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Leveraging high-res...
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Limeta, Angelo,1996Chalmers tekniska högskola,Chalmers University of Technology
(author)
Leveraging high-resolution omics data for predicting responses and adverse events to immune checkpoint inhibitors
- Article/chapterEnglish2023
Publisher, publication year, extent ...
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2023
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electronicrdacarrier
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LIBRIS-ID:oai:research.chalmers.se:dba13e4b-207a-46bc-b71b-ee7ee759181d
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https://doi.org/10.1016/j.csbj.2023.07.032DOI
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https://research.chalmers.se/publication/536993URI
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http://kipublications.ki.se/Default.aspx?queryparsed=id:153620002URI
Supplementary language notes
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Language:English
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Summary in:English
Part of subdatabase
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Subject category:for swepub-publicationtype
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Subject category:ref swepub-contenttype
Notes
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A long-standing goal of personalized and precision medicine is to enable accurate prediction of the outcomes of a given treatment regimen for patients harboring a disease. Currently, many clinical trials fail to meet their endpoints due to underlying factors in the patient population that contribute to either poor responses to the drug of interest or to treatment-related adverse events. Identifying these factors beforehand and correcting for them can lead to an increased success of clinical trials. Comprehensive and large-scale data gathering efforts in biomedicine by omics profiling of the healthy and diseased individuals has led to a treasure-trove of host, disease and environmental factors that contribute to the effectiveness of drugs aiming to treat disease. With increasing omics data, artificial intelligence allows an in-depth analysis of big data and offers a wide range of applications for real-world clinical use, including improved patient selection and identification of actionable targets for companion therapeutics for improved translatability across more patients. As a blueprint for complex drug-disease-host interactions, we here discuss the challenges of utilizing omics data for predicting responses and adverse events in cancer immunotherapy with immune checkpoint inhibitors (ICIs). The omics-based methodologies for improving patient outcomes as in the ICI case have also been applied across a wide-range of complex disease settings, exemplifying the use of omics for in-depth disease profiling and clinical use.
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Added entries (persons, corporate bodies, meetings, titles ...)
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Gatto, Francesco,1987Karolinska Institutet,Chalmers tekniska högskola,Chalmers University of Technology(Swepub:cth)gatto
(author)
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Herrgard, M. J.BioInnovation Institute (BII)
(author)
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Ji, Boyang,1983BioInnovation Institute (BII)(Swepub:cth)boyang
(author)
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Nielsen, Jens B,1962Chalmers tekniska högskola,Chalmers University of Technology,BioInnovation Institute (BII)(Swepub:cth)nielsenj
(author)
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Chalmers tekniska högskolaKarolinska Institutet
(creator_code:org_t)
Related titles
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In:Computational and Structural Biotechnology Journal21, s. 3912-39192001-0370
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