Thierry Mora

This paper introduces CloneSearch, a method to identify expanding and contracting T cell clones from longitudinal TCR sequencing without requiring antigen-specific information.

Paraplume predicts paratopes from antibody sequences using protein language models, improving downstream tasks and revealing evolutionary dynamics in antibody repertoires.

This study compares three simulation schemes for modeling protein evolution, finding that population genetics dynamics best capture phylogenetic correlations and selective sweeps without requiring additional data.

This study shows noradrenaline prolongs motor arrest through cerebrospinal fluid signaling via Adra1a receptors on ependymal radial glia.

The paper infers memory B cell and plasmablast dynamics using statistical methods, finding slow clone degradation and high plasmablast differentiation rates.

The paper quantifies information transduced through the ERK pathway in single cells and populations, showing single cells can resolve graded stimuli with over 2 bits of information.

The paper identifies collective variables in retinal ganglion cells that best predict future neural activity using an information bottleneck method.

The paper identifies a new broadly neutralizing antibody, 04_A06, with high potency and breadth against HIV-1 strains, showing potential for prevention and therapy.

The paper introduces CBA, a probabilistic method to infer genetic and growth rates from cell populations, using a backbone tree and monogram to model evolutionary processes.

This study uses spectral methods to infer resource competition in microbial communities from time series data, showing they outperform simple correlations.

The paper shows that strong noise correlations improve population coding in neural populations, contradicting the common belief that they should be negative.

TULIP is an unsupervised transformer model for predicting TCR epitope binding, outperforming existing methods on unseen epitopes by addressing data limitations and bias.

This paper introduces diffRBM, a transfer-learning model using Restricted Boltzmann Machines to predict antigen immunogenicity and T-cell receptor specificity, achieving performance comparable to existing methods.

The paper develops a small-correlation expansion to compute stimulus information in large neural populations, validated on synthetic data and applied to vertebrate retinal recordings.

This paper improves Granger causality analysis for calcium imaging data to better infer information flow in neuronal networks.

The paper benchmarks 23 TCR-epitope prediction models, finding that using paired-chain alpha-beta and CDR1/2/V/J data improves performance, and simple distance-based methods can be competitive.

This study investigates how retinal ganglion cells respond to natural scenes, finding that their ON-OFF selectivity varies with the image and is due to non-linear retinal circuit pathways.

This paper develops a probabilistic machine learning model to identify antigen-specific TCRs using sequence data and clone abundance, enabling personalized discrimination of specific versus non-specific T-cell responses.

This study demonstrates that immune repertoires can uniquely identify individuals with high accuracy using a classifier based on repertoire similarity.

This paper introduces a flexible machine learning method for predicting antigen presentation by HLA-I alleles and shows its improved accuracy for rare alleles.

This paper introduces a two-step inference strategy for training generalized linear models of noisy interacting neurons, showing improved performance and stability across complex stimuli.

OLGA calculates amino acid sequence generation probabilities for B- and T-cell receptors using dynamic programming, showing agreement with published data and potential for vaccine design.

The paper investigates how active degradation of the MarA regulator affects coordination of downstream genes in E. coli, finding trade-offs between coordination speed and maximum coordination.

This paper introduces a method to separate intrinsic neuron interactions from extrinsic correlations in sensory neural networks using mean-field corrections, validated on retinal recordings.

The paper introduces a simple model that corrects Poisson statistics to better predict low variability in neural spike trains, showing improved accuracy in retinal recordings.

This paper introduces a method to learn a neural metric from retinal ganglion cell activity to accurately discriminate visual stimuli based on spike trains.

The paper maps the epistatic landscape of antibody binding affinity using a null model and Tite-Seq data, showing that epistasis enhances the mutational accessibility of high-affinity antibodies.

The paper develops a method to estimate retinal network sensitivity to stimulus perturbations and finds a peak in sensitivity at specific frequencies, suggesting optimal information transmission.

The paper proposes a theory of olfactory circuit architecture in Drosophila that uses disorder to process complex odors, tested with empirical data.

The paper compares maximum entropy and random models for describing retinal neuron spiking activity, finding maximum entropy provides better approximations, especially in larger populations.