Genomics at the nexus of AI, computer vision, and machine learning:
The book provides a comprehensive understanding of cutting-edge research and applications at the intersection of genomics and advanced AI techniques and serves as an essential resource for researchers, bioinformaticians, and practitioners looking to leverage genomics data for AI-driven insights and...
Gespeichert in:
| Weitere beteiligte Personen: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Hoboken, NJ Beverly, MA
John Wiley & Sons, Inc.
2024
Hoboken, NJ Beverly, MA Scrivener Publishing LLC 2024 |
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781394268801/?ar |
| Zusammenfassung: | The book provides a comprehensive understanding of cutting-edge research and applications at the intersection of genomics and advanced AI techniques and serves as an essential resource for researchers, bioinformaticians, and practitioners looking to leverage genomics data for AI-driven insights and innovations. The book encompasses a wide range of topics, starting with an introduction to genomics data and its unique characteristics. Each chapter unfolds a unique facet, delving into the collaborative potential and challenges that arise from advanced technologies. It explores image analysis techniques specifically tailored for genomic data. It also delves into deep learning showcasing the power of convolutional neural networks (CNN) and recurrent neural networks (RNN) in genomic image analysis and sequence analysis. Readers will gain practical knowledge on how to apply deep learning techniques to unlock patterns and relationships in genomics data. Transfer learning, a popular technique in AI, is explored in the context of genomics, demonstrating how knowledge from pre-trained models can be effectively transferred to genomic datasets, leading to improved performance and efficiency. Also covered is the domain adaptation techniques specifically tailored for genomics data. The book explores how genomics principles can inspire the design of AI algorithms, including genetic algorithms, evolutionary computing, and genetic programming. Additional chapters delve into the interpretation of genomic data using AI and ML models, including techniques for feature importance and visualization, as well as explainable AI methods that aid in understanding the inner workings of the models. The applications of genomics in AI span various domains, and the book explores AI-driven drug discovery and personalized medicine, genomic data analysis for disease diagnosis and prognosis, and the advancement of AI-enabled genomic research. Lastly, the book addresses the ethical considerations in integrating genomics with AI, computer vision, and machine learning. Audience The book will appeal to biomedical and computer/data scientists and researchers working in genomics and bioinformatics seeking to leverage AI, computer vision, and machine learning for enhanced analysis and discovery; healthcare professionals advancing personalized medicine and patient care; industry leaders and decision-makers in biotechnology, pharmaceuticals, and healthcare industries seeking strategic insights into the integration of genomics and advanced technologies. |
| Beschreibung: | Description based on online resource; title from digital title page (viewed on October 15, 2024) |
| Umfang: | 1 Online-Ressource |
| ISBN: | 9781394268832 1394268831 9781394268825 1394268823 9781394268801 |
Internformat
MARC
| LEADER | 00000nam a22000002 4500 | ||
|---|---|---|---|
| 001 | ZDB-30-ORH-109653785 | ||
| 003 | DE-627-1 | ||
| 005 | 20241107103331.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 241107s2024 xx |||||o 00| ||eng c | ||
| 020 | |a 9781394268832 |c electronic book |9 978-1-394-26883-2 | ||
| 020 | |a 1394268831 |c electronic book |9 1-394-26883-1 | ||
| 020 | |a 9781394268825 |c electronic book |9 978-1-394-26882-5 | ||
| 020 | |a 1394268823 |c electronic book |9 1-394-26882-3 | ||
| 020 | |a 9781394268801 |9 978-1-394-26880-1 | ||
| 035 | |a (DE-627-1)109653785 | ||
| 035 | |a (DE-599)KEP109653785 | ||
| 035 | |a (ORHE)9781394268801 | ||
| 035 | |a (DE-627-1)109653785 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 082 | 0 | |a 572.8/6028563 |2 23/eng/20241015 | |
| 245 | 1 | 0 | |a Genomics at the nexus of AI, computer vision, and machine learning |c edited by Shilpa Choudhary... [and 4 others] |
| 264 | 1 | |a Hoboken, NJ |a Beverly, MA |b John Wiley & Sons, Inc. |c 2024 | |
| 264 | 1 | |a Hoboken, NJ |a Beverly, MA |b Scrivener Publishing LLC |c 2024 | |
| 264 | 4 | |c ©2024 | |
| 300 | |a 1 Online-Ressource | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Description based on online resource; title from digital title page (viewed on October 15, 2024) | ||
| 520 | |a The book provides a comprehensive understanding of cutting-edge research and applications at the intersection of genomics and advanced AI techniques and serves as an essential resource for researchers, bioinformaticians, and practitioners looking to leverage genomics data for AI-driven insights and innovations. The book encompasses a wide range of topics, starting with an introduction to genomics data and its unique characteristics. Each chapter unfolds a unique facet, delving into the collaborative potential and challenges that arise from advanced technologies. It explores image analysis techniques specifically tailored for genomic data. It also delves into deep learning showcasing the power of convolutional neural networks (CNN) and recurrent neural networks (RNN) in genomic image analysis and sequence analysis. Readers will gain practical knowledge on how to apply deep learning techniques to unlock patterns and relationships in genomics data. Transfer learning, a popular technique in AI, is explored in the context of genomics, demonstrating how knowledge from pre-trained models can be effectively transferred to genomic datasets, leading to improved performance and efficiency. Also covered is the domain adaptation techniques specifically tailored for genomics data. The book explores how genomics principles can inspire the design of AI algorithms, including genetic algorithms, evolutionary computing, and genetic programming. Additional chapters delve into the interpretation of genomic data using AI and ML models, including techniques for feature importance and visualization, as well as explainable AI methods that aid in understanding the inner workings of the models. The applications of genomics in AI span various domains, and the book explores AI-driven drug discovery and personalized medicine, genomic data analysis for disease diagnosis and prognosis, and the advancement of AI-enabled genomic research. Lastly, the book addresses the ethical considerations in integrating genomics with AI, computer vision, and machine learning. Audience The book will appeal to biomedical and computer/data scientists and researchers working in genomics and bioinformatics seeking to leverage AI, computer vision, and machine learning for enhanced analysis and discovery; healthcare professionals advancing personalized medicine and patient care; industry leaders and decision-makers in biotechnology, pharmaceuticals, and healthcare industries seeking strategic insights into the integration of genomics and advanced technologies. | ||
| 650 | 0 | |a Genomics | |
| 650 | 0 | |a Artificial intelligence |x Medical applications | |
| 650 | 4 | |a Génomique | |
| 700 | 1 | |a Choudhary, Shilpa |e HerausgeberIn |4 edt | |
| 776 | 1 | |z 1394268807 | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 1394268807 |
| 966 | 4 | 0 | |l DE-91 |p ZDB-30-ORH |q TUM_PDA_ORH |u https://learning.oreilly.com/library/view/-/9781394268801/?ar |m X:ORHE |x Aggregator |z lizenzpflichtig |3 Volltext |
| 912 | |a ZDB-30-ORH | ||
| 951 | |a BO | ||
| 912 | |a ZDB-30-ORH | ||
| 049 | |a DE-91 | ||
Datensatz im Suchindex
| DE-BY-TUM_katkey | ZDB-30-ORH-109653785 |
|---|---|
| _version_ | 1821494925763543040 |
| adam_text | |
| any_adam_object | |
| author2 | Choudhary, Shilpa |
| author2_role | edt |
| author2_variant | s c sc |
| author_facet | Choudhary, Shilpa |
| building | Verbundindex |
| bvnumber | localTUM |
| collection | ZDB-30-ORH |
| ctrlnum | (DE-627-1)109653785 (DE-599)KEP109653785 (ORHE)9781394268801 |
| dewey-full | 572.8/6028563 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 572 - Biochemistry |
| dewey-raw | 572.8/6028563 |
| dewey-search | 572.8/6028563 |
| dewey-sort | 3572.8 76028563 |
| dewey-tens | 570 - Biology |
| discipline | Biologie |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>04293nam a22004572 4500</leader><controlfield tag="001">ZDB-30-ORH-109653785</controlfield><controlfield tag="003">DE-627-1</controlfield><controlfield tag="005">20241107103331.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">241107s2024 xx |||||o 00| ||eng c</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781394268832</subfield><subfield code="c">electronic book</subfield><subfield code="9">978-1-394-26883-2</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1394268831</subfield><subfield code="c">electronic book</subfield><subfield code="9">1-394-26883-1</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781394268825</subfield><subfield code="c">electronic book</subfield><subfield code="9">978-1-394-26882-5</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1394268823</subfield><subfield code="c">electronic book</subfield><subfield code="9">1-394-26882-3</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781394268801</subfield><subfield code="9">978-1-394-26880-1</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)109653785</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP109653785</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ORHE)9781394268801</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)109653785</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">572.8/6028563</subfield><subfield code="2">23/eng/20241015</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Genomics at the nexus of AI, computer vision, and machine learning</subfield><subfield code="c">edited by Shilpa Choudhary... [and 4 others]</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Hoboken, NJ</subfield><subfield code="a">Beverly, MA</subfield><subfield code="b">John Wiley & Sons, Inc.</subfield><subfield code="c">2024</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Hoboken, NJ</subfield><subfield code="a">Beverly, MA</subfield><subfield code="b">Scrivener Publishing LLC</subfield><subfield code="c">2024</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">©2024</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Description based on online resource; title from digital title page (viewed on October 15, 2024)</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The book provides a comprehensive understanding of cutting-edge research and applications at the intersection of genomics and advanced AI techniques and serves as an essential resource for researchers, bioinformaticians, and practitioners looking to leverage genomics data for AI-driven insights and innovations. The book encompasses a wide range of topics, starting with an introduction to genomics data and its unique characteristics. Each chapter unfolds a unique facet, delving into the collaborative potential and challenges that arise from advanced technologies. It explores image analysis techniques specifically tailored for genomic data. It also delves into deep learning showcasing the power of convolutional neural networks (CNN) and recurrent neural networks (RNN) in genomic image analysis and sequence analysis. Readers will gain practical knowledge on how to apply deep learning techniques to unlock patterns and relationships in genomics data. Transfer learning, a popular technique in AI, is explored in the context of genomics, demonstrating how knowledge from pre-trained models can be effectively transferred to genomic datasets, leading to improved performance and efficiency. Also covered is the domain adaptation techniques specifically tailored for genomics data. The book explores how genomics principles can inspire the design of AI algorithms, including genetic algorithms, evolutionary computing, and genetic programming. Additional chapters delve into the interpretation of genomic data using AI and ML models, including techniques for feature importance and visualization, as well as explainable AI methods that aid in understanding the inner workings of the models. The applications of genomics in AI span various domains, and the book explores AI-driven drug discovery and personalized medicine, genomic data analysis for disease diagnosis and prognosis, and the advancement of AI-enabled genomic research. Lastly, the book addresses the ethical considerations in integrating genomics with AI, computer vision, and machine learning. Audience The book will appeal to biomedical and computer/data scientists and researchers working in genomics and bioinformatics seeking to leverage AI, computer vision, and machine learning for enhanced analysis and discovery; healthcare professionals advancing personalized medicine and patient care; industry leaders and decision-makers in biotechnology, pharmaceuticals, and healthcare industries seeking strategic insights into the integration of genomics and advanced technologies.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Genomics</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Artificial intelligence</subfield><subfield code="x">Medical applications</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Génomique</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Choudhary, Shilpa</subfield><subfield code="e">HerausgeberIn</subfield><subfield code="4">edt</subfield></datafield><datafield tag="776" ind1="1" ind2=" "><subfield code="z">1394268807</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">1394268807</subfield></datafield><datafield tag="966" ind1="4" ind2="0"><subfield code="l">DE-91</subfield><subfield code="p">ZDB-30-ORH</subfield><subfield code="q">TUM_PDA_ORH</subfield><subfield code="u">https://learning.oreilly.com/library/view/-/9781394268801/?ar</subfield><subfield code="m">X:ORHE</subfield><subfield code="x">Aggregator</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">BO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91</subfield></datafield></record></collection> |
| id | ZDB-30-ORH-109653785 |
| illustrated | Not Illustrated |
| indexdate | 2025-01-17T11:22:08Z |
| institution | BVB |
| isbn | 9781394268832 1394268831 9781394268825 1394268823 9781394268801 |
| language | English |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource |
| psigel | ZDB-30-ORH TUM_PDA_ORH ZDB-30-ORH |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | John Wiley & Sons, Inc. Scrivener Publishing LLC |
| record_format | marc |
| spelling | Genomics at the nexus of AI, computer vision, and machine learning edited by Shilpa Choudhary... [and 4 others] Hoboken, NJ Beverly, MA John Wiley & Sons, Inc. 2024 Hoboken, NJ Beverly, MA Scrivener Publishing LLC 2024 ©2024 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Description based on online resource; title from digital title page (viewed on October 15, 2024) The book provides a comprehensive understanding of cutting-edge research and applications at the intersection of genomics and advanced AI techniques and serves as an essential resource for researchers, bioinformaticians, and practitioners looking to leverage genomics data for AI-driven insights and innovations. The book encompasses a wide range of topics, starting with an introduction to genomics data and its unique characteristics. Each chapter unfolds a unique facet, delving into the collaborative potential and challenges that arise from advanced technologies. It explores image analysis techniques specifically tailored for genomic data. It also delves into deep learning showcasing the power of convolutional neural networks (CNN) and recurrent neural networks (RNN) in genomic image analysis and sequence analysis. Readers will gain practical knowledge on how to apply deep learning techniques to unlock patterns and relationships in genomics data. Transfer learning, a popular technique in AI, is explored in the context of genomics, demonstrating how knowledge from pre-trained models can be effectively transferred to genomic datasets, leading to improved performance and efficiency. Also covered is the domain adaptation techniques specifically tailored for genomics data. The book explores how genomics principles can inspire the design of AI algorithms, including genetic algorithms, evolutionary computing, and genetic programming. Additional chapters delve into the interpretation of genomic data using AI and ML models, including techniques for feature importance and visualization, as well as explainable AI methods that aid in understanding the inner workings of the models. The applications of genomics in AI span various domains, and the book explores AI-driven drug discovery and personalized medicine, genomic data analysis for disease diagnosis and prognosis, and the advancement of AI-enabled genomic research. Lastly, the book addresses the ethical considerations in integrating genomics with AI, computer vision, and machine learning. Audience The book will appeal to biomedical and computer/data scientists and researchers working in genomics and bioinformatics seeking to leverage AI, computer vision, and machine learning for enhanced analysis and discovery; healthcare professionals advancing personalized medicine and patient care; industry leaders and decision-makers in biotechnology, pharmaceuticals, and healthcare industries seeking strategic insights into the integration of genomics and advanced technologies. Genomics Artificial intelligence Medical applications Génomique Choudhary, Shilpa HerausgeberIn edt 1394268807 Erscheint auch als Druck-Ausgabe 1394268807 |
| spellingShingle | Genomics at the nexus of AI, computer vision, and machine learning Genomics Artificial intelligence Medical applications Génomique |
| title | Genomics at the nexus of AI, computer vision, and machine learning |
| title_auth | Genomics at the nexus of AI, computer vision, and machine learning |
| title_exact_search | Genomics at the nexus of AI, computer vision, and machine learning |
| title_full | Genomics at the nexus of AI, computer vision, and machine learning edited by Shilpa Choudhary... [and 4 others] |
| title_fullStr | Genomics at the nexus of AI, computer vision, and machine learning edited by Shilpa Choudhary... [and 4 others] |
| title_full_unstemmed | Genomics at the nexus of AI, computer vision, and machine learning edited by Shilpa Choudhary... [and 4 others] |
| title_short | Genomics at the nexus of AI, computer vision, and machine learning |
| title_sort | genomics at the nexus of ai computer vision and machine learning |
| topic | Genomics Artificial intelligence Medical applications Génomique |
| topic_facet | Genomics Artificial intelligence Medical applications Génomique |
| work_keys_str_mv | AT choudharyshilpa genomicsatthenexusofaicomputervisionandmachinelearning |