scholarly journals Hive Panel Explorer: an interactive network visualization tool

2020 ◽  
Author(s):  
Sarah E I Perez ◽  
Aria S Hahn ◽  
Martin Krzywinski ◽  
Steven J Hallam
Keyword(s):  
Data Driven ◽  
Supplementary Information ◽  
Network Visualization ◽  
Visualization Tool ◽  
Rule Based ◽  
Software Application ◽  
Interactive Network ◽  
Dynamics And Function ◽  
Multiple Network ◽  
And Function

Abstract Motivation Networks are used to relate topological structure to system dynamics and function, particularly in ecology systems biology. Network analysis is often guided or complemented by data-driven visualization. Hive one of many network visualizations, distinguish themselves as providing a general, consistent and coherent rule-based representation to motivate hypothesis development and testing. Results Here, we present HyPE, Hive Panel Explorer, a software application that creates a panel of interactive hive plots. HyPE enables network exploration based on user-driven layout rules and parameter combinations for simultaneous of multiple network views. We demonstrate HyPE’s features by exploring a microbial co-occurrence network constructed from forest soil microbiomes. Availability and implementation HyPE is available under the GNU license: https://github.com/hallamlab/HivePanelExplorer. Supplementary information Supplementary data are available at Bioinformatics online.

Building Network Visualization Tools to Facilitate Metacognition in Complex Analysis

Leonardo ◽  
2011 ◽  
Vol 44 (3) ◽  
pp. 248-249
Author(s):  
Barbara Mirel
Keyword(s):  
Complex Analysis ◽  
Tool Design ◽  
Network Visualization ◽  
Visualization Tool ◽  
Complex Tasks ◽  
Interactive Network ◽  
Visualization Tools

If whole communities of domain analysts are to be able to use interactive network visualization tools productively and efficiently, tool design needs to adequately support the metacognition implicit in complex visual analytic tasks. Metacognition for such exploratory network-mediated tasks applies across disciplines. This essay presents metacognitive demands inherent in complex tasks aimed at uncovering relevant relationships for hypothesizing purposes and proposes network visualization tool designs that can support these metacognitive demands.

Chordomics: a visualization tool for linking function to phylogeny in microbiomes

2019 ◽  
Author(s):  
Kevin McDonnell ◽  
Nicholas Waters ◽  
Enda Howley ◽  
Florence Abram
Keyword(s):  
Ecosystem Functioning ◽  
Supplementary Information ◽  
Visualization Tool ◽  
Omics Data ◽  
Microbial Function ◽  
Microbiome Analysis ◽  
Visualization Tools ◽  
And Function ◽  
Experimental Strategies ◽  
Omics Data Analysis

Abstract Summary The overarching aim of microbiome analysis is to uncover the links between microbial phylogeny and function in order to access ecosystem functioning. This can be done using several experimental strategies targeting different biomolecules, including DNA (metagenomics), RNA (metatranscriptomics) and proteins (metaproteomics). Despite the importance of linking microbial function to phylogeny there are currently no visualization tools that effectively integrate this information. Chordomics is a Shiny-based application for linked -omics data analysis, allowing users to visualize microbial function and phylogeny on a single plot and compare datasets across time and environments. Availability and implementation Chordomics is available on GitHub: https://github.com/kevinmcdonnell6/chordomics; software is coded in R and JavaScript and a demonstration version is available at https://kmcd.shinyapps.io/ChordomicsDemo/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals ccNetViz: a WebGL-based JavaScript library for visualization of large networks

2020 ◽  
Vol 36 (16) ◽  
pp. 4527-4529
Author(s):  
Ales Saska ◽  
David Tichy ◽  
Robert Moore ◽  
Achilles Rasquinha ◽  
Caner Akdas ◽  
...  
Keyword(s):  
Systems Biology ◽  
Complex Networks ◽  
Open Source ◽  
High Speed ◽  
A Priori ◽  
Supplementary Information ◽  
Network Visualization ◽  
Supplementary Data ◽  
Web Based ◽  
Flow Of Information

Abstract Summary Visualizing a network provides a concise and practical understanding of the information it represents. Open-source web-based libraries help accelerate the creation of biologically based networks and their use. ccNetViz is an open-source, high speed and lightweight JavaScript library for visualization of large and complex networks. It implements customization and analytical features for easy network interpretation. These features include edge and node animations, which illustrate the flow of information through a network as well as node statistics. Properties can be defined a priori or dynamically imported from models and simulations. ccNetViz is thus a network visualization library particularly suited for systems biology. Availability and implementation The ccNetViz library, demos and documentation are freely available at http://helikarlab.github.io/ccNetViz/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Extraction of organic chemistry grammar from unsupervised learning of chemical reactions

2021 ◽  
Vol 7 (15) ◽  
pp. eabe4166
Author(s):  
Philippe Schwaller ◽  
Benjamin Hoover ◽  
Jean-Louis Reymond ◽  
Hendrik Strobelt ◽  
Teodoro Laino
Keyword(s):  
Organic Chemistry ◽  
Neural Networks ◽  
Chemical Synthesis ◽  
Unsupervised Learning ◽  
Chemical Reactions ◽  
Data Driven ◽  
Experimental Task ◽  
Rule Based ◽  
Atom Mapping ◽  
Mapping Information

Humans use different domain languages to represent, explore, and communicate scientific concepts. During the last few hundred years, chemists compiled the language of chemical synthesis inferring a series of “reaction rules” from knowing how atoms rearrange during a chemical transformation, a process called atom-mapping. Atom-mapping is a laborious experimental task and, when tackled with computational methods, requires continuous annotation of chemical reactions and the extension of logically consistent directives. Here, we demonstrate that Transformer Neural Networks learn atom-mapping information between products and reactants without supervision or human labeling. Using the Transformer attention weights, we build a chemically agnostic, attention-guided reaction mapper and extract coherent chemical grammar from unannotated sets of reactions. Our method shows remarkable performance in terms of accuracy and speed, even for strongly imbalanced and chemically complex reactions with nontrivial atom-mapping. It provides the missing link between data-driven and rule-based approaches for numerous chemical reaction tasks.

HISTORICAL RETE NETWORKS TO SUPPORT THE DEBUGGING OF FORWARD-CHAINING RULE-BASED PROGRAMS

1993 ◽  
Vol 02 (01) ◽  
pp. 47-70
Author(s):  
SHARON M. TUTTLE ◽  
CHRISTOPH F. EICK
Keyword(s):  
Working Memory ◽  
Data Driven ◽  
Rule Based ◽  
Historical Information ◽  
Forward Chaining ◽  
Changing Environments ◽  
Time Performance ◽  
Inference Network ◽  
One Step ◽  
Explanation System

Forward-chaining rule-based programs, being data-driven, can function in changing environments in which backward-chaining rule-based programs would have problems. But, degugging forward-chaining programs can be tedious; to debug a forward-chaining rule-based program, certain ‘historical’ information about the program run is needed. Programmers should be able to directly request such information, instead of having to rerun the program one step at a time or search a trace of run details. As a first step in designing an explanation system for answering such questions, this paper discusses how a forward-chaining program run’s ‘historical’ details can be stored in its Rete inference network, used to match rule conditions to working memory. This can be done without seriously affecting the network’s run-time performance. We call this generalization of the Rete network a historical Rete network. Various algorithms for maintaining this network are discussed, along with how it can be used during debugging, and a debugging tool, MIRO, that incorporates these techniques is also discussed.

scholarly journals Exploring the microbiome in health and disease

2017 ◽  
Vol 1 ◽  
pp. 239784731774188 ◽  
Author(s):  
Elena Scotti ◽  
Stéphanie Boué ◽  
Giuseppe Lo Sasso ◽  
Filippo Zanetti ◽  
Vincenzo Belcastro ◽  
...  
Keyword(s):  
Human Health ◽  
Metabolic Diseases ◽  
Skin Diseases ◽  
Human Microbiome ◽  
Chronic Obstructive ◽  
Microbial Composition ◽  
Obstructive Pulmonary Disease ◽  
New Findings ◽  
Dynamics And Function ◽  
And Function

The analysis of human microbiome is an exciting and rapidly expanding field of research. In the past decade, the biological relevance of the microbiome for human health has become evident. Microbiome comprises a complex collection of microorganisms, with their genes and metabolites, colonizing different body niches. It is now well known that the microbiome interacts with its host, assisting in the bioconversion of nutrients and detoxification, supporting immunity, protecting against pathogenic microbes, and maintaining health. Remarkable new findings showed that our microbiome not only primarily affects the health and function of the gastrointestinal tract but also has a strong influence on general body health through its close interaction with the nervous system and the lung. Therefore, a perfect and sensitive balanced interaction of microbes with the host is required for a healthy body. In fact, growing evidence suggests that the dynamics and function of the indigenous microbiota can be influenced by many factors, including genetics, diet, age, and toxicological agents like cigarette smoke, environmental contaminants, and drugs. The disruption of this balance, that is called dysbiosis, is associated with a plethora of diseases, including metabolic diseases, inflammatory bowel disease, chronic obstructive pulmonary disease, periodontitis, skin diseases, and neurological disorders. The importance of the host microbiome for the human health has also led to the emergence of novel therapeutic approaches focused on the intentional manipulation of the microbiota, either by restoring missing functions or eliminating harmful roles. In the present review, we outline recent studies devoted to elucidate not only the role of microbiome in health conditions and the possible link with various types of diseases but also the influence of various toxicological factors on the microbial composition and function.

Rule-Based Data-Driven Analytics for Wide-Area Fault Detection Using Synchrophasor Data

2017 ◽  
Vol 53 (3) ◽  
pp. 1789-1798 ◽  
Author(s):  
Xiaodong Liang ◽  
Scott A. Wallace ◽  
Duc Nguyen
Keyword(s):  
Fault Detection ◽  
Data Driven ◽  
Wide Area ◽  
Rule Based
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