Jannis Born

According to our database, Jannis Born authored at least 28 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of four.

2024

Regress, Don't Guess - A Regression-like Loss on Number Tokens for Language Models.

Jonas Zausinger, Lars Pennig, Kacper Chlodny, Vincent Limbach, Anna Ketteler, Thorben Prein, Vishwa Mohan Singh, Michael Morris Danziger, Jannis Born

CoRR, 2024

Quantum Theory and Application of Contextual Optimal Transport.

Nicola Mariella, Albert Akhriev, Francesco Tacchino, Christa Zoufal, Juan Carlos Gonzalez-Espitia, Benedek Harsanyi, Eugene Koskin, Ivano Tavernelli, Stefan Woerner, Marianna Rapsomaniki, Sergiy Zhuk, Jannis Born

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Regression Transformer enables concurrent sequence regression and generation for molecular language modelling.

Jannis Born, Matteo Manica

Nat. Mac. Intell., April, 2023

Language models in molecular discovery.

Nikita Janakarajan, Tim Erdmann, Sarath Swaminathan, Teodoro Laino, Jannis Born

CoRR, 2023

Domain-agnostic and Multi-level Evaluation of Generative Models.

Girmaw Abebe Tadesse, Jannis Born, Celia Cintas, William Ogallo, Dmitry Zubarev, Matteo Manica, Komminist Weldemariam

CoRR, 2023

Unifying Molecular and Textual Representations via Multi-task Language Modelling.

Dimitrios Christofidellis, Giorgio Giannone, Jannis Born, Ole Winther, Teodoro Laino, Matteo Manica

Proceedings of the International Conference on Machine Learning, 2023

2022

Accelerating Molecular Discovery with Generative Language Models: A journey through the chemical space.

Jannis Born

PhD thesis, 2022

On the Choice of Active Site Sequences for Kinase-Ligand Affinity Prediction.

Jannis Born, Yoel Shoshan, Tien Huynh, Wendy D. Cornell, Eric J. Martin, Matteo Manica

J. Chem. Inf. Model., 2022

Active Site Sequence Representations of Human Kinases Outperform Full Sequence Representations for Affinity Prediction and Inhibitor Generation: 3D Effects in a 1D Model.

Jannis Born, Tien Huynh, Astrid Stroobants, Wendy D. Cornell, Matteo Manica

J. Chem. Inf. Model., 2022

GT4SD: Generative Toolkit for Scientific Discovery.

Matteo Manica, Joris Cadow, Dimitrios Christofidellis, Ashish Dave, Jannis Born, Dean Clarke, Yves Gaetan Nana Teukam, Samuel C. Hoffman, Matthew Buchan, Vijil Chenthamarakshan, Timothy Donovan, Hsiang-Han Hsu, Federico Zipoli, Oliver Schilter, Giorgio Giannone, Akihiro Kishimoto, Lisa Hamada, Inkit Padhi, Karl Wehden, Lauren McHugh, Alexy Khrabrov, Payel Das, Seiji Takeda, John R. Smith

CoRR, 2022

Regression Transformer: Concurrent Conditional Generation and Regression by Blending Numerical and Textual Tokens.

Jannis Born, Matteo Manica

CoRR, 2022

A computational investigation of inventive spelling and the "Lesen durch Schreiben" method.

Jannis Born, Nikola I. Nikolov, Anna Rosenkranz, Alfred Schabmann, Barbara Maria Schmidt

Comput. Educ. Artif. Intell., 2022

A Fully Differentiable Set Autoencoder.

Nikita Janakarajan, Jannis Born, Matteo Manica

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

2021

On the role of artificial intelligence in medical imaging of COVID-19.

Jannis Born, David Beymer, Deepta Rajan, Adam Coy, Vandana V. Mukherjee, Matteo Manica, Prasanth Prasanna, Deddeh Ballah, Michal Guindy, Dorith Shaham, Pallav L. Shah, Emmanouil Karteris, Jan L. Robertus, Maria Gabrani, Michal Rosen-Zvi

Patterns, 2021

Data-driven molecular design for discovery and synthesis of novel ligands: a case study on SARS-CoV-2.

Jannis Born, Matteo Manica, Joris Cadow, Greta Markert, Nil Adell Mill, Modestas Filipavicius, Nikita Janakarajan, Antonio Cardinale, Teodoro Laino, María Rodríguez Martínez

Mach. Learn. Sci. Technol., 2021

TITAN: T-cell receptor specificity prediction with bimodal attention networks.

Anna Weber, Jannis Born, María Rodríguez Martínez

Bioinform., 2021

Lessons Learned from the Development and Application of Medical Imaging-Based AI Technologies for Combating COVID-19: Why Discuss, What Next.

Maria Gabrani, Ender Konukoglu, David Beymer, Gustavo Carneiro, Jannis Born, Michal Guindy, Michal Rosen-Zvi

Proceedings of the Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning, 2021

2020

PaccMann: a web service for interpretable anticancer compound sensitivity prediction.

Joris Cadow, Jannis Born, Matteo Manica, Ali Oskooei, María Rodríguez Martínez

Nucleic Acids Res., 2020

Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound Image Analysis.

Jannis Born, Nina Wiedemann, Gabriel Brändle, Charlotte Buhre, Bastian Rieck, Karsten M. Borgwardt

CoRR, 2020

PaccMann^RL on SARS-CoV-2: Designing antiviral candidates with conditional generative models.

Jannis Born, Matteo Manica, Joris Cadow, Greta Markert, Nil Adell Mill, Modestas Filipavicius, María Rodríguez Martínez

CoRR, 2020

POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound Imaging Dataset (POCUS).

Jannis Born, Gabriel Brändle, Manuel Cossio, Marion Disdier, Julie Goulet, Jérémie Roulin, Nina Wiedemann

CoRR, 2020

COVID-19 Control by Computer Vision Approaches: A Survey.

Anwaar Ulhaq, Jannis Born, Asim Khan, Douglas P. S. Gomes, Subrata Chakraborty, Manoranjan Paul

IEEE Access, 2020

PaccMann^RL: Designing Anticancer Drugs From Transcriptomic Data via Reinforcement Learning.

Jannis Born, Matteo Manica, Ali Oskooei, Joris Cadow, María Rodríguez Martínez

Proceedings of the Research in Computational Molecular Biology, 2020

CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models.

Vijil Chenthamarakshan, Payel Das, Samuel C. Hoffman, Hendrik Strobelt, Inkit Padhi, Kar Wai Lim, Benjamin Hoover, Matteo Manica, Jannis Born, Teodoro Laino, Aleksandra Mojsilovic

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

AI Enables Explainable Drug Sensitivity Screenings.

Matteo Manica, Ali Oskooei, Jannis Born

ERCIM News, 2019

Reinforcement learning-driven de-novo design of anticancer compounds conditioned on biomolecular profiles.

Jannis Born, Matteo Manica, Ali Oskooei, María Rodríguez Martínez

CoRR, 2019

Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders.

Matteo Manica, Ali Oskooei, Jannis Born, Vigneshwari Subramanian, Julio Sáez-Rodríguez, María Rodríguez Martínez

CoRR, 2019

2018

PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks.

Ali Oskooei, Jannis Born, Matteo Manica, Vigneshwari Subramanian, Julio Sáez-Rodríguez, María Rodríguez Martínez

CoRR, 2018