7 results for "Vincent Fortuin"

MGP-AttTCN: An interpretable machine learning model for the prediction of sepsis.

Margherita Rosnati; Vincent Fortuin

PLoS ONE, Vol. 16 No. 5 · 2021 · English · DOAJ Scopus

With a mortality rate of 5.4 million lives worldwide every year and a healthcare cost of more than 16 billion dollars in the USA alone, sepsis is one of the leading causes of hospital mortality and an increasing concern in the ageing western world. Recently, medical and technological advances have…

Also available at: PLOS

Sparse Gaussian Processes on Discrete Domains

Vincent Fortuin; Gideon Dresdner; Heiko Strathmann et al.

IEEE Access, Vol. 9 · 2021 · English · DOAJ Scopus

Kernel methods on discrete domains have shown great promise for many challenging data types, for instance, biological sequence data and molecular structure data. Scalable kernel methods like Support Vector Machines may offer good predictive performances but do not intrinsically provide uncertainty…

Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations on single cell data.

Andreas Kopf; Vincent Fortuin; Vignesh Ram Somnath et al.

PLoS Computational Biology, Vol. 17 No. 6 · 2021 · English · DOAJ Scopus

Clustering high-dimensional data, such as images or biological measurements, is a long-standing problem and has been studied extensively. Recently, Deep Clustering has gained popularity due to its flexibility in fitting the specific peculiarities of complex data. Here we introduce the…

Also available at: PLOS

OneProt: Towards multi-modal protein foundation models via latent space alignment of sequence, structure, binding sites and text encoders

Klemens Flöge; Srisruthi Udayakumar; Johanna Sommer et al.

PLOS Computational Biology, Vol. 21 No. 11 · 2025 · English · PLOS Scopus

Recent advances in Artificial Intelligence have enabled multi-modal systems to model and translate diverse information spaces. Extending beyond text and vision, we introduce OneProt, a multi-modal Deep Learning model for proteins that integrates structural, sequence, text, and binding site data…

Also available at: DOAJ