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  • 1
    Online Resource
    Online Resource
    Bioinformatics and Machine Learning for Cancer Biology (2022)
    Basel : MDPI Books
    Details
    UID:
    almafu_9960854424502883
    Format: 1 electronic resource (196 p.)
    Content: Cancer is a leading cause of death worldwide, claiming millions of lives each year. Cancer biology is an essential research field to understand how cancer develops, evolves, and responds to therapy. By taking advantage of a series of “omics” technologies (e.g., genomics, transcriptomics, and epigenomics), computational methods in bioinformatics and machine learning can help scientists and researchers to decipher the complexity of cancer heterogeneity, tumorigenesis, and anticancer drug discovery. Particularly, bioinformatics enables the systematic interrogation and analysis of cancer from various perspectives, including genetics, epigenetics, signaling networks, cellular behavior, clinical manifestation, and epidemiology. Moreover, thanks to the influx of next-generation sequencing (NGS) data in the postgenomic era and multiple landmark cancer-focused projects, such as The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), machine learning has a uniquely advantageous role in boosting data-driven cancer research and unraveling novel methods for the prognosis, prediction, and treatment of cancer.
    Note: English
    Additional Edition: ISBN 3-0365-4814-9
    Additional Edition: ISBN 3-0365-4813-0
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    FU Berlin
    Charité
  • 2
    Online Resource
    Online Resource
    Bioinformatics and Machine Learning for Cancer Biology (2022)
    Basel : MDPI - Multidisciplinary Digital Publishing Institute
    Details
    UID:
    gbv_1841152811
    Format: 1 Online-Ressource (196 p.)
    ISBN: 9783036548142 , 9783036548135
    Content: Cancer is a leading cause of death worldwide, claiming millions of lives each year. Cancer biology is an essential research field to understand how cancer develops, evolves, and responds to therapy. By taking advantage of a series of “omics” technologies (e.g., genomics, transcriptomics, and epigenomics), computational methods in bioinformatics and machine learning can help scientists and researchers to decipher the complexity of cancer heterogeneity, tumorigenesis, and anticancer drug discovery. Particularly, bioinformatics enables the systematic interrogation and analysis of cancer from various perspectives, including genetics, epigenetics, signaling networks, cellular behavior, clinical manifestation, and epidemiology. Moreover, thanks to the influx of next-generation sequencing (NGS) data in the postgenomic era and multiple landmark cancer-focused projects, such as The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), machine learning has a uniquely advantageous role in boosting data-driven cancer research and unraveling novel methods for the prognosis, prediction, and treatment of cancer
    Note: English
    Language: Undetermined
    URL: kostenfrei
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    Stabi Berlin
    UB Potsdam
  • 3
    Online Resource
    Online Resource
    Clastic Hydrocarbon Reservoir Sedimentology (2018)
    Cham :Springer International Publishing :
    Details
    UID:
    almahu_9947545783602882
    Format: XVI, 712 p. 404 illus., 202 illus. in color. , online resource.
    ISBN: 9783319703350
    Series Statement: Advances in Oil and Gas Exploration & Production,
    Content: This book presents a comprehensive assessment of clastic sedimentology and its application to reservoir geology. It covers the theoretical foundations of the topic and its use for scientists as well as professionals in the field. Further, it addresses all aspects of reservoir sedimentology, clastic sequence stratigraphy, sedimentation, reservoir diagenesis and heterogeneity, as well as depositional systems (alluvial, fluvial, lacustrine, delta, sandy coast, neritic, deep-water) in detail. The research team responsible for this book has been investigating clastic sedimentology for more than three decades and consists of highly published and cited authors. The Chinese edition of this book has been a great success, and is popular among sedimentologists and petroleum geologists alike.
    Note: Preface -- 1. Formation, Development, and Trends in Reservoir Sedimentology -- Features of Clastic Reservoirs -- Theory and Methods for Studying Clastic Sequence Stratigraphy -- Research Methods of Sedimentary Facies and Sedimentation -- Reservoir Diagenesis -- Reservoir Heterogeneity -- Alluvial Fan Depositional System -- Fluvial Depositional System -- Lacustrine Depositional System -- Deltaic Depositional System -- Sandy Coast (Shore) and Neritic Depositional System -- Deep-water Depositional System -- References -- Index -- Glossary. .
    In: Springer eBooks
    Additional Edition: Printed edition: ISBN 9783319703343
    Language: English
    RVK:
    TZ 7500
    DOI: 10.1007/978-3-319-70335-0
    URL: http://dx.doi.org/10.1007/978-3-319-70335-0
    URL: Volltext  (URL des Erstveröffentlichers)
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    Online Access
    Open Access Request
    HU Berlin
    BTU Cottbus
  • 4
    Online Resource
    Online Resource
    Bioinformatics and Machine Learning for Cancer Biology (2022)
    Basel : MDPI Books
    Details
    UID:
    edoccha_9960854424502883
    Format: 1 electronic resource (196 p.)
    Content: Cancer is a leading cause of death worldwide, claiming millions of lives each year. Cancer biology is an essential research field to understand how cancer develops, evolves, and responds to therapy. By taking advantage of a series of “omics” technologies (e.g., genomics, transcriptomics, and epigenomics), computational methods in bioinformatics and machine learning can help scientists and researchers to decipher the complexity of cancer heterogeneity, tumorigenesis, and anticancer drug discovery. Particularly, bioinformatics enables the systematic interrogation and analysis of cancer from various perspectives, including genetics, epigenetics, signaling networks, cellular behavior, clinical manifestation, and epidemiology. Moreover, thanks to the influx of next-generation sequencing (NGS) data in the postgenomic era and multiple landmark cancer-focused projects, such as The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), machine learning has a uniquely advantageous role in boosting data-driven cancer research and unraveling novel methods for the prognosis, prediction, and treatment of cancer.
    Note: English
    Additional Edition: ISBN 3-0365-4814-9
    Additional Edition: ISBN 3-0365-4813-0
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Charité
  • 5
    Online Resource
    Online Resource
    Bioinformatics and Machine Learning for Cancer Biology (2022)
    Basel : MDPI Books
    Details
    UID:
    edocfu_9960854424502883
    Format: 1 electronic resource (196 p.)
    Content: Cancer is a leading cause of death worldwide, claiming millions of lives each year. Cancer biology is an essential research field to understand how cancer develops, evolves, and responds to therapy. By taking advantage of a series of “omics” technologies (e.g., genomics, transcriptomics, and epigenomics), computational methods in bioinformatics and machine learning can help scientists and researchers to decipher the complexity of cancer heterogeneity, tumorigenesis, and anticancer drug discovery. Particularly, bioinformatics enables the systematic interrogation and analysis of cancer from various perspectives, including genetics, epigenetics, signaling networks, cellular behavior, clinical manifestation, and epidemiology. Moreover, thanks to the influx of next-generation sequencing (NGS) data in the postgenomic era and multiple landmark cancer-focused projects, such as The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), machine learning has a uniquely advantageous role in boosting data-driven cancer research and unraveling novel methods for the prognosis, prediction, and treatment of cancer.
    Note: English
    Additional Edition: ISBN 3-0365-4814-9
    Additional Edition: ISBN 3-0365-4813-0
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    FU Berlin
  • 6
    Online Resource
    Online Resource
    Bioinformatics and Machine Learning for Cancer Biology (2022)
    Basel : MDPI Books
    Details
    UID:
    almahu_9949371452002882
    Format: 1 electronic resource (196 p.)
    Content: Cancer is a leading cause of death worldwide, claiming millions of lives each year. Cancer biology is an essential research field to understand how cancer develops, evolves, and responds to therapy. By taking advantage of a series of “omics” technologies (e.g., genomics, transcriptomics, and epigenomics), computational methods in bioinformatics and machine learning can help scientists and researchers to decipher the complexity of cancer heterogeneity, tumorigenesis, and anticancer drug discovery. Particularly, bioinformatics enables the systematic interrogation and analysis of cancer from various perspectives, including genetics, epigenetics, signaling networks, cellular behavior, clinical manifestation, and epidemiology. Moreover, thanks to the influx of next-generation sequencing (NGS) data in the postgenomic era and multiple landmark cancer-focused projects, such as The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), machine learning has a uniquely advantageous role in boosting data-driven cancer research and unraveling novel methods for the prognosis, prediction, and treatment of cancer.
    Note: English
    Additional Edition: ISBN 3-0365-4814-9
    Additional Edition: ISBN 3-0365-4813-0
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    HU Berlin
  • 7
    Book
    Book
    Clastic hydrocarbon reservoir sedimentology (2018)
    Cham : Springer
    Details
    UID:
    b3kat_BV044957544
    Format: xvi, 712 Seiten , Illustrationen, Diagramme
    ISBN: 9783319703343
    Series Statement: Advances in oil and gas exploration & production
    Note: Übersetzung von: Yu, Xinghe: Clastic hydrocarbon reservoir sedimentology. - Second edition. - Beijing: Petroleum Industry Press, 2006. - ISBN 978-7-5021-6399-0
    Additional Edition: Erscheint auch als Online-Ausgabe ISBN 978-3-319-70335-0
    Language: English
    RVK:
    TZ 7500
    Keywords: Klastische Sedimentation ; Sediment ; Fazies ; Stratigraphie ; Erdgasgeologie ; Erdölgeologie ; Speichergestein ; Klastisches Gestein ; Sedimentologie ; Sandstein ; Siliziklastisches Gestein ; Sequenzstratigraphie
    Library Location Call Number Volume/Issue/Year Availability
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    Book
    TU Berlin get availability status
  • 8
    Online Resource
    Online Resource
    Bioinformatics and machine learning for cancer biology (2022)
    Basel : MDPI
    Details
    UID:
    b3kat_BV048611261
    Format: 1 Online-Ressource
    ISBN: 9783036548135
    Additional Edition: Erscheint auch als Druck-Ausgabe, Hardcover ISBN 978-3-0365-4814-2
    Language: English
    DOI: 10.3390/books978-3-0365-4813-5
    URL: Volltext  (kostenfrei)
    URL: Volltext  (kostenfrei)
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    Fulltext
    Fulltext
    EUV Frankfurt
  • 9
    Online Resource
    Online Resource
    Clastic Hydrocarbon Reservoir Sedimentology / (2018)
    Cham :Springer International Publishing :
    Details
    UID:
    almafu_9958898110802883
    Format: 1 online resource ( pages)
    Edition: 1st ed. 2018.
    ISBN: 3-319-70335-8
    Series Statement: Advances in Oil and Gas Exploration & Production,
    Content: This book presents a comprehensive assessment of clastic sedimentology and its application to reservoir geology. It covers the theoretical foundations of the topic and its use for scientists as well as professionals in the field. Further, it addresses all aspects of reservoir sedimentology, clastic sequence stratigraphy, sedimentation, reservoir diagenesis and heterogeneity, as well as depositional systems (alluvial, fluvial, lacustrine, delta, sandy coast, neritic, deep-water) in detail. The research team responsible for this book has been investigating clastic sedimentology for more than three decades and consists of highly published and cited authors. The Chinese edition of this book has been a great success, and is popular among sedimentologists and petroleum geologists alike.
    Note: Preface -- 1. Formation, Development, and Trends in Reservoir Sedimentology -- Features of Clastic Reservoirs -- Theory and Methods for Studying Clastic Sequence Stratigraphy -- Research Methods of Sedimentary Facies and Sedimentation -- Reservoir Diagenesis -- Reservoir Heterogeneity -- Alluvial Fan Depositional System -- Fluvial Depositional System -- Lacustrine Depositional System -- Deltaic Depositional System -- Sandy Coast (Shore) and Neritic Depositional System -- Deep-water Depositional System -- References -- Index -- Glossary. .
    Additional Edition: ISBN 3-319-70334-X
    Language: English
    DOI: 10.1007/978-3-319-70335-0
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    Charité
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