Publications related to Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning

Related publications (22)

Integrating AI across the Chemistry Discovery Cycle: Advancing Sustainable Chemistry through Digital Methods

The chemical industry faces mounting pressure to develop more sustainable processes while accelerating innovation to address climate challenges. Traditional discovery approaches in chemistry follow an iterative cycle: forming hypotheses about new molecules ...

EPFL2025

Antibody-based delivery of interleukin-2 modulates the immunosuppressive tumor microenvironment and achieves cure in pancreatic ductal adenocarcinoma syngeneic mice

Dario Neri

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive and deadly type of cancer, with an extremely low five-year overall survival rate. To date, current treatment options primarily involve various chemotherapies, which often prove ineffecti ...

Springer Science and Business Media LLC2025

Molecular characterization of an adhesion GPCR signal transduction using Graph-neural networks

Matthieu François Marfoglia

Key cellular functions depend on the transduction of extracellular mechanical signals by specialized membrane receptors, including adhesion G-protein coupled receptors (aGPCRs). While recent structural studies support aGPCR activation through shedding of t ...

EPFL2025

Towards Unifying Evaluation of Counterfactual Explanations: Leveraging Large Language Models for Human-Centric Assessments

Rasmus Moorits Veski

As machine learning models evolve, maintaining transparency demands more human-centric explainable AI techniques. Counterfactual explanations, with roots in human reasoning, identify the minimal input changes needed to obtain a given output, and hence, are ...

Association for the Advancement of Artificial Intelligence2025

Sequential Representation Learning via Static-Dynamic Conditional Disentanglement

Pascal Frossard

This paper explores self-supervised disentangled representation learning within sequential data, focusing on separating time-indep- endent and time-varying factors in videos. We propose a new model that breaks the usual independence assumption between thos ...

Springer Science and Business Media Deutschland GmbH2025

Machine learning-aided generative molecular design

Philippe Schwaller, Jeff Guo

Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

Nature Portfolio2024

AMCG: a graph dual atomic-molecular conditional molecular generator

Andrea Cavalli

Drug design is both a time consuming and expensive endeavour. Computational strategies offer viable options to address this task; deep learning approaches in particular are indeed gaining traction for their capability of dealing with chemical structures. A ...

IOP Publishing2024

Image sensitive spectral response of semiconductor random network lasers

Kirsten Emilie Moselund

We experimentally study the spectral lasing response of on-chip InP network random lasers under illumination of different input image shapes. Deep-learning models have become increasingly omipresent throughout society. However, they are blighted by exponen ...

SPIE-INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING2024

High-dimensional non-convex landscapes and gradient descent dynamics

Davide Ghio

In these lecture notes we present different methods and concepts developed in statistical physics to analyze gradient descent dynamics in high-dimensional non-convex landscapes. Our aim is to show how approaches developed in physics, mainly statistical phy ...

2024

TEE4EHR: Transformer event encoder for better representation learning in electronic health records

David Atienza Alonso, Anisoara Ionescu, Hojjat Karami

Irregular sampling of time series in electronic health records (EHRs) is one of the main challenges for developing machine learning models. Additionally, the pattern of missing values in certain clinical variables is not at random but depends on the decisi ...

2024

Towards improving full-length ribosome density prediction by bridging sequence and graph-based representations

Mohan Vamsi Nallapareddy, Pierre Vandergheynst, Felix Naef, Cédric Gobet, Francesco Craighero

Translation elongation plays an important role in regulating protein concentrations in the cell, and dysregulation of this process has been linked to several human diseases. In this study, we use data from ribo-seq experiments to model ribosome densities, ...

PMLR2024

Supplementary files for Machine learning for histological annotation and quantification of cortical layers

Olivier Burri, Julie Meystre

Creators Meystre Julie Olivier Burri Contributors Jean Jacquemier Description This dataset contains 7 QuPath projects. The raw data images linked to these projects and located in other Zenodo datasets need to be downloaded as well. The raw data contains im ...

Zenodo2024

Floorplan Isovist Dataset

Mikhael Johanes

This dataset was created as part of the PhD project Machine Understanding of Architectural Space: From Analytical to Generative Applications at EPFL Media and Design Lab (LDM). It was derived from the Cubicasa5K dataset. The dataset contains preprocessed d ...

Zenodo2024

An Efficient Machine Learning Model for Lightning Localization via Lightning-Induced Voltages on Transmission Lines

Farhad Rachidi-Haeri, Marcos Rubinstein, Hamidreza Karami

In this paper, three machine learning (ML)-based approaches—XGBoost, Artificial Neural Network (ANN), and Random Forest algorithms—are compared for the localization of lightning through the analysis of lightninginduced voltages on power transmission lines. ...

URSI – International Union of Radio Science2024

Neural mechanisms of learned sociability in Drosophila

Victor Lobato Rios

Sociability refers to an animal's ease of living amongst members of its own species. This trait is essential for ethologically important social interactions like courtship, mating, and child rearing, as well as collective behaviors like group decision-maki ...

EPFL2024

Standardized Production of Anti-Desmoglein 3 Antibody AK23 for Translational Pemphigus Vulgaris Research

Michaël François, Laurence Durrer, Soraya Quinche, Florence Pojer, Kelvin Ka Ching Lau

Antibody-mediated receptor activation is successfully used to develop medical treatments. If the activation induces a pathological response, such antibodies are also excellent tools for defining molecular mechanisms of target receptor malfunction and desig ...

2024

ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar, Thomas Grégoire Stegmüller, Antoine Jérôme Spahr

Progress in digital pathology is hindered by high-resolution images and the prohibitive cost of exhaustive localized annotations. The commonly used paradigm to categorize pathology images is patch-based processing, which often incorporates multiple instanc ...

IEEE COMPUTER SOC2023

Assessing the wear degradation of tungsten in acidic solutions using a lubricated tribocorrosion model

Stefano Mischler, Shoufan Cao

The tribocorrosion property of tungsten plays a great role in the chemical mechanical polishing of tungsten plugs used in integrated circuits. This study applied a lubricated tribocorrosion model to tungsten, verified and calibrated the model using results ...

SAGE PUBLICATIONS LTD2023

Port-Hamiltonian observer for state-feedback control design

Anna Ngoc Minh Trang Vu

This paper extends the authors' previous work on Control by Interconnection - Model Predictive Control (CbI-MPC) design for constrained port-Hamiltonian systems to deal with the unmeasurable system states. The first contribution resides in the CbI-oriented ...

IEEE2023

A multi-objective planning method for multi-energy complementary distributed energy system: Tackling thermal integration and process synergy

François Maréchal, Hangyu Yu

This study proposes a multi-objective optimization methodology for planning multi-energy complementary distributed energy systems considering process synergy and thermal integration. The process integration tech-nique is integrated into the Energy Hub mode ...

ELSEVIER SCI LTD2023