgemeines Biologie Methoden Biometrie, Biomathematik, Biostatistik, Bioinformatik, Biokybernetik Bioinformatik Allgemeines (DE-627)1271966077 (DE-625)rvk/148144: (DE-576)201966077 42.11 Biomathematik Biokybernetik SEPA (DE-627)106421425 54.72 Künstliche Intelligenz SEPA (DE-627)10641240X 42.13 Molekularbiologie SEPA (DE-627)106410458 BO 045F 572.8/01/13 22 01 0018 384847722X olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 395724272X olr-MIT i z 23-07-21 100 01 3100 4472469839 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. Kopien, Downloads) sind nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 30-01-24 370 01 4370 4011224154 olr-ebook mitieee Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 01-12-21 2015 01 DE-93 3740753587 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/6267217 22 01 0018 Nur für Angehörige der Universität Hamburg: Volltextzugang von außerhalb des Campus http://emedien.sub.uni-hamburg.de/han/ieee/ieeexplore.ieee.org/book/6267217 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/6267217 100 01 3100 https://ieeexplore.ieee.org/book/6267217 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/6267217 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/6267217 2015 01 DE-93 https://ieeexplore.ieee.org/book/6267217 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 370 01 4370 2021.12.01 |
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9780262255707 ebook 978-0-262-25570-7 (DE-627)1727351207 (DE-599)KEP055117392 (OCoLC)704753891 (ZBM)0992.92024 (MITPRESS)6267217 (EBP)055117392 DE-627 ger DE-627 rda eng WC 7700 SEPA rvk (DE-625)rvk/148144: *92C40 msc 92-02 msc 92B05 msc 68T05 msc 42.11 bkl 54.72 bkl 42.13 bkl Baldi, Pierre verfasserin aut Bioinformatics the machine learning approach Pierre Baldi, Søren Brunak 2nd ed. Cambridge, Massachusetts MIT Press c2001 1 PDF (xxi, 452 pages) illustrations. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Adaptive computation and machine learning series "A Bradford book.". - Includes bibliographical references. - Description based on PDF viewed 12/23/2015 An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models--and to automate the process as much as possible.In this book Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology.This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised. Mode of access: World Wide Web. Bioinformatics Molecular biology ; Computer simulation Molecular biology ; Mathematical models Neural networks (Computer science) Machine learning Markov processes Computational Biology ; methods Artificial Intelligence Markov Chains Models, Theoretical Neural Networks (Computer) s (DE-588)4611085-9 (DE-627)326351531 (DE-576)214246019 Bioinformatik gnd s (DE-588)4039983-7 (DE-627)106225812 (DE-576)209037520 Molekularbiologie gnd s (DE-588)4148259-1 (DE-627)105570354 (DE-576)209763132 Computersimulation gnd s (DE-588)4226127-2 (DE-627)104455810 (DE-576)210311614 Neuronales Netz gnd s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd (DE-627) Brunak, Søren mitwirkender ctb 9780262025065 Erscheint auch als Druck-Ausgabe 9780262025065 Erscheint auch als Druck-Ausgabe Baldi, Pierre, 1957 - Bioinformatics 2. ed Cambridge, Mass. [u.a.] : MIT Press, 2001 XXI, 452 S (DE-627)326009906 (DE-576)095327800 026202506X https://ieeexplore.ieee.org/book/6267217 X:MITPRESS Verlag lizenzpflichtig https://zbmath.org/?q=an:0992.92024 B:ZBM 2021-04-12 Verlag Zentralblatt MATH Inhaltstext ZDB-37-IEM 2012 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP SSG-OPC-MAT GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2015 ISIL_DE-93 WC 7700 Allgemeines Biologie Methoden Biometrie, Biomathematik, Biostatistik, Bioinformatik, Biokybernetik Bioinformatik Allgemeines (DE-627)1271966077 (DE-625)rvk/148144: (DE-576)201966077 42.11 Biomathematik Biokybernetik SEPA (DE-627)106421425 54.72 Künstliche Intelligenz SEPA (DE-627)10641240X 42.13 Molekularbiologie SEPA (DE-627)106410458 BO 045F 572.8/01/13 22 01 0018 384847722X olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 395724272X olr-MIT i z 23-07-21 100 01 3100 4472469839 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. Kopien, Downloads) sind nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 30-01-24 370 01 4370 4011224154 olr-ebook mitieee Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. 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An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models--and to automate the process as much as possible.In this book Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology.This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised. "A Bradford book.". - Includes bibliographical references. - Description based on PDF viewed 12/23/2015 |
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An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models--and to automate the process as much as possible.In this book Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology.This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised. "A Bradford book.". - Includes bibliographical references. - Description based on PDF viewed 12/23/2015 |
| abstract_unstemmed |
An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models--and to automate the process as much as possible.In this book Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology.This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised. "A Bradford book.". - Includes bibliographical references. - Description based on PDF viewed 12/23/2015 |
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