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• W4386403485 abstract "Immunotherapy, including immune cell therapy and targeted therapy, is gradually developed through the ongoing discovery of molecular compounds or immune cells. Choosing the best one or the best combination of target compounds and immune-cell therapy is a challenge for clinical scientists and clinicians. We have found variable efficacy individually after tumor-infiltrating lymphocyte (TIL) therapy, and now TILs have been discovered in a group of heterogeneous immune cells. To select the best immunotherapy for each patient, we started to study TIL genomics, including single-cell mRNA differential display from TIL published in 2007 and single-cell RNA-seq from TIL published in 2013, set up TIL quantitative network in 2015, researched machine-learning model for immune therapy in 2022. These manual reports single-cell RNA-seq data combined with machine learning to evaluate the optimal compounds and immune cells for individual patients. The machine-learning model, one of artificial intelligence, can estimate targeting genomic variance from single-cell RNA-seq so that they can cover thirteen kinds of immune cell therapies and ongoing FDA-approved targeted therapies such as PD1 inhibitors, PDL1 inhibitors, and CTLA4 inhibitors, as well as other different treatments such as HDACI or DNMT1 inhibitors, FDA-approved drugs. Moreover, also cover Phase-1, Phase-2, Phase-3, and Phase-4 of clinical trials, such as TIL, CAR T-cells, TCR T-cells. Single-cell RNA-seq with an Artificial intelligence estimation system is much better than our published models from microarrays or just cell therapy. The medical goal is to address three issues in clinical immunotherapy: the increase of efficacy; the decrease of adverse effects and the decrease of the cost in clinical applications." @default.
• W4386403485 created "2023-09-04" @default.
• W4386403485 creator A5088349405 @default.
• W4386403485 date "2023-01-01" @default.
• W4386403485 modified "2023-09-29" @default.
• W4386403485 title "Personalized Immunotherapy of Patients: Defining by Single-cell RNA-seq with Artificial Intelligence" @default.
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• W4386403485 doi "https://doi.org/10.18103/mra.v11i8.4293" @default.
• W4386403485 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37736242" @default.
• W4386403485 hasPublicationYear "2023" @default.
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