Life Sciences and Health Seminar Sanne Abeln, Leah Dickhoff

Bioinformatics: from amyloid formation to oncology; Adaptive optimization for bi-objective treatment planning in cervical cancer brachytherapy

Zoom Meeting
https://cwi-nl.zoom.us/j/86089111701?pwd=WVVrSTBiZUt1SkI2ZTEzT2VNUXY1dz09

Meeting ID: 860 8911 1701
Passcode: 534920

Title:      Bioinformatics: from amyloid formation to oncology
Speaker:    Sanne Abeln
Abstract:   In our research group at the Computer Science Department of the VU we look at topics ranging from molecular dynamics simulations to large scale omics analysis with machine learning, most of it applied to the health domains of neurodegenerative disease and oncology.
In this talk I will give an overview of current results on multi-task learning for protein structural features, simulations that show the role of
hydrophobicity in amyloid formation, a machine learning based breakpoint analysis for colorectal cancer and a simple model for prediction of
proteins in extracellular vesicles.

Title:      Adaptive optimization for bi-objective treatment planning in cervical cancer brachytherapy
Speaker:    Leah Dickhoff
Abstract:   The previously developed bi-objective optimization method using the Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (RV-GOMEA) for prostate high-dose-rate brachytherapy (BT) has been extended to cervical cancer BT. This model directly optimizes on dose volume indices (DVIs), which describe specific maximum or minimum dose to subvolume relationships for each of the targets and organs at risk. Discussions with medical specialists have revealed that optimizing solely on the DVIs from the ESTRO-recommended protocol EMBRACE II does not suffice to obtain clinically acceptable treatment plans. Therefore, additional (potentially hospital-specific) DVIs need to be added to the objective functions. Because of interpatient variations in organ, target and applicator geometry, patient-specific aspiration values are required for these added DVIs. This entails the need for an adaptive optimization method, possibly incorporating different priorities in the objectives.