Jannis Born  Jannis Born photo         

contact information

Predoctoral Researcher
Zurich Research Laboratory, Zurich, Switzerland


Professional Associations

Professional Associations:  American Chemical Society (ACS)


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
Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning, pp. 133--140, Springer, 2021

Trends in Deep Learning for Property-driven Drug Design
Jannis Born, Matteo Manica
Current Medicinal Chemistry, Bentham Science Publishers, 2021

Accelerating detection of lung pathologies with explainable ultrasound image analysis
Jannis Born, Nina Wiedemann, Manuel Cossio, Charlotte Buhre, Gabriel Brandle, Konstantin Leidermann, Avinash Aujayeb, Michael Moor, Bastian Alexander Rieck, Karsten Borgwardt
Applied Sciences 11(2), MDPI AG, 2021

On the Importance of Looking at the Manifold
Nil Adell Mill, Jannis Born, Nathaniel Park, James Hedrick, Maria Rodriguez Martinez, Matteo Manica
Preprint, 2021

TITAN: T Cell Receptor Specificity Prediction with Bimodal Attention Networks
Anna Weber, Jannis Born, Maria Rodriguez Martinez
Bioinformatics, 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, Antonio Cardinale, Teodoro Laino, Maria Rodriguez Martinez
Machine Learning: Science and Technology 2(2), IOP Publishing, 2021

PaccMannRL: De novo generation of hit-like anticancer molecules from transcriptomic data via reinforcement learning
Jannis Born, Matteo Manica, Ali Oskooei, Joris Cadow, Greta Markert, Greta Markert, Maria Rodriguez Martinez
iScience 24(4), 102269-102269, Elsevier, 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, Pallav L Shah, Emmanouil Karteris, Jan Lukas Robertus, Maria Gabrani, Michal Rosen-Zvi
Patterns, 2021


COVID-19 Control by Computer Vision Approaches: A Survey
Anwaar Ulhaq, Jannis Born, Asim Khan, Douglas Pinto Sampaio Gomes, Subrata Chakraborty, Manoranjan Paul
IEEE Access8, 179437-179456, IEEE, Institute of Electrical and Electronics Engineers, 2020

PaccMann: a web service for interpretable anticancer compound sensitivity prediction
Joris Cadow, Jannis Born, Matteo Manica, Ali Oskooei, Maria Rodriguez Martinez
Nucleic Acids Research, 2020

PaccMannRL: Designing Anticancer Drugs From Transcriptomic Data via Reinforcement Learning
Jannis Born, Matteo Manica, Ali Oskooei, Joris Cadow, Maria Rodriguez Martinez
Research in Computational Molecular Biology, pp. 231--233, Springer International Publishing, 2020

CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models
Vijil Chenthamarakshan, Payel Das, Inkit Padhi, Hendrik Strobelt, Kar Wai Lim, Ben Hoover, Samuel C Hoffman, Matteo Manica, Jannis Born, Teodoro Laino, others
NeurIPS, 2020

POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound Imaging Dataset (POCUS)
Jannis Born, Gabriel Brandle, Manuel Cossio, Marion Disdier, Julie Goulet, Jeremie Roulin, Nina Wiedemann
arXiv preprint arXiv:2004.12084, 2020


Toward Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-Based Convolutional Encoders
Matteo Manica, Ali Oskooei, Jannis Born, Vigneshwari Subramanian, Julio Saez-Rodriguez, Maria Rodriguez-Martinez
Molecular Pharmaceutics, 2019
PMID: 31618586


PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks
Ali Oskooei, Jannis Born, Matteo Manica, Vigneshwari Subramanian, Julio Saez-Rodriguez, Maria Rodriguez Martinez
NeurIPS Workshop on ML for Molecules & Materials, 2018