eBook
Data Science, AI, and Machine Learning in Drug Development
| Τίτλος: | Data Science, AI, and Machine Learning in Drug Development |
|---|---|
| Περιγραφή: | The confluence of big data, artificial intelligence (AI), and machine learning (ML) has led to a paradigm shift in how innovative medicines are developed and healthcare delivered. To fully capitalize on these technological advances, it is essential to systematically harness data from diverse sources and leverage digital technologies and advanced analytics to enable data-driven decisions. Data science stands at a unique moment of opportunity to lead such a transformative change.Intended to be a single source of information, Data Science, AI, and Machine Learning in Drug Research and Development covers a wide range of topics on the changing landscape of drug R & D, emerging applications of big data, AI and ML in drug development, and the build of robust data science organizations to drive biopharmaceutical digital transformations.Features Provides a comprehensive review of challenges and opportunities as related to the applications of big data, AI, and ML in the entire spectrum of drug R & D Discusses regulatory developments in leveraging big data and advanced analytics in drug review and approval Offers a balanced approach to data science organization build Presents real-world examples of AI-powered solutions to a host of issues in the lifecycle of drug development Affords sufficient context for each problem and provides a detailed description of solutions suitable for practitioners with limited data science expertise |
| Συγγραφείς: | Harry Yang |
| Resource Type: | eBook. |
| Θέματα: | Artificial intelligence, Drug development--Data processing, Machine learning |
| Categories: | MEDICAL / Research, COMPUTERS / Data Science / Machine Learning, TECHNOLOGY & ENGINEERING / Pharmaceutical |
| Βάση Δεδομένων: | eBook Index |
| FullText | Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Data Science, AI, and Machine Learning in Drug Development – Name: Abstract Label: Description Group: Ab Data: The confluence of big data, artificial intelligence (AI), and machine learning (ML) has led to a paradigm shift in how innovative medicines are developed and healthcare delivered. To fully capitalize on these technological advances, it is essential to systematically harness data from diverse sources and leverage digital technologies and advanced analytics to enable data-driven decisions. Data science stands at a unique moment of opportunity to lead such a transformative change.Intended to be a single source of information, Data Science, AI, and Machine Learning in Drug Research and Development covers a wide range of topics on the changing landscape of drug R & D, emerging applications of big data, AI and ML in drug development, and the build of robust data science organizations to drive biopharmaceutical digital transformations.Features Provides a comprehensive review of challenges and opportunities as related to the applications of big data, AI, and ML in the entire spectrum of drug R & D Discusses regulatory developments in leveraging big data and advanced analytics in drug review and approval Offers a balanced approach to data science organization build Presents real-world examples of AI-powered solutions to a host of issues in the lifecycle of drug development Affords sufficient context for each problem and provides a detailed description of solutions suitable for practitioners with limited data science expertise – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Harry+Yang%22">Harry Yang</searchLink> – Name: TypePub Label: Resource Type Group: TypPub Data: eBook. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Drug+development--Data+processing%22">Drug development--Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: SubjectBISAC Label: Categories Group: Su Data: <searchLink fieldCode="ZK" term="%22MEDICAL+%2F+Research%22">MEDICAL / Research</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Data+Science+%2F+Machine+Learning%22">COMPUTERS / Data Science / Machine Learning</searchLink><br /><searchLink fieldCode="ZK" term="%22TECHNOLOGY+%26+ENGINEERING+%2F+Pharmaceutical%22">TECHNOLOGY & ENGINEERING / Pharmaceutical</searchLink> |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=3360129 |
| RecordInfo | BibRecord: BibEntity: Classifications: – Code: 615.1900285 Scheme: ddc Type: prePub Languages: – Code: eng Text: English Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Drug development--Data processing Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Data Science, AI, and Machine Learning in Drug Development Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Harry Yang – PersonEntity: Name: NameFull: Harry Yang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2022 – D: 28 M: 12 Type: profile Y: 2022 Identifiers: – Type: isbn-print Value: 9780367708078 – Type: isbn-print Value: 9780367714413 – Type: isbn-electronic Value: 9781000652673 – Type: isbn-electronic Value: 9781000652697 – Type: isbn-electronic Value: 9781003150886 Titles: – TitleFull: Data Science, AI, and Machine Learning in Drug Development Type: main |
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