scholarly journals On Crowd-verification of Biological Networks

2013 ◽  
Vol 7 ◽  
pp. BBI.S12932 ◽  
Author(s):  
Sam Ansari ◽  
Jean Binder ◽  
Stephanie Boue ◽  
Anselmo Di Fabio ◽  
William Hayes ◽  
...  
Keyword(s):  
Biological Networks ◽  
Network Models ◽  
Biological Information ◽  
Current Status ◽  
Biological Knowledge ◽  
Web Based ◽  
Domain Experts ◽  
Graphical Interfaces ◽  
Continuous Use ◽  
Complexity Increase

Biological networks with a structured syntax are a powerful way of representing biological information generated from high density data; however, they can become unwieldy to manage as their size and complexity increase. This article presents a crowd-verification approach for the visualization and expansion of biological networks. Web-based graphical interfaces allow visualization of causal and correlative biological relationships represented using Biological Expression Language (BEL). Crowdsourcing principles enable participants to communally annotate these relationships based on literature evidences. Gamification principles are incorporated to further engage domain experts throughout biology to gather robust peer-reviewed information from which relationships can be identified and verified. The resulting network models will represent the current status of biological knowledge within the defined boundaries, here processes related to human lung disease. These models are amenable to computational analysis. For some period following conclusion of the challenge, the published models will remain available for continuous use and expansion by the scientific community.

scholarly journals Data-dependent visualization of biological networks in the web-browser with NDExEdit

2021 ◽  
Author(s):  
Florian Auer ◽  
Simone Mayer ◽  
Frank Kramer
Keyword(s):  
Biological Networks ◽  
Data Model ◽  
Biological Knowledge ◽  
Network Data ◽  
Web Tool ◽  
Bioinformatics Analyses ◽  
Web Browser ◽  
Web Based ◽  
Biological Entities ◽  
The Web

Networks are a common methodology used to capture increasingly complex associations between biological entities. They serve as a resource of biological knowledge for bioinformatics analyses, and also comprise the subsequent results. However, the interpretation of biological networks is challenging and requires suitable visualizations dependent on the contained information. The most prominent software in the field for the visualization of biological networks is Cytoscape, a desktop modeling environment also including many features for analysis. A further challenge when working with networks is their distribution. Within a typical collaborative workflow, even slight changes of the network data force one to repeat the visualization step as well. Also, just minor adjustments to the visual representation not only need the networks to be transferred back and forth. Collaboration on the same resources requires specific infrastructure to avoid redundancies, or worse, the corruption of the data. A well-established solution is provided by the NDEx platform where users can upload a network, share it with selected colleagues or make it publicly available. NDExEdit is a web-based application where simple changes can be made to biological networks within the browser, and which does not require installation. With our tool, plain networks can be enhanced easily for further usage in presentations and publications. Since the network data is only stored locally within the web browser, users can edit their private networks without concerns of unintentional publication. The web tool is designed to conform to the Cytoscape Exchange (CX) format as a data model, which is used for the data transmission by both tools, Cytoscape and NDEx. Therefore the modified network can be exported as a compatible CX file, additionally to standard image formats like PNG and JPEG.

scholarly journals Enhancement of COPD biological networks using a web-based collaboration interface

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 32 ◽  
Author(s):  
◽  
Stéphanie Boué ◽  
Brett Fields ◽  
Julia Hoeng ◽  
Jennifer Park ◽  
...  
Keyword(s):  
Biological Networks ◽  
Biological Network ◽  
Network Models ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Web Based ◽  
Data Driven Approach ◽  
Transcriptomics Data ◽  
Model Set ◽  
Biological Representation

The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website (https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be used to help understand the mechanisms related to lung pathobiology. Registered users of the website can freely browse and download the networks.

scholarly journals Tailored graphical lasso for data integration in gene network reconstruction

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Camilla Lingjærde ◽  
Tonje G. Lien ◽  
Ørnulf Borgan ◽  
Helga Bergholtz ◽  
Ingrid K. Glad
Keyword(s):  
Biological Networks ◽  
Prior Information ◽  
Network Inference ◽  
Graphical Model ◽  
Network Models ◽  
R Package ◽  
Biological Information ◽  
Graphical Lasso ◽  
Special Cases ◽  
The Matrix

Abstract Background Identifying gene interactions is a topic of great importance in genomics, and approaches based on network models provide a powerful tool for studying these. Assuming a Gaussian graphical model, a gene association network may be estimated from multiomic data based on the non-zero entries of the inverse covariance matrix. Inferring such biological networks is challenging because of the high dimensionality of the problem, making traditional estimators unsuitable. The graphical lasso is constructed for the estimation of sparse inverse covariance matrices in such situations, using $$L_1$$ L 1 -penalization on the matrix entries. The weighted graphical lasso is an extension in which prior biological information from other sources is integrated into the model. There are however issues with this approach, as it naïvely forces the prior information into the network estimation, even if it is misleading or does not agree with the data at hand. Further, if an associated network based on other data is used as the prior, the method often fails to utilize the information effectively. Results We propose a novel graphical lasso approach, the tailored graphical lasso, that aims to handle prior information of unknown accuracy more effectively. We provide an R package implementing the method, . Applying the method to both simulated and real multiomic data sets, we find that it outperforms the unweighted and weighted graphical lasso in terms of all performance measures we consider. In fact, the graphical lasso and weighted graphical lasso can be considered special cases of the tailored graphical lasso, and a parameter determined by the data measures the usefulness of the prior information. We also find that among a larger set of methods, the tailored graphical is the most suitable for network inference from high-dimensional data with prior information of unknown accuracy. With our method, mRNA data are demonstrated to provide highly useful prior information for protein–protein interaction networks. Conclusions The method we introduce utilizes useful prior information more effectively without involving any risk of loss of accuracy should the prior information be misleading.

scholarly journals Tailored Graphical Lasso for Data Integration in Gene Network Reconstruction

2020 ◽  
Author(s):  
Camilla Lingjærde ◽  
Tonje G Lien ◽  
Ørnulf Borgan ◽  
Ingrid K Glad
Keyword(s):  
Graphical Models ◽  
Biological Networks ◽  
Prior Information ◽  
Graphical Model ◽  
Network Models ◽  
R Package ◽  
Biological Information ◽  
Graphical Lasso ◽  
Special Cases ◽  
The Matrix

AbstractBackgroundIdentifying gene interactions is a topic of great importance in genomics, and approaches based on network models provide a powerful tool for studying these. Assuming a Gaussian graphical model, a gene association network may be estimated from multiomic data based on the non-zero entries of the inverse covariance matrix. Inferring such biological networks is challenging because of the high dimensionality of the problem, making traditional estimators unsuitable. The graphical lasso is constructed for the estimation of sparse inverse covariance matrices in Gaussian graphical models in such situations, using L1-penalization on the matrix entries. An extension of the graphical lasso is the weighted graphical lasso, in which prior biological information from other (data) sources is integrated into the model through the weights. There are however issues with this approach, as it naïvely forces the prior information into the network estimation, even if it is misleading or does not agree with the data at hand. Further, if an associated network based on other data is used as the prior, weighted graphical lasso often fails to utilize the information effectively.ResultsWe propose a novel graphical lasso approach, the tailored graphical lasso, that aims to handle prior information of unknown accuracy more effectively. We provide an R package implementing the method, tailoredGlasso. Applying the method to both simulated and real multiomic data sets, we find that it outperforms the unweighted and weighted graphical lasso in terms of all performance measures we consider. In fact, the graphical lasso and weighted graphical lasso can be considered special cases of the tailored graphical lasso, and a parameter determined by the data measures the usefulness of the prior information. With our method, mRNA data are demonstrated to provide highly useful prior information for protein-protein interaction networks.ConclusionsThe method we introduce utilizes useful prior information more effectively without involving any risk of loss of accuracy should the prior information be misleading.

scholarly journals Enhancement of COPD biological networks using a web-based collaboration interface

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 32 ◽  
Author(s):  
◽  
Stéphanie Boué ◽  
Brett Fields ◽  
Julia Hoeng ◽  
Jennifer Park ◽  
...  
Keyword(s):  
Biological Networks ◽  
Biological Network ◽  
Network Models ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Web Based ◽  
Data Driven Approach ◽  
Transcriptomics Data ◽  
Model Set ◽  
Biological Representation

The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website (https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be used to help understand the mechanisms related to lung pathobiology. Registered users of the website can freely browse and download the networks.

scholarly journals Making Online Instruction Count: Statistical Reporting of Web-Based Library Instruction Activities

2012 ◽  
Vol 73 (1) ◽  
pp. 33-46 ◽  
Author(s):  
Tim Bottorff ◽  
Andrew Todd
Keyword(s):  
Literature Review ◽  
Online Instruction ◽  
Current Status ◽  
Library Instruction ◽  
Web Based ◽  
Face To Face ◽  
External Stakeholders ◽  
Considerable Confusion

Statistical reporting of library instruction (LI) activities has historically focused on measures relevant to face-to-face (F2F) settings. However, newer forms of LI conducted in the online realm may be difficult to count in traditional ways, leading to inaccurate reporting to both internal and external stakeholders. A thorough literature review is combined with the results of an investigative survey to reveal the current status of reporting such activities. The results reveal considerable confusion about the reporting of Web-based LI activities, even though a number of librarians are devoting significant amounts of time to this important and growing area of librarianship.

scholarly journals The Principle of Continuous Biological Information Flow as the Fundamental Foundation for the Biological Sciences. Implications for Ageing Research

2021 ◽  
Author(s):  
Xavi Marsellach
Keyword(s):  
Biological Sciences ◽  
Biological Information ◽  
Biological Knowledge ◽  
Phenotypic Characteristics ◽  
Apparent Contradiction ◽  
Current State ◽  
The Face ◽  
Unique Definition ◽  
Definition Of ◽  
Flow Of Information

The current state of biological knowledge contains an unresolved paradox: life as a continuity in the face of the phenomena of ageing. In this manuscript I propose a theoretical framework that offers a solution for this apparent contradiction. The framework proposed is based on a rethinking of what ageing is at a molecular level, as well as on a rethinking of the mechanisms in charge of the flow of information from one generation to the following ones. I propose an information-based conception of ageing instead of the widely accepted damage-based conception of ageing and propose a full recovery of the chromosome theory of inheritance to describe the intergenerational flow of information. Altogether the proposed framework allows a precise and unique definition of what life is: a continuous flow of biological information. The proposed framework also implies that ageing is merely a consequence of the way in which epigenetically-coded phenotypic characteristics are passed from one generation to the next ones.

scholarly journals NetPyNE, a tool for data-driven multiscale modeling of brain circuits

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Salvador Dura-Bernal ◽  
Benjamin A Suter ◽  
Padraig Gleeson ◽  
Matteo Cantarelli ◽  
Adrian Quintana ◽  
...  
Keyword(s):  
Multiple Scales ◽  
Network Models ◽  
Brain Regions ◽  
Data Driven ◽  
Model Parameters ◽  
Biophysical Modeling ◽  
Neuron Network ◽  
Graphical Interfaces ◽  
Information Theoretic Measures ◽  
High Level

Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing and disparate experimental datasets at multiple scales. The NetPyNE tool (www.netpyne.org) provides both programmatic and graphical interfaces to develop data-driven multiscale network models in NEURON. NetPyNE clearly separates model parameters from implementation code. Users provide specifications at a high level via a standardized declarative language, for example connectivity rules, to create millions of cell-to-cell connections. NetPyNE then enables users to generate the NEURON network, run efficiently parallelized simulations, optimize and explore network parameters through automated batch runs, and use built-in functions for visualization and analysis – connectivity matrices, voltage traces, spike raster plots, local field potentials, and information theoretic measures. NetPyNE also facilitates model sharing by exporting and importing standardized formats (NeuroML and SONATA). NetPyNE is already being used to teach computational neuroscience students and by modelers to investigate brain regions and phenomena.

Ontology Learning from a Domain Web Corpus

Web Mining ◽  
2011 ◽  
pp. 69-98 ◽  
Author(s):  
Roberto Navigli
Keyword(s):  
World Wide ◽  
Shared Vision ◽  
Domain Ontology ◽  
Virtual Organizations ◽  
Semantic Interpretation ◽  
Application Domain ◽  
Web Based ◽  
Domain Experts ◽  
Usable Knowledge ◽  
The World

Domain ontologies are widely recognized as a key element for the so-called semantic Web, an improved, “semantic aware” version of the World Wide Web. Ontologies define concepts and interrelationships in order to provide a shared vision of a given application domain. Despite the significant amount of work in the field, ontologies are still scarcely used in Web-based applications. One of the main problems is the difficulty in identifying and defining relevant concepts within the domain. In this chapter, we provide an approach to the problem, defining a method and a tool, OntoLearn, aimed at the extraction of knowledge from Websites, and more generally from documents shared among the members of virtual organizations, to support the construction of a domain ontology. Exploiting the idea that a corpus of documents produced by a community is the most representative (although implicit) repository of concepts, the method extracts a terminology, provides a semantic interpretation of relevant terms and populates the domain ontology in an automatic manner. Finally, further manual corrections are required from domain experts in order to achieve a rich and usable knowledge resource.

Ideas on the Future of Intelligent Web-Based E-Learning

2018 ◽  
pp. 2274-2287
Author(s):  
Utku Kose
Keyword(s):  
Artificial Intelligence ◽  
Teaching And Learning ◽  
Current Status ◽  
Learning Approaches ◽  
Learning Tools ◽  
Web Based ◽  
The Future ◽  
Teachers And Students ◽  
Important Approach ◽  
E Learning

With the outstanding improvements in technology, the number of e-learning applications has increased greatly. This increment is associated with awareness levels of educational institutions on the related improvements and the power of communication and computer technologies to ensure effective and efficient teaching and learning experiences for teachers and students. Consequently, there is a technological flow that changes the standards of e-learning processes and provides better ways to obtain desired educational objectives. When we consider today's widely used technological factors, Web-based e-learning approaches have a special role in directing the educational standards. Improvements among m-learning applications and the popularity of the Artificial Intelligence usage for educational works have given great momentum to this orientation. In this sense, this chapter provides some ideas on the future of intelligent Web-based e-learning applications by thinking on the current status of the literature. As it is known, current trends in developing Artificial Intelligence-supported e-learning tools continue to shape the future of e-learning. Therefore, it is an important approach to focus on the future. The author thinks that the chapter will be a brief but effective enough reference for similar works, which focus on the future of Artificial Intelligence-supported distance education and e-learning.

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