Multivariate biomarker discovery: data science methods for efficient analysis of high-dimensional biomedical data
Multivariate biomarker discovery is increasingly important in the realm of biomedical research, and is poised to become a crucial facet of personalized medicine. This will prompt the demand for a myriad of novel biomarkers representing distinct 'omic' biosignatures, allowing selection and...
Gespeichert in:
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, United Kingdom ; New York, NY
Cambridge University Press
2024
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| Schlagworte: | |
| Online-Zugang: | DE-12 DE-634 DE-92 Volltext |
| Zusammenfassung: | Multivariate biomarker discovery is increasingly important in the realm of biomedical research, and is poised to become a crucial facet of personalized medicine. This will prompt the demand for a myriad of novel biomarkers representing distinct 'omic' biosignatures, allowing selection and tailoring treatments to the various individual characteristics of a particular patient. This concise and self-contained book covers all aspects of predictive modeling for biomarker discovery based on high-dimensional data, as well as modern data science methods for identification of parsimonious and robust multivariate biomarkers for medical diagnosis, prognosis, and personalized medicine. It provides a detailed description of state-of-the-art methods for parallel multivariate feature selection and supervised learning algorithms for regression and classification, as well as methods for proper validation of multivariate biomarkers and predictive models implementing them. This is an invaluable resource for scientists and students interested in bioinformatics, data science, and related areas |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 30 May 2024) Multivariate analytics based on high-dimensional data : concepts and misconceptions -- Predictive modeling for biomarker discovery -- Evaluation of predictive models -- Multivariate feature selection -- Basic regression methods -- Regularized regression methods -- Regression with random forests -- Support vector regression -- Classification with random forests -- Classification with support vector machines -- Discriminant analysis -- Neural networks and deep learning -- Multistage signal enhancement -- Essential patterns, essential variables, and interpretable biomarkers -- Biomarker discovery study 1 : searching for essential gene expression patterns and multivariate biomarkers that are common for multiple types of cancer -- Biomarker discovery study 2 : multivariate biomarkers for liver cancer |
| Beschreibung: | 1 Online-Ressource (xvii, 275 Seiten) |
| ISBN: | 9781009006767 |
| DOI: | 10.1017/9781009006767 |
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| 520 | |a Multivariate biomarker discovery is increasingly important in the realm of biomedical research, and is poised to become a crucial facet of personalized medicine. This will prompt the demand for a myriad of novel biomarkers representing distinct 'omic' biosignatures, allowing selection and tailoring treatments to the various individual characteristics of a particular patient. This concise and self-contained book covers all aspects of predictive modeling for biomarker discovery based on high-dimensional data, as well as modern data science methods for identification of parsimonious and robust multivariate biomarkers for medical diagnosis, prognosis, and personalized medicine. It provides a detailed description of state-of-the-art methods for parallel multivariate feature selection and supervised learning algorithms for regression and classification, as well as methods for proper validation of multivariate biomarkers and predictive models implementing them. This is an invaluable resource for scientists and students interested in bioinformatics, data science, and related areas | ||
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Datensatz im Suchindex
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| dewey-full | 610.72/7 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 610 - Medicine and health |
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| dewey-search | 610.72/7 |
| dewey-sort | 3610.72 17 |
| dewey-tens | 610 - Medicine and health |
| discipline | Medizin |
| doi_str_mv | 10.1017/9781009006767 |
| format | Electronic eBook |
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| id | DE-604.BV049868114 |
| illustrated | Not Illustrated |
| indexdate | 2024-10-15T10:07:22Z |
| institution | BVB |
| isbn | 9781009006767 |
| language | English |
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| spelling | Dziuda, Darius M. aut Multivariate biomarker discovery data science methods for efficient analysis of high-dimensional biomedical data Darius M. Dziuda Cambridge, United Kingdom ; New York, NY Cambridge University Press 2024 1 Online-Ressource (xvii, 275 Seiten) txt rdacontent c rdamedia cr rdacarrier Title from publisher's bibliographic system (viewed on 30 May 2024) Multivariate analytics based on high-dimensional data : concepts and misconceptions -- Predictive modeling for biomarker discovery -- Evaluation of predictive models -- Multivariate feature selection -- Basic regression methods -- Regularized regression methods -- Regression with random forests -- Support vector regression -- Classification with random forests -- Classification with support vector machines -- Discriminant analysis -- Neural networks and deep learning -- Multistage signal enhancement -- Essential patterns, essential variables, and interpretable biomarkers -- Biomarker discovery study 1 : searching for essential gene expression patterns and multivariate biomarkers that are common for multiple types of cancer -- Biomarker discovery study 2 : multivariate biomarkers for liver cancer Multivariate biomarker discovery is increasingly important in the realm of biomedical research, and is poised to become a crucial facet of personalized medicine. This will prompt the demand for a myriad of novel biomarkers representing distinct 'omic' biosignatures, allowing selection and tailoring treatments to the various individual characteristics of a particular patient. This concise and self-contained book covers all aspects of predictive modeling for biomarker discovery based on high-dimensional data, as well as modern data science methods for identification of parsimonious and robust multivariate biomarkers for medical diagnosis, prognosis, and personalized medicine. It provides a detailed description of state-of-the-art methods for parallel multivariate feature selection and supervised learning algorithms for regression and classification, as well as methods for proper validation of multivariate biomarkers and predictive models implementing them. This is an invaluable resource for scientists and students interested in bioinformatics, data science, and related areas Biochemical markers / Research / Statistical methods Medicine / Research / Statistical methods Multivariate analysis Erscheint auch als Druck-Ausgabe 9781316518700 https://doi.org/10.1017/9781009006767?locatt=mode:legacy Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Dziuda, Darius M. Multivariate biomarker discovery data science methods for efficient analysis of high-dimensional biomedical data Biochemical markers / Research / Statistical methods Medicine / Research / Statistical methods Multivariate analysis |
| title | Multivariate biomarker discovery data science methods for efficient analysis of high-dimensional biomedical data |
| title_auth | Multivariate biomarker discovery data science methods for efficient analysis of high-dimensional biomedical data |
| title_exact_search | Multivariate biomarker discovery data science methods for efficient analysis of high-dimensional biomedical data |
| title_full | Multivariate biomarker discovery data science methods for efficient analysis of high-dimensional biomedical data Darius M. Dziuda |
| title_fullStr | Multivariate biomarker discovery data science methods for efficient analysis of high-dimensional biomedical data Darius M. Dziuda |
| title_full_unstemmed | Multivariate biomarker discovery data science methods for efficient analysis of high-dimensional biomedical data Darius M. Dziuda |
| title_short | Multivariate biomarker discovery |
| title_sort | multivariate biomarker discovery data science methods for efficient analysis of high dimensional biomedical data |
| title_sub | data science methods for efficient analysis of high-dimensional biomedical data |
| topic | Biochemical markers / Research / Statistical methods Medicine / Research / Statistical methods Multivariate analysis |
| topic_facet | Biochemical markers / Research / Statistical methods Medicine / Research / Statistical methods Multivariate analysis |
| url | https://doi.org/10.1017/9781009006767?locatt=mode:legacy |
| work_keys_str_mv | AT dziudadariusm multivariatebiomarkerdiscoverydatasciencemethodsforefficientanalysisofhighdimensionalbiomedicaldata |