Literature DB >> 35822080

HOMOTOPY CONTINUATION FOR THE SPECTRA OF PERSISTENT LAPLACIANS.

Xiaoqi Wei1, Guo-Wei Wei2.   

Abstract

The p-persistent q-combinatorial Laplacian defined for a pair of simplicial complexes is a generalization of the q-combinatorial Laplacian. Given a filtration, the spectra of persistent combinatorial Laplacians not only recover the persistent Betti numbers of persistent homology but also provide extra multiscale geometrical information of the data. Paired with machine learning algorithms, the persistent Laplacian has many potential applications in data science. Seeking different ways to find the spectrum of an operator is an active research topic, becoming interesting when ideas are originated from multiple fields. In this work, we explore an alternative approach for the spectrum of persistent Laplacians. As the eigenvalues of a persistent Laplacian matrix are the roots of its characteristic polynomial, one may attempt to find the roots of the characteristic polynomial by homotopy continuation, and thus resolving the spectrum of the corresponding persistent Laplacian. We consider a set of simple polytopes and small molecules to prove the principle that algebraic topology, combinatorial graph, and algebraic geometry can be integrated to understand the shape of data.

Entities:  

Keywords:  Persistent Laplacian; algebraic geometry; algebraic topology; combinatorial graph; homotopy continuation; persistent homology

Year:  2021        PMID: 35822080      PMCID: PMC9273002          DOI: 10.3934/fods.2021017

Source DB:  PubMed          Journal:  Found Data Sci        ISSN: 2639-8001


  10 in total

1.  Hypergraph-based persistent cohomology (HPC) for molecular representations in drug design.

Authors:  Xiang Liu; Xiangjun Wang; Jie Wu; Kelin Xia
Journal:  Brief Bioinform       Date:  2021-01-22       Impact factor: 11.622

2.  Decoupled molecules with binding polynomials of bidegree (n, 2).

Authors:  Yue Ren; Johannes W R Martini; Jacinta Torres
Journal:  J Math Biol       Date:  2018-10-03       Impact factor: 2.259

3.  Persistent homology analysis of protein structure, flexibility, and folding.

Authors:  Kelin Xia; Guo-Wei Wei
Journal:  Int J Numer Method Biomed Eng       Date:  2014-06-24       Impact factor: 2.747

4.  Weighted persistent homology for biomolecular data analysis.

Authors:  Zhenyu Meng; D Vijay Anand; Yunpeng Lu; Jie Wu; Kelin Xia
Journal:  Sci Rep       Date:  2020-02-07       Impact factor: 4.379

Review 5.  A review of mathematical representations of biomolecular data.

Authors:  Duc Duy Nguyen; Zixuan Cang; Guo-Wei Wei
Journal:  Phys Chem Chem Phys       Date:  2020-02-26       Impact factor: 3.676

6.  Persistent spectral graph.

Authors:  Rui Wang; Duc Duy Nguyen; Guo-Wei Wei
Journal:  Int J Numer Method Biomed Eng       Date:  2020-08-17       Impact factor: 2.747

7.  Numerical algebraic geometry for model selection and its application to the life sciences.

Authors:  Elizabeth Gross; Brent Davis; Kenneth L Ho; Daniel J Bates; Heather A Harrington
Journal:  J R Soc Interface       Date:  2016-10       Impact factor: 4.118

8.  Representation of molecular structures with persistent homology for machine learning applications in chemistry.

Authors:  Jacob Townsend; Cassie Putman Micucci; John H Hymel; Vasileios Maroulas; Konstantinos D Vogiatzis
Journal:  Nat Commun       Date:  2020-06-26       Impact factor: 14.919

Review 9.  Array programming with NumPy.

Authors:  Charles R Harris; K Jarrod Millman; Stéfan J van der Walt; Ralf Gommers; Pauli Virtanen; David Cournapeau; Eric Wieser; Julian Taylor; Sebastian Berg; Nathaniel J Smith; Robert Kern; Matti Picus; Stephan Hoyer; Marten H van Kerkwijk; Matthew Brett; Allan Haldane; Jaime Fernández Del Río; Mark Wiebe; Pearu Peterson; Pierre Gérard-Marchant; Kevin Sheppard; Tyler Reddy; Warren Weckesser; Hameer Abbasi; Christoph Gohlke; Travis E Oliphant
Journal:  Nature       Date:  2020-09-16       Impact factor: 49.962

  10 in total

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