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| 001 | 1052326 | ||
| 005 | 20260127203441.0 | ||
| 037 | _ | _ | |a FZJ-2026-00934 |
| 100 | 1 | _ | |a Bazarova, Alina |0 P:(DE-Juel1)192120 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a Systems Biology Lecture Series |c Berlin |w Germany |
| 245 | _ | _ | |a Multi-modal integration for biological tasks: perks, caveats and applications |f 2026-01-21 - |
| 260 | _ | _ | |c 2026 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a Other |2 DataCite |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a LECTURE_SPEECH |2 ORCID |
| 336 | 7 | _ | |a Talk (non-conference) |b talk |m talk |0 PUB:(DE-HGF)31 |s 1769503847_4497 |2 PUB:(DE-HGF) |x Invited |
| 336 | 7 | _ | |a Other |2 DINI |
| 502 | _ | _ | |c MDC-BIMSB |
| 520 | _ | _ | |a In this talk, I will present OneProt, a versatile artificial intelligence framework for protein analysis that leverages multi-modal integration across structural, sequence, textual, and binding-site data. To align these heterogeneous modalities, OneProt adopts an ImageBind-inspired training strategy, enabling efficient cross-modal representation learning without requiring fully paired data. By combining graph neural networks and transformer-based architectures, OneProt achieves strong performance across tasks such as enzyme function prediction and binding-site analysis. I will highlight two key features of the framework: its ability to seamlessly incorporate custom modalities during pre-training, and a lightweight fine-tuning strategy that relies only on a simple multi-layer perceptron projection. Through empirical results, I will demonstrate how multi-modal integration can reduce the reliance on large task-specific datasets while maintaining competitive downstream performance. Alongside these benefits, I will discuss the practical challenges and caveats of adding new modalities, including alignment noise, modality imbalance, and training stability. Finally, I will present preliminary results from a follow-up project, OneProtGPT, which integrates OneProt with scientific large language models to enable cross-modal retrieval and the integration of protein representations with natural language. |
| 536 | _ | _ | |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5112 |c POF4-511 |f POF IV |x 0 |
| 536 | _ | _ | |a Helmholtz AI Consultant Team FB Information (E54.303.11) |0 G:(DE-Juel-1)E54.303.11 |c E54.303.11 |x 1 |
| 909 | C | O | |o oai:juser.fz-juelich.de:1052326 |p VDB |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)192120 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5112 |x 0 |
| 914 | 1 | _ | |y 2026 |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
| 980 | _ | _ | |a talk |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
| 980 | _ | _ | |a UNRESTRICTED |
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