no code implementations • 13 Dec 2023 • Leonard Wossnig, Norbert Furtmann, Andrew Buchanan, Sandeep Kumar, Victor Greiff
Over the past few years, we have observed rapid developments in the field of ML-guided antibody discovery and development (D&D).
no code implementations • 26 Sep 2022 • Mai Ha Vu, Philippe A. Robert, Rahmad Akbar, Bartlomiej Swiatczak, Geir Kjetil Sandve, Dag Trygve Truslew Haug, Victor Greiff
It would facilitate a better understanding of how differences and similarities between natural language and biological sequences influence the quality of LMs, which is crucial for the design of interpretable models with extractable sequence-functions relationship rules, such as the ones underlying the antibody specificity prediction problem.
no code implementations • 3 Jul 2022 • Mai Ha Vu, Rahmad Akbar, Philippe A. Robert, Bartlomiej Swiatczak, Victor Greiff, Geir Kjetil Sandve, Dag Trygve Truslew Haug
Differences between protein sequence data and linguistic sequence data require the integration of more domain-specific knowledge in protein LMs compared to natural language LMs.
1 code implementation • 20 Apr 2022 • Milena Pavlović, Ghadi S. Al Hajj, Chakravarthi Kanduri, Johan Pensar, Mollie Wood, Ludvig M. Sollid, Victor Greiff, Geir Kjetil Sandve
Machine learning is increasingly used to discover diagnostic and prognostic biomarkers from high-dimensional molecular data.
1 code implementation • 29 Jan 2022 • Asif Khan, Alexander I. Cowen-Rivers, Antoine Grosnit, Derrick-Goh-Xin Deik, Philippe A. Robert, Victor Greiff, Eva Smorodina, Puneet Rawat, Kamil Dreczkowski, Rahmad Akbar, Rasul Tutunov, Dany Bou-Ammar, Jun Wang, Amos Storkey, Haitham Bou-Ammar
software suite as a black-box oracle to score the target specificity and affinity of designed antibodies \textit{in silico} in an unconstrained fashion~\citep{robert2021one}.
no code implementations • 20 Oct 2020 • Kerui Peng, Yana Safonova, Mikhail Shugay, Alice Popejoy, Oscar Rodriguez, Felix Breden, Petter Brodin, Amanda M. Burkhardt, Carlos Bustamante, Van-Mai Cao-Lormeau, Martin M. Corcoran, Darragh Duffy, Macarena Fuentes Guajardo, Ricardo Fujita, Victor Greiff, Vanessa D. Jonsson, Xiao Liu, Lluis Quintana-Murci, Maura Rossetti, Jianming Xie, Gur Yaari, Wei zhang, Malak S. Abedalthagafi, Khalid O. Adekoya, Rahaman A. Ahmed, Wei-Chiao Chang, Clive Gray, Yusuke Nakamura, William D. Lees, Purvesh Khatri, Houda Alachkar, Cathrine Scheepers, Corey T. Watson, Gunilla B. Karlsson Hedestam, Serghei Mangul
With the advent of high-throughput sequencing technologies, the fields of immunogenomics and adaptive immune receptor repertoire research are facing both opportunities and challenges.
1 code implementation • NeurIPS 2020 • Michael Widrich, Bernhard Schäfl, Hubert Ramsauer, Milena Pavlović, Lukas Gruber, Markus Holzleitner, Johannes Brandstetter, Geir Kjetil Sandve, Victor Greiff, Sepp Hochreiter, Günter Klambauer
We show that the attention mechanism of transformer architectures is actually the update rule of modern Hopfield networks that can store exponentially many patterns.
2 code implementations • ICLR 2021 • Hubert Ramsauer, Bernhard Schäfl, Johannes Lehner, Philipp Seidl, Michael Widrich, Thomas Adler, Lukas Gruber, Markus Holzleitner, Milena Pavlović, Geir Kjetil Sandve, Victor Greiff, David Kreil, Michael Kopp, Günter Klambauer, Johannes Brandstetter, Sepp Hochreiter
The new update rule is equivalent to the attention mechanism used in transformers.
Immune Repertoire Classification Multiple Instance Learning +1