scholarly journals High-Throughput Screening Approach for Nanoporous Materials Genome Using Topological Data Analysis: Application to Zeolites

2018 ◽  
Vol 14 (8) ◽  
pp. 4427-4437 ◽  
Author(s):  
Yongjin Lee ◽  
Senja D. Barthel ◽  
Paweł Dłotko ◽  
Seyed Mohamad Moosavi ◽  
Kathryn Hess ◽  
...  
Keyword(s):  
Data Analysis ◽  
High Throughput ◽  
High Throughput Screening ◽  
Nanoporous Materials ◽  
Topological Data Analysis ◽  
Screening Approach ◽  
Analysis Application ◽  
Materials Genome ◽  
Topological Data

scholarly journals Breeze: an integrated quality control and data analysis application for high-throughput drug screening

2020 ◽  
Vol 36 (11) ◽  
pp. 3602-3604 ◽  
Author(s):  
Swapnil Potdar ◽  
Aleksandr Ianevski ◽  
John-Patrick Mpindi ◽  
Dmitrii Bychkov ◽  
Clément Fiere ◽  
...  
Keyword(s):  
Quality Control ◽  
Data Analysis ◽  
High Throughput ◽  
High Throughput Screening ◽  
Complete Solution ◽  
Supplementary Information ◽  
Source Analysis ◽  
Technical Documentation ◽  
Resistance Patterns ◽  
Analysis Application

Abstract Summary High-throughput screening (HTS) enables systematic testing of thousands of chemical compounds for potential use as investigational and therapeutic agents. HTS experiments are often conducted in multi-well plates that inherently bear technical and experimental sources of error. Thus, HTS data processing requires the use of robust quality control procedures before analysis and interpretation. Here, we have implemented an open-source analysis application, Breeze, an integrated quality control and data analysis application for HTS data. Furthermore, Breeze enables a reliable way to identify individual drug sensitivity and resistance patterns in cell lines or patient-derived samples for functional precision medicine applications. The Breeze application provides a complete solution for data quality assessment, dose–response curve fitting and quantification of the drug responses along with interactive visualization of the results. Availability and implementation The Breeze application with video tutorial and technical documentation is accessible at https://breeze.fimm.fi; the R source code is publicly available at https://github.com/potdarswapnil/Breeze under GNU General Public License v3.0. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Topological Data Analysis For Evaluating PDE-based Denoising Models

2021 ◽  
Vol 1897 (1) ◽  
pp. 012006
Author(s):  
Ahmed K. Al-Jaberi ◽  
Ehsan M. Hameed
Keyword(s):  
Data Analysis ◽  
Topological Data Analysis ◽  
Topological Data

scholarly journals Topological Data Analysis Approaches to Uncovering the Timing of Ring Structure Onset in Filamentous Networks

2021 ◽  
Vol 83 (3) ◽  
Author(s):  
Maria-Veronica Ciocanel ◽  
Riley Juenemann ◽  
Adriana T. Dawes ◽  
Scott A. McKinley
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Actin Filaments ◽  
Time Series Data ◽  
Polymer Networks ◽  
Topological Data Analysis ◽  
Series Data ◽  
Topological Features ◽  
Topological Data

AbstractIn developmental biology as well as in other biological systems, emerging structure and organization can be captured using time-series data of protein locations. In analyzing this time-dependent data, it is a common challenge not only to determine whether topological features emerge, but also to identify the timing of their formation. For instance, in most cells, actin filaments interact with myosin motor proteins and organize into polymer networks and higher-order structures. Ring channels are examples of such structures that maintain constant diameters over time and play key roles in processes such as cell division, development, and wound healing. Given the limitations in studying interactions of actin with myosin in vivo, we generate time-series data of protein polymer interactions in cells using complex agent-based models. Since the data has a filamentous structure, we propose sampling along the actin filaments and analyzing the topological structure of the resulting point cloud at each time. Building on existing tools from persistent homology, we develop a topological data analysis (TDA) method that assesses effective ring generation in this dynamic data. This method connects topological features through time in a path that corresponds to emergence of organization in the data. In this work, we also propose methods for assessing whether the topological features of interest are significant and thus whether they contribute to the formation of an emerging hole (ring channel) in the simulated protein interactions. In particular, we use the MEDYAN simulation platform to show that this technique can distinguish between the actin cytoskeleton organization resulting from distinct motor protein binding parameters.

scholarly journals Classification of apatite structures via topological data analysis: a framework for a ‘Materials Barcode’ representation of structure maps

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Scott Broderick ◽  
Ruhil Dongol ◽  
Tianmu Zhang ◽  
Krishna Rajan
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Crystal Chemistry ◽  
Persistent Homology ◽  
Hierarchical Classification ◽  
Topological Data Analysis ◽  
Learning Tool ◽  
Coordination Polyhedra ◽  
Machine Learning Tool ◽  
Topological Data

AbstractThis paper introduces the use of topological data analysis (TDA) as an unsupervised machine learning tool to uncover classification criteria in complex inorganic crystal chemistries. Using the apatite chemistry as a template, we track through the use of persistent homology the topological connectivity of input crystal chemistry descriptors on defining similarity between different stoichiometries of apatites. It is shown that TDA automatically identifies a hierarchical classification scheme within apatites based on the commonality of the number of discrete coordination polyhedra that constitute the structural building units common among the compounds. This information is presented in the form of a visualization scheme of a barcode of homology classifications, where the persistence of similarity between compounds is tracked. Unlike traditional perspectives of structure maps, this new “Materials Barcode” schema serves as an automated exploratory machine learning tool that can uncover structural associations from crystal chemistry databases, as well as to achieve a more nuanced insight into what defines similarity among homologous compounds.

The Shape of Things: Topological Data Analysis

CHANCE ◽  
2021 ◽  
Vol 34 (2) ◽  
pp. 59-64
Author(s):  
Nicole Lazar ◽  
Hyunnam Ryu
Keyword(s):  
Data Analysis ◽  
Topological Data Analysis ◽  
Topological Data
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