scholarly journals Systems Biology Graphical Notation: Entity Relationship language Level 1 Version 2

2015 ◽  
Vol 12 (2) ◽  
pp. 281-339 ◽  
Author(s):  
Anatoly Sorokin ◽  
Nicolas Le Novère ◽  
Augustin Luna ◽  
Tobias Czauderna ◽  
Emek Demir ◽  
...  
Keyword(s):  
Systems Biology ◽  
Biological Knowledge ◽  
Graphical Notation ◽  
Levels Of Detail ◽  
Temporal Aspects ◽  
Biochemical Systems ◽  
Community Effort ◽  
Entity Relationship ◽  
Biological Entities ◽  
Biological Literature

Summary The Systems Biological Graphical Notation (SBGN) is an international community effort for standardized graphical representations of biological pathways and networks. The goal of SBGN is to provide unambiguous pathway and network maps for readers with different scientific backgrounds as well as to support efficient and accurate exchange of biological knowledge between different research communities, industry, and other players in systems biology. Three SBGN languages, Process Description (PD), Entity Relationship (ER) and Activity Flow (AF), allow for the representation of different aspects of biological and biochemical systems at different levels of detail.The SBGN Entity Relationship language (ER) represents biological entities and their interactions and relationships within a network. SBGN ER focuses on all potential relationships between entities without considering temporal aspects. The nodes (elements) describe biological entities, such as proteins and complexes. The edges (connections) provide descriptions of interactions and relationships (or influences), e.g., complex formation, stimulation and inhibition. Among all three languages of SBGN, ER is the closest to protein interaction networks in biological literature and textbooks, but its well-defined semantics offer a superior precision in expressing biological knowledge.

scholarly journals Systems Biology Graphical Notation: Process Description language Level 1 Version 1.3

2015 ◽  
Vol 12 (2) ◽  
pp. 213-280 ◽  
Author(s):  
Stuart Moodie ◽  
Nicolas Le Novère ◽  
Emek Demir ◽  
Huaiyu Mi ◽  
Alice Villéger
Keyword(s):  
Systems Biology ◽  
Biological Knowledge ◽  
Biological Interactions ◽  
Description Language ◽  
Regulatory Pathways ◽  
Graphical Notation ◽  
Biochemical Systems ◽  
Community Effort ◽  
Entity Relationship ◽  
Biological Entities

Summary The Systems Biological Graphical Notation (SBGN) is an international community effort for standardized graphical representations of biological pathways and networks. The goal of SBGN is to provide unambiguous pathway and network maps for readers with different scientific backgrounds as well as to support efficient and accurate exchange of biological knowledge between different research communities, industry, and other players in systems biology. Three SBGN languages, Process Description (PD), Entity Relationship (ER) and Activity Flow (AF), allow for the representation of different aspects of biological and biochemical systems at different levels of detail.The SBGN Process Description language represents biological entities and processes between these entities within a network. SBGN PD focuses on the mechanistic description and temporal dependencies of biological interactions and transformations. The nodes (elements) are split into entity nodes describing, e.g., metabolites, proteins, genes and complexes, and process nodes describing, e.g., reactions and associations. The edges (connections) provide descriptions of relationships (or influences) between the nodes, such as consumption, production, stimulation and inhibition. Among all three languages of SBGN, PD is the closest to metabolic and regulatory pathways in biological literature and textbooks, but its well-defined semantics offer a superior precision in expressing biological knowledge.

scholarly journals Systems Biology Graphical Notation: Process Description language Level 1 Version 2.0

2019 ◽  
Vol 16 (2) ◽  
Author(s):  
Adrien Rougny ◽  
Vasundra Touré ◽  
Stuart Moodie ◽  
Irina Balaur ◽  
Tobias Czauderna ◽  
...  
Keyword(s):  
Systems Biology ◽  
Biological Knowledge ◽  
Biological Interactions ◽  
Regulatory Pathways ◽  
Graphical Notation ◽  
Biochemical Systems ◽  
Community Effort ◽  
Version 2.0 ◽  
Level 1 ◽  
Entity Relationship

AbstractThe Systems Biology Graphical Notation (SBGN) is an international community effort that aims to standardise the visualisation of pathways and networks for readers with diverse scientific backgrounds as well as to support an efficient and accurate exchange of biological knowledge between disparate research communities, industry, and other players in systems biology. SBGN comprises the three languages Entity Relationship, Activity Flow, and Process Description (PD) to cover biological and biochemical systems at distinct levels of detail. PD is closest to metabolic and regulatory pathways found in biological literature and textbooks. Its well-defined semantics offer a superior precision in expressing biological knowledge. PD represents mechanistic and temporal dependencies of biological interactions and transformations as a graph. Its different types of nodes include entity pools (e.g. metabolites, proteins, genes and complexes) and processes (e.g. reactions, associations and influences). The edges describe relationships between the nodes (e.g. consumption, production, stimulation and inhibition). This document details Level 1 Version 2.0 of the PD specification, including several improvements, in particular: 1) the addition of the equivalence operator, subunit, and annotation glyphs, 2) modification to the usage of submaps, and 3) updates to clarify the use of various glyphs (i.e. multimer, empty set, and state variable).

scholarly journals Systems Biology Graphical Notation: Activity Flow language Level 1 Version 1.2

2015 ◽  
Vol 12 (2) ◽  
pp. 340-381 ◽  
Author(s):  
Huaiyu Mi ◽  
Falk Schreiber ◽  
Stuart Moodie ◽  
Tobias Czauderna ◽  
Emek Demir ◽  
...  
Keyword(s):  
Systems Biology ◽  
Biological Activities ◽  
Negative Influence ◽  
Positive Influence ◽  
Binding Activity ◽  
Biological Knowledge ◽  
Protein Kinase Activity ◽  
Graphical Notation ◽  
Biochemical Systems ◽  
Entity Relationship

Summary The Systems Biological Graphical Notation (SBGN) is an international community effort for standardized graphical representations of biological pathways and networks. The goal of SBGN is to provide unambiguous pathway and network maps for readers with different scientific backgrounds as well as to support efficient and accurate exchange of biological knowledge between different research communities, industry, and other players in systems biology. Three SBGN languages, Process Description (PD), Entity Relationship (ER) and Activity Flow (AF), allow for the representation of different aspects of biological and biochemical systems at different levels of detail.The SBGN Activity Flow language represents the influences of activities among various entities within a network. Unlike SBGN PD and ER that focus on the entities and their relationships with others, SBGN AF puts the emphasis on the functions (or activities) performed by the entities, and their effects to the functions of the same or other entities. The nodes (elements) describe the biological activities of the entities, such as protein kinase activity, binding activity or receptor activity, which can be easily mapped to Gene Ontology molecular function terms. The edges (connections) provide descriptions of relationships (or influences) between the activities, e.g., positive influence and negative influence. Among all three languages of SBGN, AF is the closest to signaling pathways in biological literature and textbooks, but its well-defined semantics offer a superior precision in expressing biological knowledge.

sbgntikz—a TikZ library to draw SBGN maps

2019 ◽  
Vol 35 (21) ◽  
pp. 4499-4500 ◽  
Author(s):  
Adrien Rougny
Keyword(s):  
Systems Biology ◽  
Operating Systems ◽  
Biological Activities ◽  
The Other ◽  
Supplementary Information ◽  
Markup Language ◽  
Graphical Notation ◽  
Supplementary Material ◽  
Entity Relationship ◽  
Biological Entities

Abstract Summary The systems biology graphical notation (SBGN) has emerged as the main standard to represent biological maps graphically. It comprises three complementary languages: Process Description, for detailed biomolecular processes; Activity Flow, for influences of biological activities and Entity Relationship, for independent relations shared among biological entities. On the other hand, TikZ is one of the most commonly used package to ‘program’ graphics within TEX/LATEX. Here, we present sbgntikz, a TikZ library that allows drawing and customizing SBGN maps directly into TEX/LATEX documents, using the TikZ language. sbgntikz supports all glyphs of the three SBGN languages, and offers options that facilitate the drawing of complex glyph assembly within TikZ. Furthermore, sbgntikz is provided together with a converter that allows transforming any SBGN map stored under the SBGN Markup Language into a TikZ picture, or rendering it directly into a PDF file. Availability and implementation sbgntikz, the SBGN-ML to sbgntikz converter, as well as a complete documentation can be freely downloaded from https://github.com/Adrienrougny/sbgntikz/. The library and the converter are compatible with all recent operating systems, including Windows, MacOS, and all common Linux distributions. Supplementary information Supplementary material is available at Bioinformatics online.

scholarly journals Systems Biology Graphical Notation: Entity Relationship language Level 1 (Version 1.2)

2011 ◽  
Author(s):  
Nicolas Le Novere ◽  
Emek Demir ◽  
Huaiyu Mi ◽  
Stuart Moodie ◽  
Alice Villeger
Keyword(s):  
Systems Biology ◽  
Graphical Notation ◽  
Language Level ◽  
Level 1 ◽  
Entity Relationship

scholarly journals Systems Biology Graphical Notation: Entity Relationship language Level 1 (Version 1.2)

2011 ◽  
Author(s):  
Nicolas Le Novere ◽  
Nicolas Le Novere ◽  
Emek Demir ◽  
Huaiyu Mi ◽  
Stuart Moodie ◽  
...  
Keyword(s):  
Systems Biology ◽  
Graphical Notation ◽  
Language Level ◽  
Level 1 ◽  
Entity Relationship

scholarly journals Systems biology graphical notation markup language (SBGNML) version 0.3

2020 ◽  
Vol 17 (2-3) ◽  
Author(s):  
Frank T. Bergmann ◽  
Tobias Czauderna ◽  
Ugur Dogrusoz ◽  
Adrien Rougny ◽  
Andreas Dräger ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Systems Biology ◽  
Programming Languages ◽  
Biological Knowledge ◽  
Markup Language ◽  
Graphical Notation ◽  
Accurate Representation ◽  
Previous Version ◽  
Computer Scientists ◽  
Extensible Markup

AbstractThis document defines Version 0.3 Markup Language (ML) support for the Systems Biology Graphical Notation (SBGN), a set of three complementary visual languages developed for biochemists, modelers, and computer scientists. SBGN aims at representing networks of biochemical interactions in a standard, unambiguous way to foster efficient and accurate representation, visualization, storage, exchange, and reuse of information on all kinds of biological knowledge, from gene regulation, to metabolism, to cellular signaling. SBGN is defined neutrally to programming languages and software encoding; however, it is oriented primarily towards allowing models to be encoded using XML, the eXtensible Markup Language. The notable changes from the previous version include the addition of attributes for better specify metadata about maps, as well as support for multiple maps, sub-maps, colors, and annotations. These changes enable a more efficient exchange of data to other commonly used systems biology formats (e. g., BioPAX and SBML) and between tools supporting SBGN (e. g., CellDesigner, Newt, Krayon, SBGN-ED, STON, cd2sbgnml, and MINERVA). More details on SBGN and related software are available at http://sbgn.org. With this effort, we hope to increase the adoption of SBGN in bioinformatics tools, ultimately enabling more researchers to visualize biological knowledge in a precise and unambiguous manner.

scholarly journals Systems Biology Graphical Notation: Entity Relationship language Level 1

2010 ◽  
Author(s):  
Nicolas Le Novère ◽  
Stuart Moodie ◽  
Anatoly Sorokin ◽  
Falk Schreiber ◽  
Huaiyu Mi
Keyword(s):  
Systems Biology ◽  
Graphical Notation ◽  
Language Level ◽  
Level 1 ◽  
Entity Relationship

scholarly journals Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Li-Hsin Cheng ◽  
Te-Cheng Hsu ◽  
Che Lin
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Feature Selection ◽  
Systems Biology ◽  
Ensemble Learning ◽  
Microarray Data ◽  
Deep Neural Network ◽  
Prediction Models ◽  
Biological Knowledge ◽  
Prognosis Prediction

AbstractBreast cancer is a heterogeneous disease. To guide proper treatment decisions for each patient, robust prognostic biomarkers, which allow reliable prognosis prediction, are necessary. Gene feature selection based on microarray data is an approach to discover potential biomarkers systematically. However, standard pure-statistical feature selection approaches often fail to incorporate prior biological knowledge and select genes that lack biological insights. Besides, due to the high dimensionality and low sample size properties of microarray data, selecting robust gene features is an intrinsically challenging problem. We hence combined systems biology feature selection with ensemble learning in this study, aiming to select genes with biological insights and robust prognostic predictive power. Moreover, to capture breast cancer's complex molecular processes, we adopted a multi-gene approach to predict the prognosis status using deep learning classifiers. We found that all ensemble approaches could improve feature selection robustness, wherein the hybrid ensemble approach led to the most robust result. Among all prognosis prediction models, the bimodal deep neural network (DNN) achieved the highest test performance, further verified by survival analysis. In summary, this study demonstrated the potential of combining ensemble learning and bimodal DNN in guiding precision medicine.

scholarly journals A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research

2021 ◽  
Vol 22 (6) ◽  
pp. 2822
Author(s):  
Efstathios Iason Vlachavas ◽  
Jonas Bohn ◽  
Frank Ückert ◽  
Sylvia Nürnberg
Keyword(s):  
Cancer Research ◽  
Clinical Information ◽  
Disease Diagnosis ◽  
Molecular Data ◽  
Molecular Networks ◽  
Biological Knowledge ◽  
Omics Data ◽  
Translational Cancer Research ◽  
Using Data ◽  
Biological Entities

Recent advances in sequencing and biotechnological methodologies have led to the generation of large volumes of molecular data of different omics layers, such as genomics, transcriptomics, proteomics and metabolomics. Integration of these data with clinical information provides new opportunities to discover how perturbations in biological processes lead to disease. Using data-driven approaches for the integration and interpretation of multi-omics data could stably identify links between structural and functional information and propose causal molecular networks with potential impact on cancer pathophysiology. This knowledge can then be used to improve disease diagnosis, prognosis, prevention, and therapy. This review will summarize and categorize the most current computational methodologies and tools for integration of distinct molecular layers in the context of translational cancer research and personalized therapy. Additionally, the bioinformatics tools Multi-Omics Factor Analysis (MOFA) and netDX will be tested using omics data from public cancer resources, to assess their overall robustness, provide reproducible workflows for gaining biological knowledge from multi-omics data, and to comprehensively understand the significantly perturbed biological entities in distinct cancer types. We show that the performed supervised and unsupervised analyses result in meaningful and novel findings.

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