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

By Susanna Röblitz, Marcus Weber. Advances in Data Analysis and Classification, 2013

369 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/ba75b1115f5fb7c361a3b0ecaf25dfde36ea5b25

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Frank No'e
2. Hao Wu
3. Christof Schutte
4. Stefan Klus
5. Feliks Nuske
6. Péter Koltai
7. Collin F Arbour
8. Michael Feig

In Computer Vision and Pattern Recognition

1. Dana Pe'er
2. Collin F Arbour
3. Michael Feig
4. Christof Schutte
5. Frank No'e
6. Hao Wu
7. Fabian J Theis
8. Ioannis G. Kevrekidis

In Signal Processing

1. Collin F Arbour
2. Michael Feig
3. Frank No'e
4. Hao Wu
5. Fabian J Theis
6. Lim Heo

Citations in the corpus

• Data-Driven Model Reduction and Transfer Operator Approximation (2017, methodology): “We employ the PCCA+ algorithm [46, 47] to coarse grain all MSM states into two macrostates.”
• CellRank for directed single-cell fate mapping (2020, positive): “2e, inset), as also visualized with pie charts on a directed implementation of partition-based graph abstraction 8 (PAGA) (Supplementary Fig.”
• Driven to Near-Experimental Accuracy by Refinement via Molecular Dynamics Simulations (2019, methodology): “A Markov-state model was built with microstates, and it was further lumped by the Perron Cluster Cluster Analysis+ (PCCA+) algorithm 46 to obtain a Markov-state model with macrostates according to a desired transition time-scale.”