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AstraZeneca ltd machine learning models
Machine Learning Models, supplied by AstraZeneca ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/machine+learning+model/machine+learning+models/pmc11008607-102-8-20
Average 90 stars, based on 1 article reviews
machine learning models - by Bioz Stars, 2026-10
90/100 stars

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other:

Article Title: A network-based trans-omics approach for predicting synergistic drug combinations.
Article Snippet: A machine learningmodel using an ensemble ofweakpredictivemodelswas applied for the dataset in the AstraZeneca-Sanger Drug Combination Prediction DREAMChallenge.

Article Title: A network-based trans-omics approach for predicting synergistic drug combinations.
Article Snippet: A machine learningmodel using an ensemble ofweakpredictivemodelswas applied for the dataset in the AstraZeneca-Sanger Drug Combination Prediction DREAMChallenge9.

Article Title: GCLmf: A Novel Molecular Graph Contrastive Learning Framework Based on Hard Negatives and Application in Toxicity Prediction.
Article Snippet: In the pharmaceutical company AstraZeneca, Williams et al. [8] predicted drug-induced liver injury (DILI) by using machine learning (ML) methods, quantifying the probability that a compound falls into either low, medium, or high-severity categories.

Article Title: An artificial intelligence-assisted physiologically-based pharmacokinetic model to predict nanoparticle delivery to tumors in mice
Article Snippet: Recently, using chemical structure and dose for small molecule organic drugs, machine learning models have been trained based on the AstraZeneca clinical data to predict several rat and human pharmacokinetic parameters (e.g., Cmax, AUC and volume of distribution) as well as time-concentration pharmacokinetic profiles [ 20 , 21 ].

Article Title: “Evaluating the Impact of Artificial Intelligence on Vaccine Development: Lessons Learned from the COVID-19 Pandemic”
Article Snippet: By leveraging machine learning algorithms, AstraZeneca could simulate the outcomes of different dosing regimens, allowing for the creation of adaptive trial designs.

Article Title: The digital transformation in pharmacy: embracing online platforms and the cosmeceutical paradigm shift
Article Snippet: A substantial advancement in the realm of machine learning has emerged from major pharmaceutical entities, notably AstraZeneca, in conjunction with research institutions.

Clinical Proteomics:

Article Title: The Role of Data Science in Enhancing Project Management Practices: A Case Study in the Pharmaceutical Industry
Article Snippet: Received: 27 August 2023 Reviewed: 11 October 2023 Revised: 25 December 2023 Accepted: 18 January 2024 Data science is altering the way in which we manage projects, starting from the planning stage through both execution and tracking phases.. In this paper, an examination is made into the productivity and efficiency gains in project management due to the use of data science.. It uses previous studies, practical samples and latest advancements to show how decisions based on data can influence the results of a project.

Drug discovery:

Article Title: A machine learning approach to population pharmacokinetic modelling automation
Article Snippet: .. A machine learning approach to population pharmacokinetic modelling automation Samuel Richardson AstraZeneca https://orcid.org/0000-0001-6781-3768 Itziar Irurzun Arana AstraZeneca https://orcid.org/0000-0001-6084-4481 Andrzej Nowojewski AstraZeneca https://orcid.org/0009-0005-7729-8452 Diansong Zhou AstraZeneca Jacob Leander AstraZeneca https://orcid.org/0000-0002-7411-2967 Weifeng Tang AstraZeneca Richard Dearden AstraZeneca Megan Gibbs AstraZeneca Article Keywords: Machine learning, population pharmacokinetics, automation, optimization, non-linear mixed effect model, model space search, NLME, popPK Posted Date: June 20th, 2024 DOI: https://doi.org/10.21203/rs.3.rs-4534358/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. ..



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