MOPRED Symposium - Algorithms and Applications for Multi-Modal Data Integration
16.10.2024 @ 10:00 - 18:00
Program
Preliminary Overview
Download-
Welcome
Welcome and Opening
-
OMICs Integration + Networks
-
Scientific Talks
Talks from: Markus Joppich, Arber Qoku, Ekaterina Esenkova, Asad Usmani
-
Poster Short Talks
-
Group Photo
-
Lunch Break
-
Lunch + Poster Session
-
Prediction Models
-
Scientific Talks
Talks from: Robin Mayer, Ingvild Froberg Mathisen, Shamim Ashrafiyan, Keynote - Ivan Costa
-
Poster Award
-
Poster Session and Coffee
-
Biological Application
-
Scientific Talks
Talks from: Eva Herrmann and Iulia Dahmer, Konstantinos Makris, Zahra Moslehi, Aakanksha Singh
-
Closing
Keynote Speaker
Multi-modal Integration
Prof. Dr. Ivan Costa
Registration
Open Until:
30.8.2024Abstract Notification Until:
Topics
Relevant Key Concepts
Representation Learning
Representation learning automates feature extraction from raw data, enhancing model performance in tasks like natural language processing and computer vision.
Graph Neural Networks
Graph Neural Networks (GNNs) leverage relationships in graph-structured data to improve tasks like node classification and link prediction by updating node representations based on their neighbors.
Mechanistic Models
Mechanistic models describe system behaviors based on fundamental principles, simulating complex dynamics for fields like epidemiology and engineering.
Statistical Approaches
Statistical approaches use mathematical methods to analyze data, making inferences and predictions to uncover patterns and inform decision-making.