Systematic selection of chemical fingerprint features improves the Gibbs energy prediction of biochemical reactions

Abstract Motivation Accurate and wide-ranging prediction of thermodynamic parameters for biochemical reactions can facilitate deeper insights into the workings and the design of metabolic systems. Results Here, we introduce a machine learning method with chemical fingerprint-based features for the prediction of the Gibbs free energy of biochemical reactions. From a large pool of 2D fingerprint-based features, this method systematically selects a small number of relevant ones and uses them to construct a regularized linear model. Since a manual selection of 2D structure-based features can be a tedious and time-consuming task, requiring expert knowledge about the structure-activity relationship of chemical compounds, the systematic feature selection step in our method offers a convenient means to identify relevant 2D fingerprint-based features. By comparing our method with state-of-the-art linear regression-based methods for the standard Gibbs free energy prediction, we demonstrated that its prediction accuracy and prediction coverage are most favorable. Our results show direct evidence that a number of 2D fingerprints collectively provide useful information about the Gibbs free energy of biochemical reactions and that our systematic feature selection procedure provides a convenient way to identify them. Availability and implementation Our software is freely available for download at http://sfb.kaust.edu.sa/Pages/Software.aspx. Supplementary information Supplementary data are available at Bioinformatics online.

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pyNVR: investigating factors affecting feature selection from scRNA-seq data for lineage reconstruction

Bioinformatics ◽

10.1093/bioinformatics/bty950 ◽

2018 ◽

Vol 35 (13) ◽

pp. 2335-2337 ◽

Cited By ~ 6

Author(s):

Bob Chen ◽

Charles A Herring ◽

Ken S Lau

Keyword(s):

Feature Selection ◽

Supplementary Information ◽

Multidimensional Data ◽

Factors Affecting ◽

Biologically Relevant ◽

Selection Approach ◽

Feature Selection Approach ◽

Unsupervised Algorithms ◽

Python Package ◽

Selection Of

Abstract Motivation The emergence of single-cell RNA-sequencing has enabled analyses that leverage transitioning cell states to reconstruct pseudotemporal trajectories. Multidimensional data sparsity, zero inflation and technical variation necessitate the selection of high-quality features that feed downstream analyses. Despite the development of numerous algorithms for the unsupervised selection of biologically relevant features, their differential performance remains largely unaddressed. Results We implemented the neighborhood variance ratio (NVR) feature selection approach as a Python package with substantial improvements in performance. In comparing NVR with multiple unsupervised algorithms such as dpFeature, we observed striking differences in features selected. We present evidence that quantifiable dataset properties have observable and predictable effects on the performance of these algorithms. Availability and implementation pyNVR is freely available at https://github.com/KenLauLab/NVR. Supplementary information Supplementary data are available at Bioinformatics online.

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Thermodynamically consistent estimation of Gibbs free energy from data: data reconciliation approach

Bioinformatics ◽

10.1093/bioinformatics/btz741 ◽

2019 ◽

Author(s):

Saman Salike ◽

Nirav Bhatt

Keyword(s):

Experimental Data ◽

Free Energy ◽

Gibbs Free Energy ◽

Measurement Errors ◽

Data Reconciliation ◽

Supplementary Information ◽

Group Contributions ◽

Free Energies ◽

Consistent Estimation ◽

Gibbs Energies

Abstract Motivation Thermodynamic analysis of biological reaction networks requires the availability of accurate and consistent values of Gibbs free energies of reaction and formation. These Gibbs energies can be measured directly via the careful design of experiments or can be computed from the curated Gibbs free energy databases. However, the computed Gibbs free energies of reactions and formations do not satisfy the thermodynamic constraints due to the compounding effect of measurement errors in the experimental data. The propagation of these errors can lead to a false prediction of pathway feasibility and uncertainty in the estimation of thermodynamic parameters. Results This work proposes a data reconciliation framework for thermodynamically consistent estimation of Gibbs free energies of reaction, formation and group contributions from experimental data. In this framework, we formulate constrained optimization problems that reduce measurement errors and their effects on the estimation of Gibbs energies such that the thermodynamic constraints are satisfied. When a subset of Gibbs free energies of formations is unavailable, it is shown that the accuracy of their resulting estimates is better than that of existing empirical prediction methods. Moreover, we also show that the estimation of group contributions can be improved using this approach. Further, we provide guidelines based on this approach for performing systematic experiments to estimate unknown Gibbs formation energies. Availability and implementation The MATLAB code for the executing the proposed algorithm is available for free on the GitHub repository: https://github.com/samansalike/DR-thermo. Supplementary information Supplementary data are available at Bioinformatics online.

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Gibbs Free-energy Prediction Method for Iron-base Alloy Materials Based on Deep Learning*

10.1109/ccci52664.2021.9583189 ◽

2021 ◽

Author(s):

Yabin Xu ◽

Shengjie Sun ◽

Zhuang Wu

Keyword(s):

Free Energy ◽

Deep Learning ◽

Gibbs Free Energy ◽

Base Alloy ◽

Prediction Method ◽

Energy Prediction ◽

Iron Base ◽

Iron Base Alloy

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GIBBS POTENTIAL, SYNONYM FOR GIBBS FREE ENERGY

A-to-Z Guide to Thermodynamics Heat and Mass Transfer and Fluids Engineering ◽

10.1615/atoz.g.gibpotsynforgibfreene ◽

2006 ◽

Vol g ◽

Keyword(s):

Free Energy ◽

Gibbs Free Energy ◽

Gibbs Potential

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Feature selection of the armature winding broken coils in synchronous motor using genetic algorithm and mahalanobis distance

Archives of Metallurgy and Materials ◽

10.2478/v10172-012-0091-7 ◽

2012 ◽

Vol 57 (3) ◽

pp. 829-835 ◽

Cited By ~ 1

Author(s):

Z. Głowacz ◽

J. Kozik

Keyword(s):

Genetic Algorithm ◽

Feature Selection ◽

Mahalanobis Distance ◽

Distance Measure ◽

Synchronous Motor ◽

Medical Diagnostics ◽

Motor Current ◽

Feature Spaces ◽

Multidimensional Feature Spaces ◽

Selection Of

The paper describes a procedure for automatic selection of symptoms accompanying the break in the synchronous motor armature winding coils. This procedure, called the feature selection, leads to choosing from a full set of features describing the problem, such a subset that would allow the best distinguishing between healthy and damaged states. As the features the spectra components amplitudes of the motor current signals were used. The full spectra of current signals are considered as the multidimensional feature spaces and their subspaces are tested. Particular subspaces are chosen with the aid of genetic algorithm and their goodness is tested using Mahalanobis distance measure. The algorithm searches for such a subspaces for which this distance is the greatest. The algorithm is very efficient and, as it was confirmed by research, leads to good results. The proposed technique is successfully applied in many other fields of science and technology, including medical diagnostics.

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Misconceptions arising from a Sign Discrepancy in Thermodynamic Data for the Gibbs Free Energy Profile of Ribonuclease A

Protein and Peptide Letters ◽

10.2174/0929866023408625 ◽

2002 ◽

Vol 9 (4) ◽

pp. 305-313

Author(s):

Paul Chun

Keyword(s):

Free Energy ◽

Gibbs Free Energy ◽

Thermodynamic Data ◽

Ribonuclease A ◽

Energy Profile ◽

Free Energy Profile

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Chemical equilibrium and chemical kinetics

10.1093/oso/9780198782957.003.0014 ◽

2018 ◽

Author(s):

Dennis Sherwood ◽

Paul Dalby

Keyword(s):

Free Energy ◽

Equilibrium Constant ◽

Gibbs Free Energy ◽

Chemical Kinetics ◽

Chemical Equilibrium ◽

First Principles ◽

Effect Of Temperature ◽

Total System ◽

Free Energies

Building on the previous chapter, this chapter examines gas phase chemical equilibrium, and the equilibrium constant. This chapter takes a rigorous, yet very clear, ‘first principles’ approach, expressing the total Gibbs free energy of a reaction mixture at any time as the sum of the instantaneous Gibbs free energies of each component, as expressed in terms of the extent-of-reaction. The equilibrium reaction mixture is then defined as the point at which the total system Gibbs free energy is a minimum, from which concepts such as the equilibrium constant emerge. The chapter also explores the temperature dependence of equilibrium, this being one example of Le Chatelier’s principle. Finally, the chapter links thermodynamics to chemical kinetics by showing how the equilibrium constant is the ratio of the forward and backward rate constants. We also introduce the Arrhenius equation, closing with a discussion of the overall effect of temperature on chemical equilibrium.

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