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Mathematical & Computational Sciences
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Mathematical & Computational Sciences
Mathematical & Computational Sciences
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(M-026) Enhancing Clinical Trial Simulations with Advanced Computational Techniques and Scalable Cloud Solutions
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(M-027) Implementation of Distributed Delay Differential Equations in RADAU5 and RADAR5
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(M-034) Translating PROTACs: Relating in vitro Potency to Protein Degradation Kinetics
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(M-040) Machine Learning and Chemical Language Model-Based QSAR Models for Predicting Plasma and Tissue Half-Lives of Drugs in Cattle Across Various Administration Routes
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(M-041) Integrating Machine Learning and Magnetic Resonance Imaging Radiomics for Trajectory Modeling in Duchenne Muscular Dystrophy: Explainable Artificial Intelligence to Inform Clinical Trial Design and Personalized Intervention
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(M-079) Deriving allometric scaling rules and providing Insights into the dynamics of LNP based mRNA therapeutics with an exactly solvable mathematical model
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(M-080) Harnessing Machine Learning and Real-World Data: Comparative Analysis of Cross-Sectional and Longitudinal Clustering in Patient Trajectories from Electronic Health Records
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(M-081) Modeling Interactions Between Respiratory Pathogens
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(M-082) A Novel Estimation Method for a Globally Unidentifiable Linear Compartment Models Using Bayesian Constraint Relaxation
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(M-085) An innovative framework to find potential immunotherapy targets using QSP modeling and RNA-seq data
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(S-016) Verification of an algorithm for detection of pronation-supination movements in Parkinson’s disease.
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(S-023) Deep Learning-Based Autoencoder for Outlier Detection in Dosing and Concentration-Time Data: Enabling Reliable Population Pharmacokinetic Modeling
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(S-039) Machine Learning and Mechanistic Modeling to Predict Intravenous Pharmacokinetic Profiles in Humans
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(S-053) Leveraging historical clinical trial data using Model-Based Meta-Analysis (MBMA) to investigate the impact of prior biologic therapy on the efficacy of treatments in Psoriatic Arthritis
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(S-054) Computer Modeling of Bevacizumab Drug Distribution After Intravitreal Injection in Rabbit and Human Eyes
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(S-068) Machine learning for exploratory data analysis and model diagnosis in oncology
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(S-080) Fluorescence timer-based analysis of latency-reversing agent effectiveness in HIV
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(S-081) Leveraging Kolmogorov–Arnold Networks to Discover Time-Dependent Drug Efficacy Function
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(S-093) Machine Learning-Integrated Physiology-Based Pharmacokinetic Modeling for Optimizing Nanoparticle Design
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(T-011) Accurate modeling of ACTH and cortisol dynamics for Cushing’s disease treatment
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(T-015) A pipeline for generating large virtual dog populations to predict preclinical drug-induced liver injury
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(T-016) Reducing Animal Use in Toxicological Research through Open-Source Individualized (PB)PK/QST Modeling: A Case Study with Phenobarbital in Dogs
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(T-023) Optimizing Clinical Trial Design through In Silico Modeling, Virtual Populations, and Mathematical Optimization
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(T-038) From in vitro to in vivo via Machine Learning: Predicting preclinical pharmacokinetics for small molecules in the data-scarce regime
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(T-039) Predicting Tumor Growth Dynamics and Overall Survival with Deep Learning Model
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(T-048) Modeling clinical trial design in infectious disease outbreaks considering differences in IC50 between in vitro and in vivo
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(T-056) A novel mechanistic mathematical model of liver transplant immune dynamics predicts important drivers of patient outcome
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(T-057) A Model Based Meta Analysis of Ulcerative Colitis for Clinical Remission
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(T-116) Inference of Pathological States from Skin Eruption Morphology for Drug Efficacy Evaluation and Personalized Treatment in Skin Disease
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