Simona Cocco

A maximum entropy model for m6A prediction: from human transcripts to influenza A viral segments

The paper introduces MEMAR, a Maximum Entropy model for predicting m6A methylation in human transcripts and influenza A viral segments, showing improved performance over existing methods.

Evolutionary Advantage of Diversity-Generating Retroelements in Switching Environments

The paper quantifies the fitness gain of DGR diversification compared to standard mutagenesis and identifies when DGR activation is evolutionarily favored.

Design and experimental characterization of specificity-switching mutational paths of WW domains

The paper designs mutational paths for WW domains to switch specificity using computational methods and validates them experimentally, showing high success rates for structured paths over random ones.

Optimal Maturation Protocols for High-Affinity Antibody Targets: A Path-Integral Approach

The paper develops a stochastic model for affinity maturation and identifies an optimal antigen-concentration protocol that maximizes the probability of reaching a target antibody affinity.

Evaluating SARS-CoV-2 Antibody Resilience via Prediction and Design of Escape Viral Variants

This paper presents EscapeMap, a framework for predicting and designing SARS-CoV-2 variants that escape antibodies, and identifies antibody combinations less prone to simultaneous escape.

Computational Design of Optimal Sequences for Targeted Hypermutagenesis Using Recombination-Coupled Diversity-Generating Retroelements

The paper develops a method to improve DGRec mutagenesis efficiency by recoding templates and using an LSTM model to predict mutational profiles.

Least Component Analysis reveals ecological constraints in microbial communities

The paper uses least component analysis to identify ecological constraints in microbial communities by analyzing low-variance directions in taxon abundance data.

Constrained Evolutionary Funnels Shape Viral Immune Escape

The paper develops a probabilistic framework to predict viral immune escape trajectories, showing that escape is constrained to specific pathways despite vast mutational space.

Linking Brain and Behavior States in Zebrafish Larvae Locomotion using Hidden Markov Models

This study uses Hidden Markov Models to link neural activity and behavior in zebrafish larvae, revealing state-based correlations between swimming patterns and circuit activation.

Designing molecular RNA switches with Restricted Boltzmann machines

The paper designs RNA switches using Restricted Boltzmann machines and validates their structural switching properties through experimental testing.

Repeats Mimic Pathogen-Associated Patterns Across a Vast Evolutionary Landscape

The paper quantifies viral mimicry in genomes across species and shows how repetitive elements maintain pathogen-like features under selection.

Speed Vascular Patterns in the Spatial Navigation System

This study uses functional ultrasound imaging to show that cerebral blood volume changes in rats correlate with speed and spatial features during navigation, revealing a hemodynamic signature of exploratory behavior.

Restoring data balance via generative models of T cell receptors for antigen-binding prediction

The paper uses generative models to balance T cell receptor data for improved antigen-binding prediction.

Computational design of novel Cas9 PAM-interacting domains using evolution-based modelling and structural quality assessment

The paper designs novel Cas9 PAM-interacting domains using a combination of evolutionary modeling, physics-based scoring, and machine learning, identifying functional variants with up to 20% sequence divergence from the wild-type.

Minimal epistatic networks from integrated sequence and mutational protein data.

The paper introduces a method to identify minimal epistatic networks in proteins using sequence and mutational data, achieving performance comparable to deep networks with greater interpretability.

Learning the differences: a transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity

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.

Emergence of time persistence in an interpretable data-driven neural network model

The paper develops an interpretable neural network model to capture slow dynamics in zebrafish swimming circuits using functional neural recordings.

A synaptic novelty signal to switch hippocampal attractor networks from generalization to discrimination

The paper identifies a synaptic novelty signal in hippocampal circuits that drives a switch from generalization to discrimination during memory formation.

sgDI-tector: defective interfering viral genome bioinformatics for detection of coronavirus subgenomic RNAs

sgDI-tector detects and quantifies subgenomic RNAs in SARS-CoV-2 NGS data, identifying a ranked set of sgRNAs and comparing their levels to defective interfering viral genomes.

Probing T-cell response by sequence-based probabilistic modeling

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.

Improving sequence-based modeling of protein families using secondary structure quality assessment

The paper introduces two scoring functions to assess the quality of secondary structures in generated protein sequences and shows improved detection of non-functional sequences.

Flexible machine learning prediction of antigen presentation for rare and common HLA-I alleles

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

The heterogeneous landscape and early evolution of pathogen-associated CpG dinucleotides in SARS-CoV-2

The paper analyzes CpG dinucleotide distribution in SARS-CoV-2, finding low average CpG content with fluctuations linked to immune evasion and replication strategies.

Adaptation of olfactory receptor abundances for efficient coding

The paper explains how olfactory receptor abundances adapt to efficiently encode odor information, showing context-dependent changes predicted by efficient coding theory.

Integration and multiplexing of positional and contextual information by the hippocampal network

This paper develops a dual neural activity decoder and attractor neural network model to analyze hippocampal recordings and show how the hippocampus maintains spatial and contextual representations during rapid contextual changes.

Functional connectivity models for decoding of spatial representations from hippocampal CA1 recordings

This paper introduces a functional-connectivity-based decoder for spatial representation decoding in hippocampal CA1, showing improved performance over existing methods and revealing prolonged instability after teleportation events.

Benchmarking inverse statistical approaches for protein structure and design with exactly solvable models

This paper benchmarks inverse statistical methods for protein structure and design using exactly solvable lattice protein models, showing their ability to capture both positive and negative design principles.

Inferred Model of the Prefrontal Cortex Activity Unveils Cell Assemblies and Memory Replay

The paper infers cell assemblies and memory replay in prefrontal cortex activity using a statistical physics-inspired model applied to neural recordings in rats.