A graphic tool for curating molecular interaction networks from the literature (original) (raw)
Related papers
ProLiVis: Protein-Protein Interaction Literature Visualization System
2021
Summary: We provide a visualization model that targets the visualization of Protein-Protein Interactions(PPI) and combines it with a super view based on publications and methods to extract interactions. Although there are several existing tools, our model considers the existing literature and is capable to demonstrate all interactions for the selected organisms. In our model, we propose a three-level visualization concept for the PPI networks with the current state-of-the-art studies based on several organisms. And, we abstract of overall network based on two perspectives; "experimental method types of each interaction" and "ownership with the publication". We claim that it is more efficient to work on our proposed layout rather than parsing text files or databases. For that way, we plan to support the existing visuals with complementary outsourced information from the existing knowledge base. Availability: ProLiVis is available under the MIT License. Source code...
A web-based protein interaction network visualizer
BMC Bioinformatics, 2014
Background: Interaction between proteins is one of the most important mechanisms in the execution of cellular functions. The study of these interactions has provided insight into the functioning of an organism's processes. As of October 2013, Homo sapiens had over 170000 Protein-Protein interactions (PPI) registered in the Interologous Interaction Database, which is only one of the many public resources where protein interactions can be accessed. These numbers exemplify the volume of data that research on the topic has generated. Visualization of large data sets is a well known strategy to make sense of information, and protein interaction data is no exception. There are several tools that allow the exploration of this data, providing different methods to visualize protein network interactions. However, there is still no native web tool that allows this data to be explored interactively online.
Biological network extraction from scientific literature: state of the art and challenges
Briefings in Bioinformatics, 2013
Networks of molecular interactions explain complex biological processes and all known information on molecular events is contained in a number of public repositories including the scientific literature. Metabolic and signalling pathways are often viewed separately, even though both types are composed of interactions involving proteins and other chemical entities.
GeneWays: a system for extracting, analyzing, visualizing, and integrating molecular pathway data
Journal of Biomedical Informatics, 2004
The immense growth in the volume of research literature and experimental data in the field of molecular biology calls for efficient automatic methods to capture and store information. In recent years, several groups have worked on specific problems in this area, such as automated selection of articles pertinent to molecular biology, or automated extraction of information using natural-language processing, information visualization, and generation of specialized knowledge bases for molecular biology. GeneWays is an integrated system that combines several such subtasks. It analyzes interactions between molecular substances, drawing on multiple sources of information to infer a consensus view of molecular networks. GeneWays is designed as an open platform, allowing researchers to query, review, and critique stored information.
Visualization of protein interaction networks: problems and solutions
BMC Bioinformatics, 2013
Background: Visualization concerns the representation of data visually and is an important task in scientific research. Protein-protein interactions (PPI) are discovered using either wet lab techniques, such mass spectrometry, or in silico predictions tools, resulting in large collections of interactions stored in specialized databases. The set of all interactions of an organism forms a protein-protein interaction network (PIN) and is an important tool for studying the behaviour of the cell machinery. Since graphic representation of PINs may highlight important substructures, e.g. protein complexes, visualization is more and more used to study the underlying graph structure of PINs. Although graphs are well known data structures, there are different open problems regarding PINs visualization: the high number of nodes and connections, the heterogeneity of nodes (proteins) and edges (interactions), the possibility to annotate proteins and interactions with biological information extracted by ontologies (e.g. Gene Ontology) that enriches the PINs with semantic information, but complicates their visualization.
PathBinder – text empirics and automatic extraction of biomolecular interactions
BMC Bioinformatics, 2009
Motivation The increasingly large amount of free, online biological text makes automatic interaction extraction correspondingly attractive. Machine learning is one strategy that works by uncovering and using useful properties that are implicit in the text. However these properties are usually not reported in the literature explicitly. By investigating specific properties of biological text passages in this paper, we aim to facilitate an alternative strategy, the use of text empirics, to support mining of biomedical texts for biomolecular interactions. We report on our application of this approach, and also report some empirical findings about an important class of passages. These may be useful to others who may also wish to use the empirical properties we describe. Results We manually analyzed syntactic and semantic properties of sentences likely to describe interactions between biomolecules. The resulting empirical data were used to design an algorithm for the PathBinder system to ...
MINERVA—a platform for visualization and curation of molecular interaction networks
npj Systems Biology and Applications
Our growing knowledge about various molecular mechanisms is becoming increasingly more structured and accessible. Different repositories of molecular interactions and available literature enable construction of focused and high-quality molecular interaction networks. Novel tools for curation and exploration of such networks are needed, in order to foster the development of a systems biology environment. In particular, solutions for visualization, annotation and data cross-linking will facilitate usage of networkencoded knowledge in biomedical research. To this end we developed the MINERVA (Molecular Interaction NEtwoRks VisuAlization) platform, a standalone webservice supporting curation, annotation and visualization of molecular interaction networks in Systems Biology Graphical Notation (SBGN)-compliant format. MINERVA provides automated content annotation and verification for improved quality control. The end users can explore and interact with hosted networks, and provide direct feedback to content curators. MINERVA enables mapping drug targets or overlaying experimental data on the visualized networks. Extensive export functions enable downloading areas of the visualized networks as SBGN-compliant models for efficient reuse of hosted networks. The software is available under Affero GPL 3.0 as a Virtual Machine snapshot, Debian package and Docker instance at http://r3lab. uni.lu/web/minerva-website/. We believe that MINERVA is an important contribution to systems biology community, as its architecture enables setup of locally or globally accessible SBGN-oriented repositories of molecular interaction networks. Its functionalities allow overlay of multiple information layers, facilitating exploration of content and interpretation of data. Moreover, annotation and verification workflows of MINERVA improve the efficiency of curation of networks, allowing life-science researchers to better engage in development and use of biomedical knowledge repositories.
A protein interaction extraction system
Pacific Symposium on Biocomputing, 2001
We consider the problem of extracting, manipulating, and managing pathways, especially protein-protein interaction pathways. We discuss here the Protein Interaction Extraction System (PIES). The PIES is contructed on top of three main technologies: Kleisli, BioNLP, and Graphviz. Kleisli is a broad-scale data integration system that we use for downloading Medline abstracts and for general manipulation and management of pathway/interaction databases. BioNLP is a natural language-based information extraction module that we use for analysing Medline abstracts and to extract precise protein-protein and other interaction information. Graphviz is a graphically layout package developed for directed graphs that we use for visualization of the extracted pathways. The PIES can be augmented with means for extracting protein interaction information from sequence databases, for example, by using Kleisli's power to integrate sequence comparison tools to detect gene fusion events in sequence databases.
VisANT: an online visualization and analysis tool for biological interaction data
BMC bioinformatics, 2004
New techniques for determining relationships between biomolecules of all types--genes, proteins, noncoding DNA, metabolites and small molecules--are now making a substantial contribution to the widely discussed explosion of facts about the cell. The data generated by these techniques promote a picture of the cell as an interconnected information network, with molecular components linked with one another in topologies that can encode and represent many features of cellular function. This networked view of biology brings the potential for systematic understanding of living molecular systems. We present VisANT, an application for integrating biomolecular interaction data into a cohesive, graphical interface. This software features a multi-tiered architecture for data flexibility, separating back-end modules for data retrieval from a front-end visualization and analysis package. VisANT is a freely available, open-source tool for researchers, and offers an online interface for a large ra...
Database : the journal of biological databases and curation, 2015
Capture and representation of scientific knowledge in a structured format are essential to improve the understanding of biological mechanisms involved in complex diseases. Biological knowledge and knowledge about standardized terminologies are difficult to capture from literature in a usable form. A semi-automated knowledge extraction workflow is presented that was developed to allow users to extract causal and correlative relationships from scientific literature and to transcribe them into the computable and human readable Biological Expression Language (BEL). The workflow combines state-of-the-art linguistic tools for recognition of various entities and extraction of knowledge from literature sources. Unlike most other approaches, the workflow outputs the results to a curation interface for manual curation and converts them into BEL documents that can be compiled to form biological networks. We developed a new semi-automated knowledge extraction workflow that was designed to captu...