Temporal Subspace Clustering for Molecular Dynamics Data

Autor:	Beer, Anna, Heinrigs, Martin, Plant, Claudia, Assent, Ira
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Information Retrieval Physics - Chemical Physics I.5.3 H.3.3 J.2
Druh dokumentu:	Working Paper
Popis:	We introduce MOSCITO (MOlecular Dynamics Subspace Clustering with Temporal Observance), a subspace clustering for molecular dynamics data. MOSCITO groups those timesteps of a molecular dynamics trajectory together into clusters in which the molecule has similar conformations. In contrast to state-of-the-art methods, MOSCITO takes advantage of sequential relationships found in time series data. Unlike existing work, MOSCITO does not need a two-step procedure with tedious post-processing, but directly models essential properties of the data. Interpreting clusters as Markov states allows us to evaluate the clustering performance based on the resulting Markov state models. In experiments on 60 trajectories and 4 different proteins, we show that the performance of MOSCITO achieves state-of-the-art performance in a novel single-step method. Moreover, by modeling temporal aspects, MOSCITO obtains better segmentation of trajectories, especially for small numbers of clusters. Comment: Accepted as a research paper at BIOKDD 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2408.00056 Zobrazit plný text záznamu View this record from Arxiv