Persistent-homology-based machine learning: a survey and a comparative study
A suitable feature representation that can both preserve the data intrinsic information and
reduce data complexity and dimensionality is key to the performance of machine learning …
reduce data complexity and dimensionality is key to the performance of machine learning …
Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening
This work introduces a number of algebraic topology approaches, including multi-
component persistent homology, multi-level persistent homology, and electrostatic …
component persistent homology, multi-level persistent homology, and electrostatic …
Persistent homology analysis of protein structure, flexibility, and folding
Proteins are the most important biomolecules for living organisms. The understanding of
protein structure, function, dynamics, and transport is one of the most challenging tasks in …
protein structure, function, dynamics, and transport is one of the most challenging tasks in …
Weighted persistent homology for osmolyte molecular aggregation and hydrogen-bonding network analysis
It has long been observed that trimethylamine N-oxide (TMAO) and urea demonstrate
dramatically different properties in a protein folding process. Even with the enormous …
dramatically different properties in a protein folding process. Even with the enormous …
A topological approach for protein classification
Protein function and dynamics are closely related to its sequence and structure. However,
prediction of protein function and dynamics from its sequence and structure is still a …
prediction of protein function and dynamics from its sequence and structure is still a …
Weighted persistent homology for biomolecular data analysis
In this paper, we systematically review weighted persistent homology (WPH) models and
their applications in biomolecular data analysis. Essentially, the weight value, which reflects …
their applications in biomolecular data analysis. Essentially, the weight value, which reflects …
[HTML][HTML] Geometrical and topological approaches to Big Data
Modern data science uses topological methods to find the structural features of data sets
before further supervised or unsupervised analysis. Geometry and topology are very natural …
before further supervised or unsupervised analysis. Geometry and topology are very natural …
Multidimensional persistence in biomolecular data
Persistent homology has emerged as a popular technique for the topological simplification
of big data, including biomolecular data. Multidimensional persistence bears considerable …
of big data, including biomolecular data. Multidimensional persistence bears considerable …
A review of geometric, topological and graph theory apparatuses for the modeling and analysis of biomolecular data
Geometric, topological and graph theory modeling and analysis of biomolecules are of
essential importance in the conceptualization of molecular structure, function, dynamics, and …
essential importance in the conceptualization of molecular structure, function, dynamics, and …
Topological data analysis (TDA) for time series
N Ravishanker, R Chen - arXiv preprint arXiv:1909.10604, 2019 - arxiv.org
The study of topology is strictly speaking, a topic in pure mathematics. However in only a few
years, Topological Data Analysis (TDA), which refers to methods of utilizing topological …
years, Topological Data Analysis (TDA), which refers to methods of utilizing topological …