Francesco Caredda

Ph.D. candidate in Fisica , 38^th cycle (2022-2025)
Department of Applied Science and Technology (DISAT)

Interpretable and controllable Statistical Inference Models for Protein Structure Prediction and Sequence Design

Statistical Physics, Inference, Protein Structure Prediction & Design, Machine Learning, Attention

Biofisica Statistica / Statistical Biophysics

Fisica dei Sistemi Complessi / Physics of Complex Systems

Meccanica Statistica / Statistical Mechanics

PE6_13 - Bioinformatics, bio-inspired computing, and natural computing

PE2_18 - Equilibrium and non-equilibrium statistical mechanics: steady states and dynamics

PE1_15 - Generic statistical methodology and modelling

PE6_11 - Machine learning, statistical data processing and applications using signal processing (e.g. speech, image, video)

PE1_12 - Mathematical physics

PE1_14 - Mathematical statistics

PE2_16 - Non-linear physics

PE4_11 - Physical chemistry of biological systems

PE1_13 - Probability

PE1_19 - Scientific computing and data processing

Goal 3: Good health and well-being

Caredda, Francesco; Gennai, Lisa; De Los Rios, Paolo; Pagnani, Andrea (2026)
Controllable protein design via autoregressive direct coupling analysis conditioned on principal components. In: PLOS COMPUTATIONAL BIOLOGY, vol. 22. ISSN 1553-7358
Contributo su Rivista
Caredda, F.; Pagnani, A. (2025)
Direct coupling analysis and the attention mechanism. In: BMC BIOINFORMATICS, vol. 26. ISSN 1471-2105
Contributo su Rivista