mlsanityData to 14 Oct 2026

adabmDCA 2.0 -- a flexible but easy-to-use package for Direct Coupling Analysis

arXiv 2501.18456 ↗First posted 30 Jan 2025q-bio.QM cs.LG physics.bio-phcited by 2

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Abstract (arXiv)

In this methods article, we provide a flexible but easy-to-use implementation of Direct Coupling Analysis (DCA) based on Boltzmann machine learning, together with a tutorial on how to use it. The package textttadabmDCA 2.0 is available in different programming languages (C++, Julia, Python) usable on different architectures (single-core and multi-core CPU, GPU) using a common front-end interface. In addition to several learning protocols for dense and sparse generative DCA models, it allows to directly address common downstream tasks like residue-residue contact prediction, mutational-effect prediction, scoring of sequence libraries and generation of artificial sequences for sequence design. It is readily applicable to protein and RNA sequence data.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 2 of its 14 references that are in the map. First posted on arXiv 30 Jan 2025.

Builds on

2 of 14 references.

References · 2

  1. 2021Equilibrium and non-Equilibrium regimes in the learning of Restricted Boltzmann Machinescited by 27
  2. 2020Sparse generative modeling via parameter-reduction of Boltzmann machines: application to protein-sequence familiescited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Expanding functional protein sequence space using high entropy generative modelscited by 0
  2. 2026Equilibrium Training of Energy-Based Models with Parallel Trajectory Temperingcited by 0