no code implementations • 12 Jun 2023 • Bongjin Koo, Julien Martel, Ariana Peck, Axel Levy, Frédéric Poitevin, Nina Miolane
Cryogenic electron microscopy (cryo-EM) has transformed structural biology by allowing to reconstruct 3D biomolecular structures up to near-atomic resolution.
1 code implementation • 13 Oct 2022 • Arsene Fansi Tchango, Rishab Goel, Julien Martel, Zhi Wen, Gaetan Marceau Caron, Joumana Ghosn
In their initial interaction with patients, doctors do not only focus on identifying the pathology a patient is suffering from; they instead generate a differential diagnosis (in the form of a short list of plausible diseases) because the medical evidence collected from patients is often insufficient to establish a final diagnosis.
no code implementations • 13 Oct 2022 • Axel Levy, Gordon Wetzstein, Julien Martel, Frederic Poitevin, Ellen D. Zhong
Cryo-electron microscopy (cryo-EM) is an imaging modality that provides unique insights into the dynamics of proteins and other building blocks of life.
no code implementations • 29 Sep 2022 • Youssef Nashed, Ariana Peck, Julien Martel, Axel Levy, Bongjin Koo, Gordon Wetzstein, Nina Miolane, Daniel Ratner, Frédéric Poitevin
Cryogenic electron microscopy (cryo-EM) provides a unique opportunity to study the structural heterogeneity of biomolecules.
1 code implementation • 18 May 2022 • Arsene Fansi Tchango, Rishab Goel, Zhi Wen, Julien Martel, Joumana Ghosn
In this work, we present a large-scale synthetic dataset of roughly 1. 3 million patients that includes a differential diagnosis, along with the ground truth pathology, symptoms and antecedents for each patient.
1 code implementation • 15 Mar 2022 • Axel Levy, Frédéric Poitevin, Julien Martel, Youssef Nashed, Ariana Peck, Nina Miolane, Daniel Ratner, Mike Dunne, Gordon Wetzstein
We introduce cryoAI, an ab initio reconstruction algorithm for homogeneous conformations that uses direct gradient-based optimization of particle poses and the electron scattering potential from single-particle cryo-EM data.
2 code implementations • ICML 2018 • Lorenz Muller, Julien Martel, Giacomo Indiveri
In this paper we introduce a novel neural network architecture, in which weight matrices are re-parametrized in terms of low-dimensional vectors, interacting through kernel functions.
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