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

Abstract

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.

Detection of Anomalous Diffusion with Deep Residual Networks

“…The first one is perceived as traditional machine learning and utilizes a set of human-engineered features that should be extracted from trajectories to feed the classifiers (see also Refs. [ 46 , 47 ] for a more extensive analysis). The second one is based on deep neural networks, which constitute the state-of-the-art of the modern machine learning classification.…”

“…We will apply our classifier to data from a single particle tracking experiment on G protein-coupled receptors and G proteins, already analyzed in Refs. [ 38 , 46 , 47 ]. The receptors mediate biological effects of many hormones and neourotransmitters and are also important as pharmacological targets [ 75 ].…”

“…In the last few years, classification of diffusion modes utilizing machine learning (ML) algorithms is gaining on popularity. Bayesian approach [ 40 , 41 , 42 ], random forests [ 43 , 44 , 45 , 46 , 47 ], gradient boosting [ 44 , 45 , 46 , 47 ], neural networks [ 48 ], and deep neural networks [ 44 , 49 , 50 , 51 ] have already been used in an attempt to either just classify the trajectories or to extract quantitative information about them (e.g., the anomalous exponent [ 45 , 49 , 51 ]). The ML approach seems to be more powerful than the analytical one.…”

“…Despite the enormous progress in both the analytical and ML methods, the analysis of SPT data remains challenging. The classification results produced by different methods often do not agree with each other [ 27 , 38 , 46 , 47 ]. The reasons are similar to the ones limiting the applicability of MSD: localization errors, short trajectories, or irregular sampling.…”

“…The resulting network will then be applied to the G protein-coupled receptors and G proteins data set, already analyzed in Refs. [ 38 , 46 , 47 ]. Although our method is not a direct response to the AnDi Challenge [ 52 ] (e.g., we use different diffusion models for training), it is consistent with its goal to search for new robust algorithms for classification.…”