21 citations · 59 across the 7 of their papers we have counts for
8 papers
Machine learning approaches for interpretable antibody property prediction using structural data
Kevin Michalewicz, Mauricio Barahona, Barbara Bravi
Understanding the relationship between antibody sequence, structure and function is essential for the design of antibody-based therapeutics and research tools. Recently, machine le…
Protein generation with embedding learning for motif diversification
Kevin Michalewicz, Chen Jin, Philip Alexander Teare +4
A fundamental challenge in protein design is the trade-off between generating structural diversity while preserving motif biological function. Current state-of-the-art methods, suc…
Integrating protein sequence embeddings with structure via graph-based deep learning for single-residue property prediction
Kevin Michalewicz, Mauricio Barahona, Barbara Bravi
Understanding the intertwined contributions of amino acid sequence and spatial structure is essential to explain protein behaviour. Here, we introduce INFUSSE (Integrated Network F…
ANTIPASTI: interpretable prediction of antibody binding affinity exploiting Normal Modes and Deep Learning
Kevin Michalewicz, Mauricio Barahona, Barbara Bravi
The high binding affinity of antibodies towards their cognate targets is key to eliciting effective immune responses, as well as to the use of antibodies as research and therapeuti…
The MAPS Algorithm: Fast model-agnostic and distribution-free prediction intervals for supervised learning
Daniel Salnikov, Dan Leonte, Kevin Michalewicz
A fundamental problem in modern supervised learning is computing reliable conditional prediction intervals in high-dimensional settings: existing methods often rely on restrictive…
Image deconvolution and PSF reconstruction with STARRED: a wavelet-based two-channel method optimized for light-curve extraction
Martin Millon, Kevin Michalewicz, Frédéric Dux +2
We present STARRED, a Point Spread Function (PSF) reconstruction, two-channel deconvolution, and light curve extraction method designed for high-precision photometric measurements…
STARRED: a two-channel deconvolution method with Starlet regularization
Kevin Michalewicz, Martin Millon, Frédéric Dux +1
The spatial resolution of astronomical images is limited by atmospheric turbulence and diffraction in the telescope optics, resulting in blurred images. This makes it difficult to…
Deep Learning-based galaxy image deconvolution
Utsav Akhaury, Jean-Luc Starck, Pascale Jablonka +2
With the onset of large-scale astronomical surveys capturing millions of images, there is an increasing need to develop fast and accurate deconvolution algorithms that generalize w…