Identification of high‐dimensional omics‐derived predictors for tumor growth dynamics using machine learning and pharmacometric modeling-Reference-Cited by-同舟云学术

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Identification of high‐dimensional omics‐derived predictors for tumor growth dynamics using machine learning and pharmacometric modeling

Published:2021-04 Issue:4 Volume:10 Page:350-361
ISSN:2163-8306
Container-title:CPT: Pharmacometrics & Systems Pharmacology
language:en
Short-container-title:CPT Pharmacometrics Syst. Pharmacol.

Author:

Zwep Laura B.¹²,Duisters Kevin L. W.²,Jansen Martijn¹,Guo Tingjie¹³,Meulman Jacqueline J.²,Upadhyay Parth J.¹,Hasselt J. G. Coen¹

Affiliation:

1. Leiden Academic Centre for Drug Research Leiden University Leiden The Netherlands

2. Mathematical Institute Leiden University Leiden The Netherlands

3. Department of Intensive Care Medicine Amsterdam UMC Vrije Universiteit Amsterdam Amsterdam The Netherlands

Publisher

Wiley

Subject

Pharmacology (medical),Modeling and Simulation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/psp4.12603

Reference46 articles.

1. A Review of Mixed-Effects Models of Tumor Growth and Effects of Anticancer Drug Treatment Used in Population Analysis

2. Population pharmacokinetic–pharmacodynamic modelling in oncology: a tool for predicting clinical response

3. Tumor growth dynamics: insights into evolutionary processes

4. Model-Based Prediction of Phase III Overall Survival in Colorectal Cancer on the Basis of Phase II Tumor Dynamics

5. Integrated Simulation Framework for Toxicity, Dose Intensity, Disease Progression, and Cost Effectiveness for Castration‐Resistant Prostate Cancer Treatment With Eribulin

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1. Analyzing the Distribution of Progression-Free Survival for Combination Therapies: A Study of Model-Based Translational Predictive Methods in Oncology;European Journal of Pharmaceutical Sciences;2024-09

2. Adoption of Machine Learning in Pharmacometrics: An Overview of Recent Implementations and Their Considerations;Pharmaceutics;2022-08-29

3. Modeling the efficacy of different anti-angiogenic drugs on treatment of solid tumors using 3D computational modeling and machine learning;Computers in Biology and Medicine;2022-07

4. Contribution of machine learning to tumor growth inhibition modeling for hepatocellular carcinoma patients under Roblitinib ( FGF401 ) drug treatment;CPT: Pharmacometrics & Systems Pharmacology;2022-06-21

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