6 papers
SFO: Learning PDE Operators via Spectral Filtering
Noam Koren, Rafael Moschopoulos, Kira Radinsky +1
Partial differential equations (PDEs) govern complex systems, yet neural operators often struggle to efficiently capture the long-range, nonlocal interactions inherent in their sol…
GOProteinGNN: Leveraging Protein Knowledge Graphs for Protein Representation Learning
Dan Kalifa, Uriel Singer, Kira Radinsky
Proteins play a vital role in biological processes and are indispensable for living organisms. Accurate representation of proteins is crucial, especially in drug development. Recen…
ReactEmbed: A Plug-and-Play Module for Unifying Protein-Molecule Representations Guided by Biochemical Reaction Networks
Amitay Sicherman, Kira Radinsky
State-of-the-art models represent proteins and molecules in separate embedding manifolds, limiting the modeling of systemic biological processes. We introduce ReactEmbed, a lightwe…
ODE-Constrained Generative Modeling of Cardiac Dynamics for 12-Lead ECG Synthesis
Yakir Yehuda, Kira Radinsky
Generating realistic training data for supervised learning remains a significant challenge in artificial intelligence, particularly in domains where large, expert-labeled datasets…
SVD-NO: Learning PDE Solution Operators with SVD Integral Kernels
Noam Koren, Ralf J. J. Mackenbach, Ruud J. G. van Sloun +2
Neural operators have emerged as a promising paradigm for learning solution operators of partial differential equa- tions (PDEs) directly from data. Existing methods, such as those…
Docking-Aware Attention: Dynamic Protein Representations through Molecular Context Integration
Amitay Sicherman, Kira Radinsky
Computational prediction of enzymatic reactions represents a crucial challenge in sustainable chemical synthesis across various scientific domains, ranging from drug discovery to m…