Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Lupo, Umberto"'
Identifying interacting partners from two sets of protein sequences has important applications in computational biology. Interacting partners share similarities across species due to their common evolutionary history, and feature correlations in amin
Externí odkaz:
http://arxiv.org/abs/2409.16142
Predicting which proteins interact together from amino-acid sequences is an important task. We develop a method to pair interacting protein sequences which leverages the power of protein language models trained on multiple sequence alignments, such a
Externí odkaz:
http://arxiv.org/abs/2308.07136
Local and global inference methods have been developed to infer structural contacts from multiple sequence alignments of homologous proteins. They rely on correlations in amino-acid usage at contacting sites. Because homologous proteins share a commo
Externí odkaz:
http://arxiv.org/abs/2209.13045
Autor:
Myers, Adele, Utpala, Saiteja, Talbar, Shubham, Sanborn, Sophia, Shewmake, Christian, Donnat, Claire, Mathe, Johan, Lupo, Umberto, Sonthalia, Rishi, Cui, Xinyue, Szwagier, Tom, Pignet, Arthur, Bergsson, Andri, Hauberg, Soren, Nielsen, Dmitriy, Sommer, Stefan, Klindt, David, Hermansen, Erik, Vaupel, Melvin, Dunn, Benjamin, Xiong, Jeffrey, Aharony, Noga, Pe'er, Itsik, Ambellan, Felix, Hanik, Martin, Nava-Yazdani, Esfandiar, von Tycowicz, Christoph, Miolane, Nina
This paper presents the computational challenge on differential geometry and topology that was hosted within the ICLR 2022 workshop ``Geometric and Topological Representation Learning". The competition asked participants to provide implementations of
Externí odkaz:
http://arxiv.org/abs/2206.09048
Computational models starting from large ensembles of evolutionarily related protein sequences capture a representation of protein families and learn constraints associated to protein structure and function. They thus open the possibility for generat
Externí odkaz:
http://arxiv.org/abs/2204.07110
Publikováno v:
Nat Commun 13, 6298 (2022)
Self-supervised neural language models with attention have recently been applied to biological sequence data, advancing structure, function and mutational effect prediction. Some protein language models, including MSA Transformer and AlphaFold's EvoF
Externí odkaz:
http://arxiv.org/abs/2203.15465
Autor:
Miolane, Nina, Caorsi, Matteo, Lupo, Umberto, Guerard, Marius, Guigui, Nicolas, Mathe, Johan, Cabanes, Yann, Reise, Wojciech, Davies, Thomas, Leitão, António, Mohapatra, Somesh, Utpala, Saiteja, Shailja, Shailja, Corso, Gabriele, Liu, Guoxi, Iuricich, Federico, Manolache, Andrei, Nistor, Mihaela, Bejan, Matei, Nicolicioiu, Armand Mihai, Luchian, Bogdan-Alexandru, Stupariu, Mihai-Sorin, Michel, Florent, Duc, Khanh Dao, Abdulrahman, Bilal, Beketov, Maxim, Maignant, Elodie, Liu, Zhiyuan, Černý, Marek, Bauw, Martin, Velasco-Forero, Santiago, Angulo, Jesus, Long, Yanan
This paper presents the computational challenge on differential geometry and topology that happened within the ICLR 2021 workshop "Geometric and Topological Representation Learning". The competition asked participants to provide creative contribution
Externí odkaz:
http://arxiv.org/abs/2108.09810
We introduce giotto-ph, a high-performance, open-source software package for the computation of Vietoris-Rips barcodes. giotto-ph is based on Morozov and Nigmetov's lockfree (multicore) implementation of Ulrich Bauer's Ripser package. It also contain
Externí odkaz:
http://arxiv.org/abs/2107.05412
Autor:
Tauzin, Guillaume, Lupo, Umberto, Tunstall, Lewis, Pérez, Julian Burella, Caorsi, Matteo, Reise, Wojciech, Medina-Mardones, Anibal, Dassatti, Alberto, Hess, Kathryn
Publikováno v:
NeurIPS 2020 workshop "Topological Data Analysis and beyond" (https://openreview.net/forum?id=fjQtZJOCTXf); JMLR 22 (https://www.jmlr.org/papers/v22/20-325.html)
We introduce giotto-tda, a Python library that integrates high-performance topological data analysis with machine learning via a scikit-learn-compatible API and state-of-the-art C++ implementations. The library's ability to handle various types of da
Externí odkaz:
http://arxiv.org/abs/2004.02551
It has long been envisioned that the strength of the barcode invariant of filtered cellular complexes could be increased using cohomology operations. Leveraging recent advances in the computation of Steenrod squares, we introduce a new family of comp
Externí odkaz:
http://arxiv.org/abs/1812.05031