Article

How to predict relapse in leukemia using time series data: A comparative in silico study

2020-12-07

Abstract excerpt

Risk stratification and treatment decisions for leukemia patients are regularly based on clinical markers determined at diagnosis, while measurements on system dynamics are often neglected. However, there is increasing evidence that linking quantitative time-course information to disease outcomes can improve the predictions for patient-specific treatment responses. We designed a synthetic experiment to compare dif...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: f77ab0d3-c2e3-51f9-aeed-303831630540
DOI: 10.1101/2020.12.04.20243907

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

How to predict relapse in leukemia using time series data: A comparative in silico studyDOI 10.1101/2020.12.04.20243907Read

Early Dynamics of Chronic Myeloid Leukemia on Nilotinib Predicts Deep Molecular ResponseSemantic proximity Read

Discovery of Dynamic Models for AML Disease Progression from Longitudinal Multi-Modal Clinical Data Using Explainable Machine LearningSemantic proximity Read

Mechanistic learning to predict and understand minimal residual diseaseSemantic proximity Read

State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Leukemia DevelopmentSemantic proximity Read

Genotype- and Age-Specific Relapse Dynamics in Childhood Acute Lymphoblastic Leukemia: A Longitudinal Mechanistic StudySemantic proximity Read

A computational model of CAR T-cell immunotherapy predicts leukemia patient responses at remission, resistance, and relapseSemantic proximity Read

State-transition Modeling of Blood Transcriptome Predicts Disease Evolution and Treatment Response in Chronic Myeloid LeukemiaSemantic proximity Read

State-transition modeling of blood transcriptome predicts disease evolution and treatment response in chronic myeloid leukemiaSemantic proximity Read

Clinical Forecasting using <i>Ex Vivo</i> Drug Sensitivity Profiling of Acute Myeloid LeukemiaSemantic proximity Read

From molecular interaction to acute promyelocytic leukemia: Calculating leukemogenesis and remission from endogenous molecular-cellular networkSemantic proximity Read

DyNDG: Identifying Leukemia-Related Genes based on the Time-Series Dynamical Network by Integrating Differential GenesSemantic proximity Read

Supervised learning on synthetic data for reverse engineering gene regulatory networks from experimental time-seriesSemantic proximity Read