Dataset Information

Integrating physics in deep learning algorithms: a force field as a PyTorch module.

ABSTRACT:

Motivation

Deep learning algorithms applied to structural biology often struggle to converge to meaningful solutions when limited data is available, since they are required to learn complex physical rules from examples. State-of-the-art force-fields, however, cannot interface with deep learning algorithms due to their implementation.

Results

We present MadraX, a forcefield implemented as a differentiable PyTorch module, able to interact with deep learning algorithms in an end-to-end fashion.

Availability and implementation

MadraX documentation, together with tutorials and installation guide, is available at madrax.readthedocs.io.

SUBMITTER: Orlando G

PROVIDER: S-EPMC11007235 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Publications

Integrating physics in deep learning algorithms: a force field as a PyTorch module.

Orlando Gabriele G Serrano Luis L Schymkowitz Joost J Rousseau Frederic F

Bioinformatics (Oxford, England) 20240301 4

<h4>Motivation</h4>Deep learning algorithms applied to structural biology often struggle to converge to meaningful solutions when limited data is available, since they are required to learn complex physical rules from examples. State-of-the-art force-fields, however, cannot interface with deep learning algorithms due to their implementation.<h4>Results</h4>We present MadraX, a forcefield implemented as a differentiable PyTorch module, able to interact with deep learning algorithms in an end-to-e ...[more]

PMID: 38514422

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Project description:BACKGROUND: In recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Despite the demonstrated success of such algorithms in uncovering biologically relevant regulatory relations, further developments in the area are hampered by a lack of tools to compare the performance of alternative module network learning strategies. Here, we demonstrate the use of the synthetic data generator SynTReN for the purpose of testing and comparing module network learning algorithms. We introduce a software package for learning module networks, called LeMoNe, which incorporates a novel strategy for learning regulatory programs. Novelties include the use of a bottom-up Bayesian hierarchical clustering to construct the regulatory programs, and the use of a conditional entropy measure to assign regulators to the regulation program nodes. Using SynTReN data, we test the performance of LeMoNe in a completely controlled situation and assess the effect of the methodological changes we made with respect to an existing software package, namely Genomica. Additionally, we assess the effect of various parameters, such as the size of the data set and the amount of noise, on the inference performance. RESULTS: Overall, application of Genomica and LeMoNe to simulated data sets gave comparable results. However, LeMoNe offers some advantages, one of them being that the learning process is considerably faster for larger data sets. Additionally, we show that the location of the regulators in the LeMoNe regulation programs and their conditional entropy may be used to prioritize regulators for functional validation, and that the combination of the bottom-up clustering strategy with the conditional entropy-based assignment of regulators improves the handling of missing or hidden regulators. CONCLUSION: We show that data simulators such as SynTReN are very well suited for the purpose of developing, testing and improving module network algorithms. We used SynTReN data to develop and test an alternative module network learning strategy, which is incorporated in the software package LeMoNe, and we provide evidence that this alternative strategy has several advantages with respect to existing methods.

Dataset Information

Integrating physics in deep learning algorithms: a force field as a PyTorch module.

Motivation

Results

Availability and implementation

Publications

Integrating physics in deep learning algorithms: a force field as a PyTorch module.

Similar Datasets

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