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Kira Radinsky

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15 papers

2 author rows

TMLR Journal 2026 Journal Article

Mechanism-Aware Prediction of Tissue-Specific Drug Activity via Multi-Modal Biological Graphs

Sally Turutov
Kira Radinsky

Predicting how small molecules behave across human tissues is essential for targeted therapy development. While some existing models incorporate tissue identity, they treat it as a label—ignoring the underlying biological mechanisms that differentiate tissues. We present Expresso, a multi-modal architecture that predicts tissue-contextual molecular activity as measured by the assay by modeling how compounds interact with transcriptomic and pathway-level tissue context. Expresso constructs heterogeneous graphs from GTEx data, linking samples, genes, and pathways to reflect expression profiles and curated biological relationships. These graphs are encoded using a hierarchical GNN and fused with frozen molecular embeddings to produce context-aware predictions. A multi-task pretraining strategy—spanning gene recovery, tissue classification, and pathway-level contrastive learning—guides the model to learn mechanistically grounded representations. On nine tissues, Expresso improves mean AUC by up to 27.9 points over molecule-only baselines. Our results demonstrate that incorporating biological structure—as defined by the assay—yields more accurate and interpretable models for tissue-specific drug behavior in human cell-based in vitro assay systems.

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TMLR Journal 2026 Journal Article

Neural Fourier Transform for Multiple Time Series Prediction

Noam Koren
Kira Radinsky
Daniel Freedman

Multivariate time series forecasting is an important task in various fields such as economic planning, healthcare management, and environmental monitoring. In this work, we present a novel methodology for improving multivariate forecasting, particularly, in data sets with strong seasonality. We frame the forecasting task as a Multi-Dimensional Fourier Transform (MFT) problem and propose the Neural Fourier Transform (NFT) that leverages a deep learning model to predict future time series values by learning the MFT coefficients. The efficacy of NFT is empirically validated on 7 diverse datasets, demonstrating improvements over multiple forecasting horizons and lookbacks, thereby establishing new state-of-the-art results. Our contributions advance the field of multivariate time series forecasting by providing a model that excels in predictive accuracy. The code of this study is publicly available.

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TMLR Journal 2026 Journal Article

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. This is particularly true in the synthesis of electrocardiograms (ECGs), where the objective is to develop a synthetic 12-lead ECG model. The primary challenge in this task lies in accurately modeling the intricate biological and physiological interactions among different ECG leads. Although mathematical process models have shed light on these dynamics, effectively incorporating this understanding into generative models is not straightforward. We introduce an innovative method that employs ordinary differential equations (ODEs) to enhance the fidelity of 12-lead ECG data generation. This approach integrates cardiac dynamics directly into the generative optimization process via a novel Euler Loss, producing biologically plausible data that respects real-world variability and inter-lead constraints. Empirical analysis on the G12EC and PTB-XL datasets demonstrates that augmenting training data with MultiODE-GAN yields consistent, statistically significant improvements in specificity across multiple cardiac abnormalities. This highlights the value of enforcing physiological coherence in synthetic medical data.

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AAAI Conference 2026 Conference Paper

PDE-Driven Spatiotemporal Generative Modeling for Multilead ECG Synthesis

Yakir Yehuda
Kira Radinsky

Synthesizing realistic 12-lead electrocardiogram (ECG) data is a complex task due to the intricate spatial and temporal dynamics of cardiac electrophysiology. Traditional generative models often struggle to capture the nuanced interdependencies among ECG leads, which are essential for accurate medical analysis. In this paper, we propose Physics-Inspired Partial Differential Equation GAN for Multilead ECG Synthesis (PhysioPDE-GAN), a generative framework designed to model the spatiotemporal structure of multilead ECG signals by incorporating physiological priors and spatial constraints directly into the generative process. By embedding PDE-based representations directly into the generative process, our approach effectively captures both the temporal evolution and spatial relationships between ECG leads. We conduct extensive experiments to evaluate the performance of various base classifiers trained on the synthetic 12-lead ECG data generated by PhysioPDE-GAN. These classifiers outperform those trained on data produced by other conventional methods, achieving statistically significant improvements in detecting cardiac abnormalities. Our work highlights the potential of combining PDE-driven cardiac models with advanced generative techniques to enhance the quality and utility of synthetic biomedical datasets.

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AAAI Conference 2026 Conference Paper

SVD-NO: Learning PDE Solution Operators with SVD Integral Kernels

Noam Koren
Ralf J. J. Mackenbach
Ruud J. G. van Sloun
Kira Radinsky
Daniel Freedman

Neural operators have emerged as a promising paradigm for learning solution operators of partial differential equations (PDEs) directly from data. Existing methods, such as those based on Fourier or graph techniques, make strong assumptions about the structure of the kernel integral operator, assumptions which may limit expressivity. We present SVD-NO, a neural operator that explicitly parameterizes the kernel by its singular-value decomposition (SVD) and then carries out the integral directly in the low-rank basis. Two lightweight networks learn the left and right singular functions, a diagonal parameter matrix learns the singular values, and a Gram-matrix regularizer enforces orthonormality. As SVD-NO approximates the full kernel, it obtains a high degree of expressivity. Furthermore, due to its low-rank structure the computational complexity of applying the operator remains reasonable, leading to a practical system. In extensive evaluations on five diverse benchmark equations, SVD-NO achieves a new state of the art. In particular, SVD-NO provides greater performance gains on PDEs whose solutions are highly spatially variable.

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NeurIPS Conference 2025 Conference Paper

BioCG: Constrained Generative Modeling for Biochemical Interaction Prediction

Amitay Sicherman
Kira Radinsky

Predicting interactions between biochemical entities is a core challenge in drug discovery and systems biology, often hindered by limited data and poor generalization to unseen entities. Traditional discriminative models frequently underperform in such settings. We propose BioCG (Biochemical Constrained Generation), a novel framework that reformulates interaction prediction as a constrained sequence generation task. BioCG encodes target entities as unique discrete sequences via Iterative Residual Vector Quantization (I-RVQ) and trains a generative model to produce the sequence of an interacting partner given a query entity. A trie-guided constrained decoding mechanism, built from a catalog of valid target sequences, concentrates the model's learning on the critical distinctions between valid biochemical options, ensuring all outputs correspond to an entity within the pre-defined target catalog. An information-weighted training objective further focuses learning on the most critical decision points. BioCG achieves state-of-the-art (SOTA) performance across diverse tasks, Drug-Target Interaction (DTI), Drug-Drug Interaction (DDI), and Enzyme-Reaction Prediction, especially in data-scarce and cold-start conditions. On the BioSNAP DTI benchmark, for example, BioCG attains an AUC of 89. 31\% on unseen proteins, representing a 14. 3 percentage point gain over prior SOTA. By directly generating interacting partners from a known biochemical space, BioCG provides a robust and data-efficient solution for in-silico biochemical discovery.

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TMLR Journal 2025 Journal Article

FusionProt: Fusing Sequence and Structural Information for Unified Protein Representation Learning

Dan Kalifa
Uriel Singer
Kira Radinsky

Accurate protein representations that integrate sequence and three-dimensional (3D) structure are critical to many biological and biomedical tasks. Most existing models either ignore structure or combine it with sequence through a single, static fusion step. Here we present FusionProt, a unified model that learns representations via iterative, bidirectional fusion between a protein language model and a structure encoder. A single learnable token serves as a carrier, alternating between sequence attention and spatial message passing across layers. FusionProt is evaluated on Enzyme Commission (EC), Gene Ontology (GO), and mutation stability prediction tasks. It improves F\textsubscript{max} by a median of $+1.3$ points (up to $+2.0$) across EC and GO benchmarks, and boosts AUROC by $+3.6$ points over the strongest baseline on mutation stability. Inference cost remains practical, with only $\sim2\text{--}5\%$ runtime overhead. Beyond state-of-the-art performance, we further demonstrate FusionProt’s practical relevance through representative biological case studies, suggesting that the model captures biologically relevant features.

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AAAI Conference 2024 Conference Paper

Molecular Optimization Model with Patentability Constraint

Sally Turutov
Kira Radinsky

In drug development, molecular optimization is a crucial challenge that involves generating novel molecules given a lead molecule as input. The task requires maintaining molecular similarity to the original molecule while simultaneously optimizing multiple chemical attributes. To aid in this process, numerous generative models have been proposed. However, in practical applications, it is crucial for these models not only to generate novel molecules with the above constraints but also to generate molecules that significantly differ from any existing patented compounds. In this work, we present a multi-optimization molecular framework to address this challenge. Our framework trains a model to prioritize both enhanced properties and substantial dissimilarity from patented compounds. By jointly learning continuous representations of optimized and patentable molecules, we ensure that the generated molecules are significantly distant from any patented compounds while improving chemical properties. Through empirical evaluation, we demonstrate the superior performance of our approach compared to state-of-the-art molecular optimization methods both in chemical property optimization and patentability.

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AAAI Conference 2022 Conference Paper

EqGNN: Equalized Node Opportunity in Graphs

Uriel Singer
Kira Radinsky

Graph neural networks (GNNs), has been widely used for supervised learning tasks in graphs reaching state-of-the-art results. However, little work was dedicated to creating unbiased GNNs, i. e. , where the classification is uncorrelated with sensitive attributes, such as race or gender. Some ignore the sensitive attributes or optimize for the criteria of statistical parity for fairness. However, it has been shown that neither approaches ensure fairness, but rather cripple the utility of the prediction task. In this work, we present a GNN framework that allows optimizing representations for the notion of Equalized Odds fairness criteria. The architecture is composed of three components: (1) a GNN classifier predicting the utility class, (2) a sampler learning the distribution of the sensitive attributes of the nodes given their labels. It generates samples fed into a (3) discriminator that discriminates between true and sampled sensitive attributes using a novel “permutation loss” function. Using these components, we train a model to neglect information regarding the sensitive attribute only with respect to its label. To the best of our knowledge, we are the first to optimize GNNs for the equalized odds criteria. We evaluate our classifier over several graph datasets and sensitive attributes and show our algorithm reaches state-of-the-art results.

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ICML Conference 2021 Conference Paper

12-Lead ECG Reconstruction via Koopman Operators

Tomer Golany
Kira Radinsky
Daniel Freedman
Saar Minha

32% of all global deaths in the world are caused by cardiovascular diseases. Early detection, especially for patients with ischemia or cardiac arrhythmia, is crucial. To reduce the time between symptoms onset and treatment, wearable ECG sensors were developed to allow for the recording of the full 12-lead ECG signal at home. However, if even a single lead is not correctly positioned on the body that lead becomes corrupted, making automatic diagnosis on the basis of the full signal impossible. In this work, we present a methodology to reconstruct missing or noisy leads using the theory of Koopman Operators. Given a dataset consisting of full 12-lead ECGs, we learn a dynamical system describing the evolution of the 12 individual signals together in time. The Koopman theory indicates that there exists a high-dimensional embedding space in which the operator which propagates from one time instant to the next is linear. We therefore learn both the mapping to this embedding space, as well as the corresponding linear operator. Armed with this representation, we are able to impute missing leads by solving a least squares system in the embedding space, which can be achieved efficiently due to the sparse structure of the system. We perform an empirical evaluation using 12-lead ECG signals from thousands of patients, and show that we are able to reconstruct the signals in such way that enables accurate clinical diagnosis.

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AAAI Conference 2021 Conference Paper

ECG ODE-GAN: Learning Ordinary Differential Equations of ECG Dynamics via Generative Adversarial Learning

Tomer Golany
Daniel Freedman
Kira Radinsky

Understanding the dynamics of complex biological and physiological systems has been explored for many years in the form of physically-based mathematical simulators. The behavior of a physical system is often described via ordinary differential equations (ODE), referred to as the dynamics. In the standard case, the dynamics are derived from purely physical considerations. By contrast, in this work we study how the dynamics can be learned by a generative adversarial network which combines both physical and data considerations. As a use case, we focus on the dynamics of the heart signal electrocardiogram (ECG). We begin by introducing a new GAN framework, dubbed ODE-GAN, in which the generator learns the dynamics of a physical system in the form of an ordinary differential equation. Specifically, the generator network receives as input a value at a specific time step, and produces the derivative of the system at that time step. Thus, the ODE-GAN learns purely data-driven dynamics. We then show how to incorporate physical considerations into ODE- GAN. We achieve this through the introduction of an additional input to the ODE-GAN generator: physical parameters, which partially characterize the signal of interest. As we focus on ECG signals, we refer to this new framework as ECG- ODE-GAN. We perform an empirical evaluation and show that generating ECG heartbeats from our learned dynamics improves ECG heartbeat classification.

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ICML Conference 2020 Conference Paper

SimGANs: Simulator-Based Generative Adversarial Networks for ECG Synthesis to Improve Deep ECG Classification

Tomer Golany
Kira Radinsky
Daniel Freedman

Generating training examples for supervised tasks is a long sought after goal in AI. We study the problem of heart signal electrocardiogram (ECG) synthesis for improved heartbeat classification. ECG synthesis is challenging: the generation of training examples for such biological-physiological systems is not straightforward, due to their dynamic nature in which the various parts of the system interact in complex ways. However, an understanding of these dynamics has been developed for years in the form of mathematical process simulators. We study how to incorporate this knowledge into the generative process by leveraging a biological simulator for the task of ECG classification. Specifically, we use a system of ordinary differential equations representing heart dynamics, and incorporate this ODE system into the optimization process of a generative adversarial network to create biologically plausible ECG training examples. We perform empirical evaluation and show that heart simulation knowledge during the generation process improves ECG classification.

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AAAI Conference 2019 Conference Paper

Building Causal Graphs from Medical Literature and Electronic Medical Records

Galia Nordon
Gideon Koren
Varda Shalev
Benny Kimelfeld
Uri Shalit
Kira Radinsky

Large repositories of medical data, such as Electronic Medical Record (EMR) data, are recognized as promising sources for knowledge discovery. Effective analysis of such repositories often necessitate a thorough understanding of dependencies in the data. For example, if the patient age is ignored, then one might wrongly conclude a causal relationship between cataract and hypertension. Such confounding variables are often identified by causal graphs, where variables are connected by causal relationships. Current approaches to automatically building such graphs are based on text analysis over medical literature; yet, the result is typically a large graph of low precision. There are statistical methods for constructing causal graphs from observational data, but they are less suitable for dealing with a large number of covariates, which is the case in EMR data. Consequently, confounding variables are often identified by medical domain experts via a manual, expensive, and time-consuming process. We present a novel approach for automatically constructing causal graphs between medical conditions. The first part is a novel graph-based method to better capture causal relationships implied by medical literature, especially in the presence of multiple causal factors. Yet even after using these advanced text-analysis methods, the text data still contains many weak or uncertain causal connections. Therefore, we construct a second graph for these terms based on an EMR repository of over 1. 5M patients. We combine the two graphs, leaving only edges that have both medical-text-based and observational evidence. We examine several strategies to carry out our approach, and compare the precision of the resulting graphs using medical experts. Our results show a significant improvement in the precision of any of our methods compared to the state of the art.

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IJCAI Conference 2019 Conference Paper

Node Embedding over Temporal Graphs

Uriel Singer
Ido Guy
Kira Radinsky

In this work, we present a method for node embedding in temporal graphs. We propose an algorithm that learns the evolution of a temporal graph's nodes and edges over time and incorporates this dynamics in a temporal node embedding framework for different graph prediction tasks. We present a joint loss function that creates a temporal embedding of a node by learning to combine its historical temporal embeddings, such that it optimizes per given task (e. g. , link prediction). The algorithm is initialized using static node embeddings, which are then aligned over the representations of a node at different time points, and eventually adapted for the given task in a joint optimization. We evaluate the effectiveness of our approach over a variety of temporal graphs for the two fundamental tasks of temporal link prediction and multi-label node classification, comparing to competitive baselines and algorithmic alternatives. Our algorithm shows performance improvements across many of the datasets and baselines and is found particularly effective for graphs that are less cohesive, with a lower clustering coefficient.

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AAAI Conference 2019 Conference Paper

PGANs: Personalized Generative Adversarial Networks for ECG Synthesis to Improve Patient-Specific Deep ECG Classification

Tomer Golany
Kira Radinsky

The Electrocardiogram (ECG) is performed routinely by medical personnel to identify structural, functional and electrical cardiac events. Many attempts were made to automate this task using machine learning algorithms including classic supervised learning algorithms and deep neural networks, reaching state-of-the-art performance. The ECG signal conveys the specific electrical cardiac activity of each subject thus extreme variations are observed between patients. These variations are challenging for deep learning algorithms, and impede generalization. In this work, we propose a semisupervised approach for patient-specific ECG classification. We propose a generative model that learns to synthesize patient-specific ECG signals, which can then be used as additional training data to improve a patient-specific classifier performance. Empirical results prove that the generated signals significantly improve ECG classification in a patient-specific setting.

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