Time-dependent classification of protein diffusion types: A statistical detection of mean-squared-displacement exponent transitions

Hubicka, Katarzyna; Janczura, Joanna

doi:10.1103/physreve.101.022107

Cited by 11 publications

(10 citation statements)

References 51 publications

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“…We just could use some additional information about the G proteins in order to indicate if the classifiers work reasonably or not. Second, real trajectories are often heterogeneous, meaning that a particle may change its type of motion within a single trajectory [69]. Thus the classifiers fed with homogeneous synthetic data may be not the best choice to work with such data.…”

Section: Empirical Datamentioning

confidence: 99%

“…The scaling of trajectories in the training data set has introduced significant changes in the results (please compare Tables 12 and 14), thus the properties of particular features should be further examined (for example, their normalisation). Moreover, in [69], the authors showed that the trajectories in the analysed data set change their character during the time evolution. Different features used in the classifiers probably capture slightly different characteristics of the trajectories; thus, the sensitivity of features for the heterogeneity of movement should be verified.…”

Section: Random Forest Gradient Boostingmentioning

confidence: 99%

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Impact of Feature Choice on Machine Learning Classification of Fractional Anomalous Diffusion

Loch-Olszewska

Szwabiński

2020

Entropy

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The growing interest in machine learning methods has raised the need for a careful study of their application to the experimental single-particle tracking data. In this paper, we present the differences in the classification of the fractional anomalous diffusion trajectories that arise from the selection of the features used in random forest and gradient boosting algorithms. Comparing two recently used sets of human-engineered attributes with a new one, which was tailor-made for the problem, we show the importance of a thoughtful choice of the features and parameters. We also analyse the influence of alterations of synthetic training data set on the classification results. The trained classifiers are tested on real trajectories of G proteins and their receptors on a plasma membrane.

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Section: Empirical Datamentioning

confidence: 99%

Section: Random Forest Gradient Boostingmentioning

confidence: 99%

Impact of Feature Choice on Machine Learning Classification of Fractional Anomalous Diffusion

Loch-Olszewska

Szwabiński

2020

Entropy

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“…Really, if the motion is driven by the fractional Brownian motion, then the best among the available methods is the one based on the p-VAR test, especially for longer trajectories. But, if the particle dynamics can be described by the Ornstein-Uhlenbeck or diffusive Brownian motion process, then the method yielding the smallest errors is based on the MAX test [102][103][104][105]. Following the procedure, we use the statistical test:…”

Section: G-proteins Vs A2ar Receptors From the Analysis Of The Spt Datamentioning

confidence: 99%

“…Possible transitions of the particles' diffusion type within single trajectories are noted and investigated. For example, in [104] it has been proposed a statistical procedure for detecting transitions of the MSD exponent value within a single trajectory.…”

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confidence: 99%

Look at Tempered Subdiffusion in a Conjugate Map: Desire for the Confinement

Stanislavsky

Weron

2020

Entropy

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The Laplace distribution of random processes was observed in numerous situations that include glasses, colloidal suspensions, live cells, and firm growth. Its origin is not so trivial as in the case of Gaussian distribution, supported by the central limit theorem. Sums of Laplace distributed random variables are not Laplace distributed. We discovered a new mechanism leading to the Laplace distribution of observable values. This mechanism changes the contribution ratio between a jump and a continuous parts of random processes. Our concept uses properties of Bernstein functions and subordinators connected with them.

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“…CD is a mature area and has been applied to a wide range of applications, including speech processing [ 33 , 34 ], image processing [ 35 ], analysis of electroencephalogram (EEG) and electrocardiogram (ECG) signals [ 36 , 37 , 38 ], and geophysics [ 35 ]. They have also been used in the context of SPT to segment trajectories based on model type, most commonly to distinguish between free and confined modes of motion [ 39 , 40 , 41 ]. CD methods use a threshold on a detection (residual) signal to indicate when a change has occurred and their performance depends heavily on the choice of that level.…”

Section: Introductionmentioning

confidence: 99%

An Estimation Algorithm for General Linear Single Particle Tracking Models with Time-Varying Parameters

2021

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Single Particle Tracking (SPT) is a powerful class of methods for studying the dynamics of biomolecules inside living cells. The techniques reveal the trajectories of individual particles, with a resolution well below the diffraction limit of light, and from them the parameters defining the motion model, such as diffusion coefficients and confinement lengths. Most existing algorithms assume these parameters are constant throughout an experiment. However, it has been demonstrated that they often vary with time as the tracked particles move through different regions in the cell or as conditions inside the cell change in response to stimuli. In this work, we propose an estimation algorithm to determine time-varying parameters of systems that discretely switch between different linear models of motion with Gaussian noise statistics, covering dynamics such as diffusion, directed motion, and Ornstein–Uhlenbeck dynamics. Our algorithm consists of three stages. In the first stage, we use a sliding window approach, combined with Expectation Maximization (EM) to determine maximum likelihood estimates of the parameters as a function of time. These results are only used to roughly estimate the number of model switches that occur in the data to guide the selection of algorithm parameters in the second stage. In the second stage, we use Change Detection (CD) techniques to identify where the models switch, taking advantage of the off-line nature of the analysis of SPT data to create non-causal algorithms with better precision than a purely causal approach. Finally, we apply EM to each set of data between the change points to determine final parameter estimates. We demonstrate our approach using experimental data generated in the lab under controlled conditions.

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Time-dependent classification of protein diffusion types: A statistical detection of mean-squared-displacement exponent transitions

Cited by 11 publications

References 51 publications

Impact of Feature Choice on Machine Learning Classification of Fractional Anomalous Diffusion

Impact of Feature Choice on Machine Learning Classification of Fractional Anomalous Diffusion

Look at Tempered Subdiffusion in a Conjugate Map: Desire for the Confinement

An Estimation Algorithm for General Linear Single Particle Tracking Models with Time-Varying Parameters

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