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| 2. |
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| 3. |
- Bravo, L, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 4. |
- Tabiri, S, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 5. |
- Glasbey, JC, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 6. |
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| 7. |
- Orthofer, M, et al.
(författare)
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Identification of ALK in Thinness
- 2020
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Ingår i: Cell. - : Elsevier BV. - 1097-4172 .- 0092-8674. ; 181:6, s. 1246-
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Tidskriftsartikel (refereegranskat)
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| 8. |
- Martson, A-G, et al.
(författare)
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How to design a study to evaluate therapeutic drug monitoring in infectious diseases?
- 2020
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Ingår i: Clinical Microbiology and Infection. - : Elsevier BV. - 1198-743X .- 1469-0691. ; 26:8, s. 1008-1016
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Forskningsöversikt (refereegranskat)abstract
- Background: Therapeutic drug monitoring (TDM) is a tool to personalize and optimize dosing by measuring the drug concentration and subsequently adjusting the dose to reach a target concentration or exposure. The evidence to support TDM is however often ranked as expert opinion. Limitations in study design and sample size have hampered definitive conclusions of the potential added value of TDM.Objectives: We aim to give expert opinion and discuss the main points and limitations of available data from antibiotic TDM trials and emphasize key elements for consideration in design of future clinical studies to quantify the benefits of TDM.Sources: The sources were peer-reviewed publications, guidelines and expert opinions from the field of TDM.Content: This review focuses on key aspects of antimicrobial TDM study design: describing the rationale for a TDM study, assessing the exposure of a drug, assessing susceptibility of pathogens and selecting appropriate clinical endpoints. Moreover we provide guidance on appropriate study design.
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| 9. |
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| 10. |
- Neely, Benjamin A., et al.
(författare)
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Toward an Integrated Machine Learning Model of a Proteomics Experiment
- 2023
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 22:3, s. 681-696
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Forskningsöversikt (refereegranskat)abstract
- In recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and machine learning experts in a workshop with the goals to evaluate and explore machine learning applications for realistic modeling of data from multidimensional mass spectrometry-based proteomics analysis of any sample or organism. Following this sample-to-data roadmap helped identify knowledge gaps and define needs. Being able to generate bespoke and realistic synthetic data has legitimate and important uses in system suitability, method development, and algorithm benchmarking, while also posing critical ethical questions. The interdisciplinary nature of the workshop informed discussions of what is currently possible and future opportunities and challenges. In the following perspective we summarize these discussions in the hope of conveying our excitement about the potential of machine learning in proteomics and to inspire future research.
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