[{"OA_place":"publisher","author":[{"id":"94f2fe44-70fa-11f0-b76b-92922c09452b","last_name":"Vedula","full_name":"Vedula, Sanketh","first_name":"Sanketh"},{"first_name":"Alexander","full_name":"Bronstein, Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","last_name":"Bronstein","orcid":"0000-0001-9699-8730"},{"last_name":"Marx","first_name":"Ailie","full_name":"Marx, Ailie"}],"year":"2025","article_processing_charge":"Yes","title":"Improving prediction accuracy in chimeric proteins with windowed multiple sequence alignment","oa_version":"Published Version","publisher":"Elsevier","publication_status":"published","acknowledgement":"AM acknowledges the financial support of the Helmsley Fellowships Program for Sustainability and Health. AMB is supported by the Schmidt Chair in Artificial Intelligence.","status":"public","date_created":"2025-08-03T22:01:31Z","date_published":"2025-06-27T00:00:00Z","license":"https://creativecommons.org/licenses/by/4.0/","ddc":["000","570"],"day":"27","page":"3292-3298","type":"journal_article","scopus_import":"1","isi":1,"language":[{"iso":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","file_date_updated":"2025-08-04T06:25:23Z","citation":{"chicago":"Vedula, Sanketh, Alex M. Bronstein, and Ailie Marx. “Improving Prediction Accuracy in Chimeric Proteins with Windowed Multiple Sequence Alignment.” <i>Computational and Structural Biotechnology Journal</i>. Elsevier, 2025. <a href=\"https://doi.org/10.1016/j.csbj.2025.07.039\">https://doi.org/10.1016/j.csbj.2025.07.039</a>.","apa":"Vedula, S., Bronstein, A. M., &#38; Marx, A. (2025). Improving prediction accuracy in chimeric proteins with windowed multiple sequence alignment. <i>Computational and Structural Biotechnology Journal</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.csbj.2025.07.039\">https://doi.org/10.1016/j.csbj.2025.07.039</a>","ista":"Vedula S, Bronstein AM, Marx A. 2025. Improving prediction accuracy in chimeric proteins with windowed multiple sequence alignment. Computational and Structural Biotechnology Journal. 27, 3292–3298.","mla":"Vedula, Sanketh, et al. “Improving Prediction Accuracy in Chimeric Proteins with Windowed Multiple Sequence Alignment.” <i>Computational and Structural Biotechnology Journal</i>, vol. 27, Elsevier, 2025, pp. 3292–98, doi:<a href=\"https://doi.org/10.1016/j.csbj.2025.07.039\">10.1016/j.csbj.2025.07.039</a>.","ieee":"S. Vedula, A. M. Bronstein, and A. Marx, “Improving prediction accuracy in chimeric proteins with windowed multiple sequence alignment,” <i>Computational and Structural Biotechnology Journal</i>, vol. 27. Elsevier, pp. 3292–3298, 2025.","short":"S. Vedula, A.M. Bronstein, A. Marx, Computational and Structural Biotechnology Journal 27 (2025) 3292–3298.","ama":"Vedula S, Bronstein AM, Marx A. Improving prediction accuracy in chimeric proteins with windowed multiple sequence alignment. <i>Computational and Structural Biotechnology Journal</i>. 2025;27:3292-3298. doi:<a href=\"https://doi.org/10.1016/j.csbj.2025.07.039\">10.1016/j.csbj.2025.07.039</a>"},"quality_controlled":"1","OA_type":"gold","PlanS_conform":"1","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"has_accepted_license":"1","doi":"10.1016/j.csbj.2025.07.039","publication_identifier":{"eissn":["2001-0370"]},"related_material":{"link":[{"url":"https://github.com/sankethvedula/AFChimera","relation":"software"}],"record":[{"status":"public","id":"20103","relation":"software"}]},"date_updated":"2025-11-27T14:09:59Z","external_id":{"isi":["001583543100001"]},"oa":1,"month":"06","article_type":"original","file":[{"creator":"dernst","checksum":"78d01f30fc1dc11dd2bd1d7bb7ac8a62","file_id":"20104","success":1,"date_created":"2025-08-04T06:25:23Z","file_size":6609770,"content_type":"application/pdf","relation":"main_file","date_updated":"2025-08-04T06:25:23Z","access_level":"open_access","file_name":"2025_CompStrucBiotechJour_Vedula.pdf"}],"abstract":[{"text":"A key step in protein structure prediction involves the detection of co-evolving pairs of residues, a signal for spatial proximity. This information is gleaned from multiple sequence alignment and underscores Alphafold’s structure prediction for almost every known protein. A simple means to create proteins beyond those found in nature, is by unnaturally fusing together two known proteins or protein parts. Here we demonstrate that structured peptides are predicted with significantly reduced accuracy when added to the terminal ends of scaffold proteins. Appending the multiple sequence alignment for the individual peptide tags to that of the scaffold protein often restores prediction accuracy. This work suggests that this windowed multiple sequence alignment approach can be a useful tool for predicting the structure of fused, chimeric proteins.","lang":"eng"}],"_id":"20100","intvolume":"        27","publication":"Computational and Structural Biotechnology Journal","department":[{"_id":"AlBr"}],"DOAJ_listed":"1","volume":27},{"article_processing_charge":"No","title":"Replication Data for: \"Improving Prediction Accuracy in Chimeric Proteins with Windowed Multiple Sequence Alignment\"","oa_version":"Published Version","author":[{"full_name":"Vedula, Sanketh","first_name":"Sanketh","id":"94f2fe44-70fa-11f0-b76b-92922c09452b","last_name":"Vedula"},{"orcid":"0000-0001-9699-8730","last_name":"Bronstein","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","first_name":"Alexander","full_name":"Bronstein, Alexander"},{"last_name":"Marx","first_name":"Ailie","full_name":"Marx, Ailie"}],"citation":{"ieee":"S. Vedula, A. M. Bronstein, and A. Marx, “Replication Data for: ‘Improving Prediction Accuracy in Chimeric Proteins with Windowed Multiple Sequence Alignment.’” Harvard Dataverse, 2025.","chicago":"Vedula, Sanketh, Alex M. Bronstein, and Ailie Marx. “Replication Data for: ‘Improving Prediction Accuracy in Chimeric Proteins with Windowed Multiple Sequence Alignment.’” Harvard Dataverse, 2025. <a href=\"https://doi.org/10.7910/DVN/DYEBVM\">https://doi.org/10.7910/DVN/DYEBVM</a>.","mla":"Vedula, Sanketh, et al. <i>Replication Data for: “Improving Prediction Accuracy in Chimeric Proteins with Windowed Multiple Sequence Alignment.”</i> Harvard Dataverse, 2025, doi:<a href=\"https://doi.org/10.7910/DVN/DYEBVM\">10.7910/DVN/DYEBVM</a>.","apa":"Vedula, S., Bronstein, A. M., &#38; Marx, A. (2025). Replication Data for: “Improving Prediction Accuracy in Chimeric Proteins with Windowed Multiple Sequence Alignment.” Harvard Dataverse. <a href=\"https://doi.org/10.7910/DVN/DYEBVM\">https://doi.org/10.7910/DVN/DYEBVM</a>","ista":"Vedula S, Bronstein AM, Marx A. 2025. Replication Data for: ‘Improving Prediction Accuracy in Chimeric Proteins with Windowed Multiple Sequence Alignment’, Harvard Dataverse, <a href=\"https://doi.org/10.7910/DVN/DYEBVM\">10.7910/DVN/DYEBVM</a>.","ama":"Vedula S, Bronstein AM, Marx A. Replication Data for: “Improving Prediction Accuracy in Chimeric Proteins with Windowed Multiple Sequence Alignment.” 2025. doi:<a href=\"https://doi.org/10.7910/DVN/DYEBVM\">10.7910/DVN/DYEBVM</a>","short":"S. Vedula, A.M. Bronstein, A. Marx, (2025)."},"year":"2025","OA_place":"repository","main_file_link":[{"open_access":"1","url":"https://doi.org/10.7910/DVN/DYEBVM"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_created":"2025-08-04T06:18:55Z","status":"public","has_accepted_license":"1","doi":"10.7910/DVN/DYEBVM","publisher":"Harvard Dataverse","tmp":{"legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","short":"CC0 (1.0)","name":"Creative Commons Public Domain Dedication (CC0 1.0)","image":"/images/cc_0.png"},"oa":1,"date_updated":"2025-11-27T14:09:58Z","related_material":{"record":[{"id":"20100","relation":"used_for_analysis_in","status":"public"}]},"date_published":"2025-06-27T00:00:00Z","department":[{"_id":"AlBr"}],"ddc":["000"],"day":"27","abstract":[{"text":"Official implementation, windowed MSAs, and the predictions as reported in the manuscript titled \"Improving Prediction Accuracy in Chimeric Proteins with Windowed Multiple Sequence Alignment\". (2025-06-27)","lang":"eng"}],"_id":"20103","type":"research_data_reference","license":"https://creativecommons.org/publicdomain/zero/1.0/","month":"06"},{"citation":{"ieee":"B. Gahtan, S. Vedula, G. Samuelly Leichtag, E. Kodesh, and A. M. Bronstein, “From lab to wrist: Bridging metabolic monitoring and consumer wearables for heart rate and oxygen consumption modeling,” in <i>Proceedings of the 27th International Conference on Multimodal Interaction</i>, Canberra, Australia, 2025, pp. 60–77.","chicago":"Gahtan, Barak, Sanketh Vedula, Gil Samuelly Leichtag, Einat Kodesh, and Alex M. Bronstein. “From Lab to Wrist: Bridging Metabolic Monitoring and Consumer Wearables for Heart Rate and Oxygen Consumption Modeling.” In <i>Proceedings of the 27th International Conference on Multimodal Interaction</i>, 60–77. Association for Computing Machinery, 2025. <a href=\"https://doi.org/10.1145/3716553.3750815\">https://doi.org/10.1145/3716553.3750815</a>.","mla":"Gahtan, Barak, et al. “From Lab to Wrist: Bridging Metabolic Monitoring and Consumer Wearables for Heart Rate and Oxygen Consumption Modeling.” <i>Proceedings of the 27th International Conference on Multimodal Interaction</i>, Association for Computing Machinery, 2025, pp. 60–77, doi:<a href=\"https://doi.org/10.1145/3716553.3750815\">10.1145/3716553.3750815</a>.","apa":"Gahtan, B., Vedula, S., Samuelly Leichtag, G., Kodesh, E., &#38; Bronstein, A. M. (2025). From lab to wrist: Bridging metabolic monitoring and consumer wearables for heart rate and oxygen consumption modeling. In <i>Proceedings of the 27th International Conference on Multimodal Interaction</i> (pp. 60–77). Canberra, Australia: Association for Computing Machinery. <a href=\"https://doi.org/10.1145/3716553.3750815\">https://doi.org/10.1145/3716553.3750815</a>","ista":"Gahtan B, Vedula S, Samuelly Leichtag G, Kodesh E, Bronstein AM. 2025. From lab to wrist: Bridging metabolic monitoring and consumer wearables for heart rate and oxygen consumption modeling. Proceedings of the 27th International Conference on Multimodal Interaction. ICMI: International Conference on Multimodal Interaction, 60–77.","ama":"Gahtan B, Vedula S, Samuelly Leichtag G, Kodesh E, Bronstein AM. From lab to wrist: Bridging metabolic monitoring and consumer wearables for heart rate and oxygen consumption modeling. In: <i>Proceedings of the 27th International Conference on Multimodal Interaction</i>. Association for Computing Machinery; 2025:60-77. doi:<a href=\"https://doi.org/10.1145/3716553.3750815\">10.1145/3716553.3750815</a>","short":"B. Gahtan, S. Vedula, G. Samuelly Leichtag, E. Kodesh, A.M. Bronstein, in:, Proceedings of the 27th International Conference on Multimodal Interaction, Association for Computing Machinery, 2025, pp. 60–77."},"file_date_updated":"2025-12-01T07:19:06Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","OA_type":"hybrid","conference":{"location":"Canberra, Australia","name":"ICMI: International Conference on Multimodal Interaction","end_date":"2025-10-17","start_date":"2025-10-13"},"quality_controlled":"1","doi":"10.1145/3716553.3750815","has_accepted_license":"1","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"publication_identifier":{"isbn":["9798400714993"]},"external_id":{"arxiv":["2505.00101"]},"date_updated":"2025-12-01T07:22:09Z","corr_author":"1","oa":1,"abstract":[{"text":"Understanding physiological responses during running is critical for performance optimization, tailored training prescriptions, and athlete health management. We introduce a comprehensive framework—what we believe to be the first capable of predicting instantaneous oxygen consumption (VO2) trajectories exclusively from consumer-grade wearable data. Our approach employs two complementary physiological models: (1) accurate modeling of heart rate (HR) dynamics via a physiologically constrained ordinary differential equation (ODE) and neural Kalman filter, trained on over 3 million HR observations, achieving 1-second interval predictions with mean absolute errors as low as 2.81 bpm (correlation 0.87); and (2) leveraging the principles of precise HR modeling, a novel VO2 prediction architecture requiring only the initial second of VO2 data for calibration, enabling robust, sequence-to-sequence metabolic demand estimation. Despite relying solely on smartwatch and chest-strap data, our method achieves mean absolute percentage errors of approximately 13%, effectively capturing rapid physiological transitions and steady-state conditions across diverse running intensities. Our synchronized dataset, complemented by blood lactate measurements, further lays the foundation for future noninvasive metabolic zone identification. By embedding physiological constraints within modern machine learning, this framework democratizes advanced metabolic monitoring, bridging laboratory-grade accuracy and everyday accessibility, thus empowering both elite athletes and recreational fitness enthusiasts.","lang":"eng"}],"_id":"20707","file":[{"file_name":"2025_ICMI_Gahtan.pdf","access_level":"open_access","date_updated":"2025-12-01T07:19:06Z","relation":"main_file","date_created":"2025-12-01T07:19:06Z","success":1,"file_size":3045062,"content_type":"application/pdf","file_id":"20713","checksum":"f793472a71d27012244567b499a4967f","creator":"dernst"}],"month":"10","department":[{"_id":"AlBr"}],"publication":"Proceedings of the 27th International Conference on Multimodal Interaction","year":"2025","author":[{"last_name":"Gahtan","full_name":"Gahtan, Barak","first_name":"Barak"},{"first_name":"Sanketh","full_name":"Vedula, Sanketh","last_name":"Vedula"},{"full_name":"Samuelly Leichtag, Gil","first_name":"Gil","last_name":"Samuelly Leichtag"},{"last_name":"Kodesh","full_name":"Kodesh, Einat","first_name":"Einat"},{"orcid":"0000-0001-9699-8730","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","last_name":"Bronstein","first_name":"Alexander","full_name":"Bronstein, Alexander"}],"OA_place":"publisher","arxiv":1,"oa_version":"Published Version","article_processing_charge":"No","title":"From lab to wrist: Bridging metabolic monitoring and consumer wearables for heart rate and oxygen consumption modeling","publication_status":"published","publisher":"Association for Computing Machinery","date_created":"2025-11-30T23:02:08Z","status":"public","date_published":"2025-10-12T00:00:00Z","type":"conference","page":"60-77","day":"12","ddc":["000"],"language":[{"iso":"eng"}],"scopus_import":"1"},{"publication_identifier":{"issn":["0021-9606"],"eissn":["1089-7690"]},"tmp":{"short":"CC BY-NC (4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc/4.0/legalcode","image":"/images/cc_by_nc.png","name":"Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)"},"has_accepted_license":"1","doi":"10.1063/5.0257558","quality_controlled":"1","OA_type":"hybrid","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","article_number":"144101","citation":{"ieee":"Y. Davidson, A. Philipp, S. Chakraborty, A. M. Bronstein, and R. Gershoni-Poranne, “How local is ‘local’? Deep learning reveals locality of the induced magnetic field of polycyclic aromatic hydrocarbons,” <i>Journal of Chemical Physics</i>, vol. 162, no. 14. AIP Publishing, 2025.","chicago":"Davidson, Yair, Aviad Philipp, Sabyasachi Chakraborty, Alex M. Bronstein, and Renana Gershoni-Poranne. “How Local Is ‘Local’? Deep Learning Reveals Locality of the Induced Magnetic Field of Polycyclic Aromatic Hydrocarbons.” <i>Journal of Chemical Physics</i>. AIP Publishing, 2025. <a href=\"https://doi.org/10.1063/5.0257558\">https://doi.org/10.1063/5.0257558</a>.","ista":"Davidson Y, Philipp A, Chakraborty S, Bronstein AM, Gershoni-Poranne R. 2025. How local is “local”? Deep learning reveals locality of the induced magnetic field of polycyclic aromatic hydrocarbons. Journal of Chemical Physics. 162(14), 144101.","mla":"Davidson, Yair, et al. “How Local Is ‘Local’? Deep Learning Reveals Locality of the Induced Magnetic Field of Polycyclic Aromatic Hydrocarbons.” <i>Journal of Chemical Physics</i>, vol. 162, no. 14, 144101, AIP Publishing, 2025, doi:<a href=\"https://doi.org/10.1063/5.0257558\">10.1063/5.0257558</a>.","apa":"Davidson, Y., Philipp, A., Chakraborty, S., Bronstein, A. M., &#38; Gershoni-Poranne, R. (2025). How local is “local”? Deep learning reveals locality of the induced magnetic field of polycyclic aromatic hydrocarbons. <i>Journal of Chemical Physics</i>. AIP Publishing. <a href=\"https://doi.org/10.1063/5.0257558\">https://doi.org/10.1063/5.0257558</a>","ama":"Davidson Y, Philipp A, Chakraborty S, Bronstein AM, Gershoni-Poranne R. How local is “local”? Deep learning reveals locality of the induced magnetic field of polycyclic aromatic hydrocarbons. <i>Journal of Chemical Physics</i>. 2025;162(14). doi:<a href=\"https://doi.org/10.1063/5.0257558\">10.1063/5.0257558</a>","short":"Y. Davidson, A. Philipp, S. Chakraborty, A.M. Bronstein, R. Gershoni-Poranne, Journal of Chemical Physics 162 (2025)."},"file_date_updated":"2025-04-22T09:27:43Z","publication":"Journal of Chemical Physics","department":[{"_id":"AlBr"}],"volume":162,"article_type":"original","month":"04","file":[{"file_id":"19606","date_created":"2025-04-22T09:27:43Z","success":1,"file_size":7812182,"content_type":"application/pdf","creator":"dernst","checksum":"20a31a4c506b52de863bab7d3ff989ef","file_name":"2025_JourChemicalPhysics_Davidson.pdf","relation":"main_file","access_level":"open_access","date_updated":"2025-04-22T09:27:43Z"}],"abstract":[{"lang":"eng","text":"We investigate the locality of magnetic response in polycyclic aromatic molecules using a novel deep-learning approach. Our method employs graph neural networks (GNNs) with a graph-of-rings representation to predict nucleus independent chemical shifts (NICS) in the space around the molecule. We train a series of models, each time reducing the size of the largest molecules used in training. The accuracy of prediction remains high (MAE < 0.5 ppm), even when training the model only on molecules with up to four rings, thus providing strong evidence for the locality of magnetic response. To overcome the known problem of generalization of GNNs, we implement a k-hop expansion strategy and succeed in achieving accurate predictions for molecules with up to 15 rings (almost 4 times the size of the largest training example). Our findings have implications for understanding the magnetic response in complex molecules and demonstrate a promising approach to overcoming GNN scalability limitations. Furthermore, the trained models enable rapid characterization, without the need for more expensive DFT calculations."}],"_id":"19595","intvolume":"       162","corr_author":"1","oa":1,"related_material":{"link":[{"relation":"software","url":"https://gitlab.com/porannegroup/magnetic_locality"}]},"date_updated":"2025-09-30T12:06:51Z","external_id":{"pmid":["40197568"],"isi":["001466311300030"]},"project":[{"_id":"92f4a086-16d5-11f0-9cad-c929f5c58b0c","name":"Acoustics-based drone navigation and interaction","grant_number":"863839"}],"acknowledgement":"The authors express their gratitude to Professor Dr. Peter Chen for his continued support. The authors acknowledge the Branco Weiss Fellowship for supporting this research as part of a Society in Science grant and the Israel Science Foundation for financial support (Grant No. 1745/23 to R.G.-P.). R.G.-P. is a Branco Weiss Fellow, a Horev Fellow, and an Alon Scholarship recipient. A.M.B. was supported by the ERC StG EARS and the Israeli Science Foundation.","date_created":"2025-04-20T22:01:28Z","status":"public","publisher":"AIP Publishing","publication_status":"published","article_processing_charge":"Yes (in subscription journal)","title":"How local is “local”? Deep learning reveals locality of the induced magnetic field of polycyclic aromatic hydrocarbons","oa_version":"Published Version","pmid":1,"OA_place":"publisher","author":[{"last_name":"Davidson","full_name":"Davidson, Yair","first_name":"Yair"},{"full_name":"Philipp, Aviad","first_name":"Aviad","last_name":"Philipp"},{"last_name":"Chakraborty","full_name":"Chakraborty, Sabyasachi","first_name":"Sabyasachi"},{"last_name":"Bronstein","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","full_name":"Bronstein, Alexander","first_name":"Alexander","orcid":"0000-0001-9699-8730"},{"last_name":"Gershoni-Poranne","full_name":"Gershoni-Poranne, Renana","first_name":"Renana"}],"year":"2025","scopus_import":"1","isi":1,"language":[{"iso":"eng"}],"license":"https://creativecommons.org/licenses/by-nc/4.0/","day":"14","ddc":["000"],"type":"journal_article","issue":"14","date_published":"2025-04-14T00:00:00Z"},{"doi":"10.3847/1538-4357/ae0cbc","has_accepted_license":"1","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"publication_identifier":{"eissn":["1538-4357"],"issn":["0004-637X"]},"file_date_updated":"2026-02-17T12:32:18Z","citation":{"ama":"Kamai I, Bronstein AM, Perets HB. Machine Learning inference of stellar properties using integrated photometric and spectroscopic data. <i>The Astrophysical Journal</i>. 2025;994. doi:<a href=\"https://doi.org/10.3847/1538-4357/ae0cbc\">10.3847/1538-4357/ae0cbc</a>","short":"I. Kamai, A.M. Bronstein, H.B. Perets, The Astrophysical Journal 994 (2025).","ieee":"I. Kamai, A. M. Bronstein, and H. B. Perets, “Machine Learning inference of stellar properties using integrated photometric and spectroscopic data,” <i>The Astrophysical Journal</i>, vol. 994. IOP Publishing, 2025.","apa":"Kamai, I., Bronstein, A. M., &#38; Perets, H. B. (2025). Machine Learning inference of stellar properties using integrated photometric and spectroscopic data. <i>The Astrophysical Journal</i>. IOP Publishing. <a href=\"https://doi.org/10.3847/1538-4357/ae0cbc\">https://doi.org/10.3847/1538-4357/ae0cbc</a>","mla":"Kamai, Ilay, et al. “Machine Learning Inference of Stellar Properties Using Integrated Photometric and Spectroscopic Data.” <i>The Astrophysical Journal</i>, vol. 994, 110, IOP Publishing, 2025, doi:<a href=\"https://doi.org/10.3847/1538-4357/ae0cbc\">10.3847/1538-4357/ae0cbc</a>.","ista":"Kamai I, Bronstein AM, Perets HB. 2025. Machine Learning inference of stellar properties using integrated photometric and spectroscopic data. The Astrophysical Journal. 994, 110.","chicago":"Kamai, Ilay, Alex M. Bronstein, and Hagai B. Perets. “Machine Learning Inference of Stellar Properties Using Integrated Photometric and Spectroscopic Data.” <i>The Astrophysical Journal</i>. IOP Publishing, 2025. <a href=\"https://doi.org/10.3847/1538-4357/ae0cbc\">https://doi.org/10.3847/1538-4357/ae0cbc</a>."},"article_number":"110","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","PlanS_conform":"1","OA_type":"gold","quality_controlled":"1","_id":"21246","intvolume":"       994","abstract":[{"text":"Stellar astrophysics relies on diverse observational modalities—primarily photometric light curves and spectroscopic data—from which fundamental stellar properties are inferred. While machine learning (ML) has advanced analysis within individual modalities, the complementary information encoded across modalities remains largely underexploited. We present the dual embedding for stellar astronomy (DESA) model, a novel multimodal foundation model that integrates light curves and spectra to learn a unified, physically meaningful latent space for stars. DESA first trains separate modality-specific encoders using a hybrid supervised/self-supervised scheme, and then aligns them through DualFormer, a transformer-based cross-modal integration module tailored for astrophysical data. DualFormer combines cross- and self-attention, a novel dual-projection alignment loss, and a projection-space eigendecomposition that yields physically structured embeddings. We demonstrate that DESA significantly outperforms leading unimodal and self-supervised baselines across a range of tasks. In zero- and few-shot settings, DESA’s learned representations recover stellar color–magnitude and Hertzsprung–Russell diagrams with high fidelity (R2 = 0.92 for photometric regressions). In full fine-tuning, DESA achieves state-of-the-art accuracy for binary star detection (AUC = 0.99, AP = 1.00) and stellar age prediction (RMSE = 0.94 Gyr). As a compelling case, DESA naturally separates synchronized binaries from young stars—two populations with nearly identical light curves—purely from their embedded positions in UMAP space, without requiring external kinematic or luminosity information. DESA thus offers a powerful new framework for multimodal, data-driven stellar population analysis, enabling both accurate prediction and novel discovery.","lang":"eng"}],"file":[{"checksum":"255ffd6d664e6c2d1cffbaced650bd10","creator":"dernst","content_type":"application/pdf","file_size":16415089,"date_created":"2026-02-17T12:32:18Z","success":1,"file_id":"21302","date_updated":"2026-02-17T12:32:18Z","access_level":"open_access","relation":"main_file","file_name":"2025_AstrophysicalJournal_Kamai.pdf"}],"month":"11","article_type":"original","volume":994,"DOAJ_listed":"1","department":[{"_id":"AlBr"}],"publication":"The Astrophysical Journal","external_id":{"arxiv":["2507.10666"]},"date_updated":"2026-02-17T12:33:19Z","oa":1,"publication_status":"published","publisher":"IOP Publishing","date_created":"2026-02-16T15:35:29Z","status":"public","acknowledgement":"This research was partially supported by the Israeli Science Foundation grant 1834/24.","year":"2025","author":[{"last_name":"Kamai","first_name":"Ilay","full_name":"Kamai, Ilay"},{"first_name":"Alexander","full_name":"Bronstein, Alexander","last_name":"Bronstein","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","orcid":"0000-0001-9699-8730"},{"last_name":"Perets","first_name":"Hagai B.","full_name":"Perets, Hagai B."}],"OA_place":"publisher","arxiv":1,"oa_version":"Published Version","title":"Machine Learning inference of stellar properties using integrated photometric and spectroscopic data","article_processing_charge":"Yes","type":"journal_article","ddc":["520","000"],"day":"19","language":[{"iso":"eng"}],"date_published":"2025-11-19T00:00:00Z"},{"publication_status":"published","publisher":"ML Research Press","acknowledgement":"This work was supported by the Israeli Science Foundation (ISF) grant number 1834/24. We acknowledge support from the Austrian Science Fund (FWF, grant numbers I5812-B and I6223) and the financial support of the Helmsley Fellowships Program for Sustainability and Health. This research uses resources of the Institute of Science and Technology Austria’s scientific computing cluster. ","status":"public","date_created":"2026-02-18T12:11:17Z","project":[{"_id":"eb9c82eb-77a9-11ec-83b8-aadd536561cf","name":"AlloSpace. The emergence and mechanisms of allostery","grant_number":"I05812"},{"_id":"bdb9578d-d553-11ed-ba76-ed5d39fce6f0","grant_number":"I06223","name":"Structure and mechanism of the mitochondrial MIM insertase"}],"author":[{"first_name":"Sai A","full_name":"Maddipatla, Sai A","last_name":"Maddipatla","id":"e957f5e5-91c9-11f0-a95f-e090f66ecb4d"},{"id":"ef280fe0-91c9-11f0-a95f-8dea3f5bc513","last_name":"Sellam","full_name":"Sellam, Nadav E","first_name":"Nadav E"},{"first_name":"Meital I","full_name":"Bojan, Meital I","id":"11d88cf5-91ca-11f0-a95f-edf9f08f47b7","last_name":"Bojan"},{"last_name":"Vedula","id":"94f2fe44-70fa-11f0-b76b-92922c09452b","full_name":"Vedula, Sanketh","first_name":"Sanketh"},{"first_name":"Paul","full_name":"Schanda, Paul","last_name":"Schanda","id":"7B541462-FAF6-11E9-A490-E8DFE5697425","orcid":"0000-0002-9350-7606"},{"last_name":"Marx","full_name":"Marx, Ailie","first_name":"Ailie"},{"last_name":"Bronstein","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","full_name":"Bronstein, Alexander","first_name":"Alexander","orcid":"0000-0001-9699-8730"}],"year":"2025","OA_place":"publisher","arxiv":1,"title":"Inverse problems with experiment-guided AlphaFold","article_processing_charge":"No","oa_version":"Published Version","ddc":["000","540"],"page":"42366 - 42393","day":"30","type":"conference","language":[{"iso":"eng"}],"date_published":"2025-07-30T00:00:00Z","has_accepted_license":"1","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"publication_identifier":{"eissn":["2640-3498"]},"acknowledged_ssus":[{"_id":"ScienComp"}],"citation":{"short":"S.A. Maddipatla, N.E. Sellam, M.I. Bojan, S. Vedula, P. Schanda, A. Marx, A.M. Bronstein, in:, Proceedings of the 42nd International Conference on Machine Learning, ML Research Press, 2025, pp. 42366–42393.","ama":"Maddipatla SA, Sellam NE, Bojan MI, et al. Inverse problems with experiment-guided AlphaFold. In: <i>Proceedings of the 42nd International Conference on Machine Learning</i>. Vol 267. ML Research Press; 2025:42366-42393.","chicago":"Maddipatla, Sai A, Nadav E Sellam, Meital I Bojan, Sanketh Vedula, Paul Schanda, Ailie Marx, and Alex M. Bronstein. “Inverse Problems with Experiment-Guided AlphaFold.” In <i>Proceedings of the 42nd International Conference on Machine Learning</i>, 267:42366–93. ML Research Press, 2025.","mla":"Maddipatla, Sai A., et al. “Inverse Problems with Experiment-Guided AlphaFold.” <i>Proceedings of the 42nd International Conference on Machine Learning</i>, vol. 267, ML Research Press, 2025, pp. 42366–93.","ista":"Maddipatla SA, Sellam NE, Bojan MI, Vedula S, Schanda P, Marx A, Bronstein AM. 2025. Inverse problems with experiment-guided AlphaFold. Proceedings of the 42nd International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 267, 42366–42393.","apa":"Maddipatla, S. A., Sellam, N. E., Bojan, M. I., Vedula, S., Schanda, P., Marx, A., &#38; Bronstein, A. M. (2025). Inverse problems with experiment-guided AlphaFold. In <i>Proceedings of the 42nd International Conference on Machine Learning</i> (Vol. 267, pp. 42366–42393). Vancouver, Canada: ML Research Press.","ieee":"S. A. Maddipatla <i>et al.</i>, “Inverse problems with experiment-guided AlphaFold,” in <i>Proceedings of the 42nd International Conference on Machine Learning</i>, Vancouver, Canada, 2025, vol. 267, pp. 42366–42393."},"file_date_updated":"2026-02-19T08:56:10Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","OA_type":"gold","conference":{"name":"ICML: International Conference on Machine Learning","end_date":"2025-07-19","start_date":"2025-07-13","location":"Vancouver, Canada"},"quality_controlled":"1","file":[{"file_name":"2025_ICML_Maddipatla.pdf","access_level":"open_access","date_updated":"2026-02-19T08:56:10Z","relation":"main_file","content_type":"application/pdf","date_created":"2026-02-19T08:56:10Z","file_size":1924177,"success":1,"file_id":"21338","checksum":"f33230a6d59b7978d4cd72795e4e9059","creator":"dernst"}],"intvolume":"       267","_id":"21327","abstract":[{"text":"Proteins exist as a dynamic ensemble of multiple conformations, and these motions are often crucial for their functions. However, current structure prediction methods predominantly yield a single conformation, overlooking the conformational heterogeneity revealed by diverse experimental modalities. Here, we present a framework for building experiment-grounded protein structure generative models that infer conformational ensembles consistent with measured experimental data. The key idea is to treat stateof-the-art protein structure predictors (e.g., AlphaFold3) as sequence-conditioned structural priors, and cast ensemble modeling as posterior inference of protein structures given experimental measurements. Through extensive real-data experiments, we demonstrate the generality of our method to incorporate a variety of experimental measurements. In particular, our framework uncovers previously unmodeled conformational heterogeneity from crystallographic densities, and generates high-accuracy NMR ensembles orders of magnitude faster than the status quo. Notably, we demonstrate that our ensembles outperform AlphaFold3 (Abramson et al., 2024) and sometimes better fit experimental data than publicly deposited structures to the Protein Data Bank (PDB, Burley et al. (2017)). We believe that this approach will unlock building predictive models that fully embrace experimentally observed conformational diversity.","lang":"eng"}],"month":"07","volume":267,"publication":"Proceedings of the 42nd International Conference on Machine Learning","department":[{"_id":"PaSc"},{"_id":"AlBr"},{"_id":"GradSch"}],"alternative_title":["PMLR"],"date_updated":"2026-02-19T08:56:43Z","external_id":{"arxiv":["2502.09372"]},"corr_author":"1","oa":1}]