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Journal ArticleDOI

Fuzzy spectral clustering by PCCA+: application to Markov state models and data classification

TLDR
It is demonstrated in this paper that PCCA+ always delivers an optimal fuzzy clustering for nearly uncoupled, not necessarily reversible, Markov chains with transition states.
Abstract
Given a row-stochastic matrix describing pairwise similarities between data objects, spectral clustering makes use of the eigenvectors of this matrix to perform dimensionality reduction for clustering in fewer dimensions. One example from this class of algorithms is the Robust Perron Cluster Analysis (PCCA+), which delivers a fuzzy clustering. Originally developed for clustering the state space of Markov chains, the method became popular as a versatile tool for general data classification problems. The robustness of PCCA+, however, cannot be explained by previous perturbation results, because the matrices in typical applications do not comply with the two main requirements: reversibility and nearly decomposability. We therefore demonstrate in this paper that PCCA+ always delivers an optimal fuzzy clustering for nearly uncoupled, not necessarily reversible, Markov chains with transition states.

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Posted ContentDOI

RPnet: A Reverse Projection Based Neural Network for Coarse-graining Metastable Conformational States for Protein Dynamics

TL;DR: In this paper, a reverse projection based neural network (RPnet) method is proposed to estimate the probability of inter-state transitions based on short molecular dynamics trajectories, which can be used to extrapolate long time kinetics if the Markovian condition is met.
Journal ArticleDOI

Oncogenic Mutations in the DNA-Binding Domain of FOXO1 that Disrupt Folding: Quantitative Insights from Experiments and Molecular Simulations

- 27 Jul 2022 - 
TL;DR: In this article , the effect of mutations on FOXO1 folding was performed using alchemical free energy perturbation (FEP), and a Markov model of the entire folding reaction was constructed from massively parallel molecular simulations, which predicts folding pathways involving the late folding of helix α3.
Journal ArticleDOI

Understanding the Structure and Apo Dynamics of the Functionally Active JIP1 Fragment.

TL;DR: Molecular dynamics simulations using AMBER 14 were used to study the structure and dynamics of a functionally active JIP1 10mer fragment to better understand the solution behavior of the fragment and identified seven structurally stable conformations of the 10mer fragments identified via classical clustering.
Journal ArticleDOI

Reweighting non-equilibrium steady-state dynamics along collective variables

TL;DR: In this article, the authors proposed a Maximum Caliber (MaxCal) approach for dynamical reweighting of trajectories of complex systems at a single thermodynamic state point, which can be dynamically reweighted both from and to equilibrium or non-equilibrium steady states.
Journal ArticleDOI

Conformational Plasticity in α-Synuclein and How Crowded Environment Modulates It.

TL;DR: In this paper , the authors identify an optimal set of distinct metastable states of α-synuclein in aqueous media by dissecting a 73 μs-long molecular dynamics ensemble via building a comprehensive Markov state model.
References
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Book

Perturbation theory for linear operators

Tosio Kato
TL;DR: The monograph by T Kato as discussed by the authors is an excellent reference work in the theory of linear operators in Banach and Hilbert spaces and is a thoroughly worthwhile reference work both for graduate students in functional analysis as well as for researchers in perturbation, spectral, and scattering theory.
Journal ArticleDOI

Normalized cuts and image segmentation

TL;DR: This work treats image segmentation as a graph partitioning problem and proposes a novel global criterion, the normalized cut, for segmenting the graph, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups.
Journal ArticleDOI

A tutorial on spectral clustering

TL;DR: In this article, the authors present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches, and discuss the advantages and disadvantages of these algorithms.
Proceedings Article

On Spectral Clustering: Analysis and an algorithm

TL;DR: A simple spectral clustering algorithm that can be implemented using a few lines of Matlab is presented, and tools from matrix perturbation theory are used to analyze the algorithm, and give conditions under which it can be expected to do well.
Journal ArticleDOI

Laplacian Eigenmaps for dimensionality reduction and data representation

TL;DR: In this article, the authors proposed a geometrically motivated algorithm for representing high-dimensional data, based on the correspondence between the graph Laplacian, the Laplace Beltrami operator on the manifold and the connections to the heat equation.
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