Publications related to Deep Learning-Assisted Single-Molecule Detection of Protein Post-translational Modifications with a Biological Nanopore

Related publications (27)

Learning the language of the genome: AI- and data-driven decoding of genetic variants and disease mechanisms

Genetic variation plays a crucial role in human disease susceptibility and molecular pathophysiology. This thesis explores genetic determinants of disease using advanced computational approaches, including statistical modeling, network analysis, and deep l ...

EPFL2025

Musculoskeletal motor control with reinforcement learning

Alberto Silvio Chiappa

Animals, including humans, interact with the external environment primarily through motion. Replicating their motor control skills in artificial embodied agents is a major objective of artificial intelligence research. This thesis presents a collection of ...

EPFL2025

Allosteric Modulation of GCase Enhances Lysosomal Activity and Reduces ER Stress in GCase-Related Disorders

Mikhail Rudinskiy, Maurizio Molinari

Variants in the GBA1 gene, encoding the lysosomal enzyme glucosylceramidase beta 1 (GCase), are linked to Parkinson’s disease (PD) and Gaucher disease (GD). Heterozygous variants increase PD risk, while homozygous variants lead to GD, a lysosomal storage d ...

MDPI AG2025

Intelligent Soft Robotic System for Sensing and Recognition via Triboelectric‐Based Multi‐Sensor Fusion

Xin Chen

Triboelectric nanogenerators (TENGs) serve as a key technology in flexible electronic devices, especially tactile sensors, enabling flexible robotic manipulators to effectively interact with their external environment. Persistent challenges include inadequ ...

Wiley2025

Transferability of Learnt Speech Representations for Decoding Non-Human Vocal Communication

Eklavya Sarkar

Humans and animals both use acoustic signals for vocal communication. The advent of self-supervised learning (SSL) has enabled neural networks to learn robust and general feature representations through the intrinsic acoustic structure of input signals, wi ...

EPFL2025

MARBLE: interpretable representations of neural population dynamics using geometric deep learning

Pierre Vandergheynst

The dynamics of neuron populations commonly evolve on low-dimensional manifolds. Thus, we need methods that learn the dynamical processes over neural manifolds to infer interpretable and consistent latent representations. We introduce a representation lear ...

Nature Publishing Group2025

Foundation neural-networks quantum states as a unified Ansatz for multiple hamiltonians

Luciano Loris Viteritti, Giuseppe Carleo

Foundation models are highly versatile neural-network architectures capable of processing different data types, such as text and images, and generalizing across various tasks like classification and generation. Inspired by this success, we propose Foundati ...

2025

Detecting mass wasting of Retrogressive Thaw Slumps in spaceborne elevation models using deep learning

Sophia Kublytska

Climate change has led to stronger warming in the Arctic, causing higher ground temperatures and extensive permafrost thaw. Retrogressive Thaw Slumps (RTSs) represent one of the most rapid and considerable geomorphological changes in permafrost regions, oc ...

2025

Teaching oxidation states to neural networks

Nicola Marzari

While the accurate description of redox reactions remains a challenge for first-principles calculations, it has been shown that extended Hubbard functionals (DFT+U+V) can provide a reliable approach, mitigating self-interaction errors, in materials with st ...

2025

TimeGPT in load forecasting: A large time series model perspective

Fernando Porté Agel, Jiannong Fang, Wenlong Liao

Machine learning models have made significant progress in load forecasting, but their forecast accuracy is limited in cases where historical load data is scarce. Inspired by the outstanding performance of large language models (LLMs) in computer vision and ...

2025

Differential Effects of Post-translational Modifications on the Membrane Interaction of Huntingtin Protein

Hilal Lashuel, Matteo Dal Peraro, Luciano Andres Abriata, Charlotte Julie Caroline Gehin, Zhidian Zhang

Huntington's disease is a neurodegenerative disorder caused by an expanded polyglutamine stretch near the N-terminus of the huntingtin (HTT) protein, rendering the protein more prone to aggregate. The first 17 residues in HTT (Nt17) interact with lipid mem ...

Amer Chemical Soc2024

Anatomy-Informed Multimodal Learning for Myocardial Infarction Prediction

Pascal Frossard, Emmanuel Abbé, Ntorina Thanou, Ortal Yona Senouf, Ivan-Daniel Sievering

Goal: In patients with coronary artery disease, the prediction of future cardiac events such as myocardial infarction (MI) remains a major challenge. In this work, we propose a novel anatomy-informed multimodal deep learning framework to predict future MI ...

2024

The influence of data on adversarial robustness

Javier Alejandro Maroto Morales

Deep learning models are generally vulnerable to small perturbations, raising concerns about safety, security, and performance guarantees. Achieving robustness has been shown to be costly in terms of computational resources and quantity of data. This thesi ...

EPFL2024

Topics in statistical physics of high-dimensional machine learning

Hugo Chao Cui

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

EPFL2024

Fusing physics-based and deep learning models for prognostics

Olga Fink

Physics-based and data-driven models for remaining useful lifetime (RUL) prediction typically suffer from two major challenges that limit their applicability to complex real-world domains: (1) the incompleteness of physics-based models and (2) the limited ...

Elsevier2023

Treating Noise and Anomalies in Vehicle Trajectories From an Experiment With a Swarm of Drones

Nikolaos Geroliminis, Emmanouil Barmpounakis

Unmanned aerial systems, known as "drones", are relatively new in collecting traffic data. Data from drone videography can have potential applications for traffic research. Drones can record the vehicles from their aerial point-of-view and provide their na ...

IEEE Institute of Electrical and Electronics Engineers2023

Stable parameterization of continuous and piecewise-linear functions

Michaël Unser, Alexis Marie Frederic Goujon, Joaquim Gonçalves Garcia Barreto Campos

Rectified-linear-unit (ReLU) neural networks, which play a prominent role in deep learning, generate continuous and piecewise-linear (CPWL) functions. While they provide a powerful parametric representation, the mapping between the parameter and function s ...

2023

Learnable Wavelet Transform and Domain Adversarial Learning for Enhanced Bearing Fault Diagnosis

Olga Fink, Gaëtan Michel Frusque, Qi Li

The application of unsupervised domain adaptation (UDA)-based fault diagnosis methods has shown significant efficacy in industrial settings, facilitating the transfer of operational experience and fault signatures between different operating conditions, di ...

Research Publishing2023

Bridging structural MRI with cognitive function for individual level classification of early psychosis via deep learning

Lijing Xin

IntroductionRecent efforts have been made to apply machine learning and deep learning approaches to the automated classification of schizophrenia using structural magnetic resonance imaging (sMRI) at the individual level. However, these approaches are less ...

FRONTIERS MEDIA SA2023

Novel trehalose-based excipients for stabilizing nebulized anti-SARS-CoV-2 antibody

Sandrine Gerber, Solène Marcelle Françoise Marie Passemard, François Rémi Pierre Noverraz

COVID-19 is caused by the infection of the lungs by SARS-CoV-2. Monoclonal antibodies, such as sotrovimab, showed great efficiency in neutralizing the virus before its internalization by lung epithelial cells. However, parenteral routes are still the prefe ...

2023