scholarly journals Newt: a comprehensive web-based tool for viewing, constructing and analyzing biological maps

2020 ◽  
Author(s):  
Hasan Balci ◽  
Metin Can Siper ◽  
Nasim Saleh ◽  
Ilkin Safarli ◽  
Ludovic Roy ◽  
...  
Keyword(s):  
Data Analysis ◽  
Source Code ◽  
Biological Data ◽  
Web Based ◽  
Cellular Processes ◽  
Biological Data Analysis ◽  
Accepted Standard ◽  
Visualization Techniques

Abstract Motivation Visualization of cellular processes and pathways is a key recurring requirement for effective biological data analysis. There is a considerable need for sophisticated web-based pathway viewers and editors operating with widely accepted standard formats, using the latest visualization techniques and libraries. Results We developed a web-based tool named Newt for viewing, constructing and analyzing biological maps in standard formats such as SBGN, SBML and SIF. Availability and implementation Newt’s source code is publicly available on GitHub and freely distributed under the GNU LGPL. Ample documentation on Newt can be found on http://newteditor.org and on YouTube.

scholarly journals KymoButler, a deep learning software for automated kymograph analysis

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Maximilian AH Jakobs ◽  
Andrea Dimitracopoulos ◽  
Kristian Franze
Keyword(s):  
Deep Learning ◽  
Data Analysis ◽  
Biological Data ◽  
Machine Learning Techniques ◽  
Unconscious Bias ◽  
Web Based ◽  
Complex Particle ◽  
Learning Techniques ◽  
Biological Data Analysis ◽  
Learning Software

Kymographs are graphical representations of spatial position over time, which are often used in biology to visualise the motion of fluorescent particles, molecules, vesicles, or organelles moving along a predictable path. Although in kymographs tracks of individual particles are qualitatively easily distinguished, their automated quantitative analysis is much more challenging. Kymographs often exhibit low signal-to-noise-ratios (SNRs), and available tools that automate their analysis usually require manual supervision. Here we developed KymoButler, a Deep Learning-based software to automatically track dynamic processes in kymographs. We demonstrate that KymoButler performs as well as expert manual data analysis on kymographs with complex particle trajectories from a variety of different biological systems. The software was packaged in a web-based ‘one-click’ application for use by the wider scientific community (http://kymobutler.deepmirror.ai). Our approach significantly speeds up data analysis, avoids unconscious bias, and represents another step towards the widespread adaptation of Machine Learning techniques in biological data analysis.

scholarly journals KymoButler, a Deep Learning software for automated kymograph analysis

2018 ◽  
Author(s):  
Maximilian A. H. Jakobs ◽  
Andrea Dimitracopoulos ◽  
Kristian Franze
Keyword(s):  
Deep Learning ◽  
Data Analysis ◽  
Biological Data ◽  
Machine Learning Techniques ◽  
Unconscious Bias ◽  
Web Based ◽  
Complex Particle ◽  
Learning Techniques ◽  
Biological Data Analysis ◽  
Learning Software

AbstractKymographs are graphical representations of spatial position over time, which are often used in biology to visualise the motion of fluorescent particles, molecules, vesicles, or organelles moving along a predictable path. Although in kymographs tracks of individual particles are qualitatively easily distinguished, their automated quantitative analysis is much more challenging. Kymographs often exhibit low signal-to-noise-ratios (SNRs), and available tools that automate their analysis usually require manual supervision. Here we developed KymoButler, a Deep Learning-based software to automatically track dynamic processes in kymographs. We demonstrate that KymoButler performs as well as expert manual data analysis on kymographs with complex particle trajectories from a variety of different biological systems. The software was packaged in a web-based “one-click” application for use by the wider scientific community. Our approach significantly speeds up data analysis, avoids unconscious bias, and represents another step towards the widespread adaptation of Machine Learning techniques in biological data analysis.

Graph Cutting in Image Processing Handling with Biological Data Analysis

2019 ◽  
pp. 203-216
Author(s):  
Mária Ždímalová ◽  
Tomáš Bohumel ◽  
Katarína Plachá-Gregorovská ◽  
Peter Weismann ◽  
Hisham El Falougy
Keyword(s):  
Image Processing ◽  
Data Analysis ◽  
Biological Data ◽  
Biological Data Analysis

Innovative Formalism for Biological Data Analysis

2018 ◽  
pp. 1814-1824
Author(s):  
Calin Ciufudean
Keyword(s):  
Information Technology ◽  
Data Analysis ◽  
Medical Devices ◽  
Fuzzy Set ◽  
Biological Data ◽  
Fuzzy Operators ◽  
Biological Data Analysis ◽  
Biological Signals ◽  
Design And Manufacture ◽  
Gathering Data

Modern medical devices involves information technology (IT) based on electronic structures for data and signals sensing and gathering, data and signals transmission as well as data and signals processing in order to assist and help the medical staff to diagnose, cure and to monitors the evolution of patients. By focusing on biological signals processing we may notice that numerical processing of information delivered by sensors has a significant importance for a fair and optimum design and manufacture of modern medical devices. We consider for this approach fuzzy set as a formalism of analysis of biological signals processing and we propose to be accomplished this goal by developing fuzzy operators for filtering the noise of biological signals measurement. We exemplify this approach on neurological measurements performed with an Electro-Encephalograph (EEG).

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