Improving prediction accuracy in chimeric proteins with windowed multiple sequence alignment
S. Vedula, A.M. Bronstein, A. Marx, Computational and Structural Biotechnology Journal 27 (2025) 3292–3298.

Replication Data for: "Improving Prediction Accuracy in Chimeric Proteins with Windowed Multiple Sequence Alignment"
S. Vedula, A.M. Bronstein, A. Marx, (2025).

How local is clocald? Deep learning reveals locality of the induced magnetic field of polycyclic aromatic hydrocarbons
Y. Davidson, A. Philipp, S. Chakraborty, A.M. Bronstein, R. Gershoni-Poranne, Journal of Chemical Physics 162 (2025).

A dataset of alternately located segments in protein crystal structures
A.A. Rosenberg, A. Marx, A.M. Bronstein, Scientific Data 11 (2024).

Data-driven modeling of interrelated dynamical systems
Y. Elul, E. Rozenberg, A. Boyarski, Y. Yaniv, A. Schuster, A.M. Bronstein, Communications Physics 7 (2024).

Unsupervised physics-informed deep learning-based reconstruction for time-resolved imaging by multiplexed ptychography
O. Wengrowicz, A.M. Bronstein, O. Cohen, Optics Express 32 (2024) 8791–8803.

Enhancing predictive accuracy in embryo implantation: The Bonna algorithm and its clinical implications
G. Rave, D.E. Fordham, A.M. Bronstein, D.H. Silver, in:, First International Conference on Artificial Intelligence in Healthcare, Springer Nature, 2024, pp. 160–171.

Water stabilizes an alternate turn conformation in horse heart myoglobin
A.M. Bronstein, A. Marx, Scientific Reports 13 (2023).

Guided diffusion for inverse molecular design
T. Weiss, E. Mayo Yanes, S. Chakraborty, L. Cosmo, A.M. Bronstein, R. Gershoni-Poranne, Nature Computational Science 3 (2023) 873–882.

Interpretable deep-learning unveils structure–property relationships in polybenzenoid hydrocarbons
T. Weiss, A. Wahab, A.M. Bronstein, R. Gershoni-Poranne, The Journal of Organic Chemistry 88 (2023) 9645–9656.

GRACE: A scalable graph-based approach to accelerating recommendation model inference
H. Ye, S. Vedula, Y. Chen, Y. Yang, A.M. Bronstein, R. Dreslinski, T. Mudge, N. Talati, in:, Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Association for Computing Machinery, 2023, pp. 282–301.

GRASP: Scalable graph alignment by spectral corresponding functions
J. Hermanns, K. Skitsas, A. Tsitsulin, M. Munkhoeva, A. Kyster, S. Nielsen, A.M. Bronstein, D. Mottin, P. Karras, ACM Transactions on Knowledge Discovery from Data 17 (2023).

Demystifying graph sparsification algorithms in graph properties preservation
Y. Chen, H. Ye, S. Vedula, A.M. Bronstein, R. Dreslinski, T. Mudge, N. Talati, Proceedings of the VLDB Endowment 17 (2023) 427–440.

Using deep reinforcement learning for mmWave real-time scheduling
B. Gahtan, R. Cohen, A.M. Bronstein, G. Kedar, in:, 14th International Conference on Network of the Future, IEEE, 2023, pp. 71–79.

An amino-domino model described by a cross-peptide-bond Ramachandran plot defines amino acid pairs as local structural units
A.A. Rosenberg, N. Yehishalom, A. Marx, A.M. Bronstein, Proceedings of the National Academy of Sciences 120 (2023).

Towards predicting fine finger motions from ultrasound images via kinematic representation
D. Zadok, O. Salzman, A. Wolf, A.M. Bronstein, in:, 2023 IEEE International Conference on Robotics and Automation, IEEE, 2023.

Physical passive patch adversarial attacks on visual odometry systems
Y. Nemcovsky, M. Jacoby, A.M. Bronstein, C. Baskin, in:, 16th Asian Conference on Computer Vision, Springer Nature, 2023, pp. 518–534.

ISP Distillation
E. Schwartz, A.M. Bronstein, R. Giryes, IEEE Open Journal of Signal Processing 4 (2023) 12–20.

Adversarial robustness via noise injection in smoothed models
Y. Nemcovsky, E. Zheltonozhskii, C. Baskin, B. Chmiel, A.M. Bronstein, A. Mendelson, Applied Intelligence 53 (2022) 9483–9498.

Machine learning approaches demonstrate that protein structures carry information about their genetic coding
L. Ackerman-Schraier, A.A. Rosenberg, A. Marx, A.M. Bronstein, Scientific Reports 12 (2022).