scholarly journals Levels and building blocks—towards a domain granularity framework for the life sciences

2017 ◽  
Author(s):  
Lars Vogt
Keyword(s):  
Life Sciences ◽  
Building Blocks ◽  
Formal Ontology ◽  
Basic Formal Ontology ◽  
Online Data ◽  
Data Repositories ◽  
Comprehensive Information ◽  
Material Entity ◽  
Information Framework ◽  
Require Data

The use of online data repositories and the establishment of new data standards that require data to be computer-parsable so that algorithms can reason over them have become increasingly important with the emergence of high-throughput technologies, Big Data and eScience. As a consequence, there is an increasing need for new approaches for organizing and structuring data from various sources into integrated hierarchies of levels of entities that facilitate algorithm-based approaches for data exploration, data comparison and analysis. In this paper I contrast various accounts of the level idea and resulting hierarchies published by philosophers and natural scientists with the more formal approaches of theories of granularity published by information scientists and ontology researchers. I discuss the shortcomings of the former and argue that the general theory of granularity proposed by Keet circumvents these problems and allows the integration of various different hierarchies into a domain granularity framework. I introduce the concept of general building blocks, which gives rise to a hierarchy of levels that can be formally characterized by Keet's theory. This hierarchy functions as an organizational backbone for integrating various other hierarchies that I briefly discuss, resulting in a general domain granularity framework for the life sciences. I also discuss the implicit consequences of this granularity framework for the structure of top-level categories of 'material entity' of the Basic Formal Ontology. The here suggested domain granularity framework is meant to provide the basis on which a more comprehensive information framework for the life sciences can be developed.

scholarly journals Levels and building blocks—towards a domain granularity framework for the life sciences

2017 ◽  
Author(s):  
Lars Vogt
Keyword(s):  
Life Sciences ◽  
Building Blocks ◽  
Formal Ontology ◽  
Basic Formal Ontology ◽  
Online Data ◽  
Data Repositories ◽  
Comprehensive Information ◽  
Material Entity ◽  
Information Framework ◽  
Require Data

The use of online data repositories and the establishment of new data standards that require data to be computer-parsable so that algorithms can reason over them have become increasingly important with the emergence of high-throughput technologies, Big Data and eScience. As a consequence, there is an increasing need for new approaches for organizing and structuring data from various sources into integrated hierarchies of levels of entities that facilitate algorithm-based approaches for data exploration, data comparison and analysis. In this paper I contrast various accounts of the level idea and resulting hierarchies published by philosophers and natural scientists with the more formal approaches of theories of granularity published by information scientists and ontology researchers. I discuss the shortcomings of the former and argue that the general theory of granularity proposed by Keet circumvents these problems and allows the integration of various different hierarchies into a domain granularity framework. I introduce the concept of general building blocks, which gives rise to a hierarchy of levels that can be formally characterized by Keet's theory. This hierarchy functions as an organizational backbone for integrating various other hierarchies that I briefly discuss, resulting in a general domain granularity framework for the life sciences. I also discuss the implicit consequences of this granularity framework for the structure of top-level categories of 'material entity' of the Basic Formal Ontology. The here suggested domain granularity framework is meant to provide the basis on which a more comprehensive information framework for the life sciences can be developed.

scholarly journals Levels metaphor and building blocks—towards a domain granularity framework for the life sciences

2016 ◽  
Author(s):  
Lars Vogt
Keyword(s):  
Life Sciences ◽  
Formal Theory ◽  
Building Blocks ◽  
The Other ◽  
General Domain ◽  
Comprehensive Information ◽  
Initial Basis ◽  
Information Framework

Arranging a heterogeneous collection of entities into a hierarchy of linearly ordered levels (layers or strata) is a general ordering scheme that is a widely used notion for organizing knowledge. On the basis of four specific examples, all of which are relevant in the life sciences, I briefly discuss the diversity of different notions of the underlying levels metaphor. Before I turn to ontology research and Keet's formal theory of granularity, I introduce a specific notion of general building blocks, which gives rise to a hierarchy of levels of building blocks that is intended to function as an organizational backbone for integrating various granular perspectives that are relevant in the life sciences. Each such granular perspective employs its own specific application of the levels metaphor, which is integrated with the other perspectives within a general domain granularity framework for the life sciences. The resulting granularity framework is meant to provide the initial basis on which a desperately required overarching and more comprehensive information framework for the life sciences can be developed.

scholarly journals metaXplor: an interactive viral and microbial metagenomic data manager

GigaScience ◽  
2021 ◽  
Vol 10 (2) ◽  
Author(s):  
Guilhem Sempéré ◽  
Adrien Pétel ◽  
Magsen Abbé ◽  
Pierre Lefeuvre ◽  
Philippe Roumagnac ◽  
...  
Keyword(s):  
Heterogeneous Data ◽  
Metagenomic Data ◽  
Online Data ◽  
Data Repositories ◽  
Ongoing Research ◽  
Efficient Management ◽  
Public Data ◽  
Reference Databases ◽  
Interactive Data ◽  
User Friendly

Abstract Background Efficiently managing large, heterogeneous data in a structured yet flexible way is a challenge to research laboratories working with genomic data. Specifically regarding both shotgun- and metabarcoding-based metagenomics, while online reference databases and user-friendly tools exist for running various types of analyses (e.g., Qiime, Mothur, Megan, IMG/VR, Anvi'o, Qiita, MetaVir), scientists lack comprehensive software for easily building scalable, searchable, online data repositories on which they can rely during their ongoing research. Results metaXplor is a scalable, distributable, fully web-interfaced application for managing, sharing, and exploring metagenomic data. Being based on a flexible NoSQL data model, it has few constraints regarding dataset contents and thus proves useful for handling outputs from both shotgun and metabarcoding techniques. By supporting incremental data feeding and providing means to combine filters on all imported fields, it allows for exhaustive content browsing, as well as rapid narrowing to find specific records. The application also features various interactive data visualization tools, ways to query contents by BLASTing external sequences, and an integrated pipeline to enrich assignments with phylogenetic placements. The project home page provides the URL of a live instance allowing users to test the system on public data. Conclusion metaXplor allows efficient management and exploration of metagenomic data. Its availability as a set of Docker containers, making it easy to deploy on academic servers, on the cloud, or even on personal computers, will facilitate its adoption.

scholarly journals 2-Fluoromalonate Esters: Fluoroaliphatic Building Blocks for the Life Sciences

2014 ◽  
Vol 18 (8) ◽  
pp. 981-992 ◽  
Author(s):  
Antal Harsanyi ◽  
Graham Sandford
Keyword(s):  
Life Sciences ◽  
Building Blocks

Developing a Competitive City through Healthy Decision-Making

2012 ◽  
pp. 808-822
Author(s):  
Ori Gudes ◽  
Elizabeth Kendall ◽  
Tan Yigitcanlar ◽  
Jung Hoon Han ◽  
Virendra Pathak
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Information Science ◽  
Health Planning ◽  
Healthy City ◽  
Web Based ◽  
Comprehensive Information ◽  
Challenges And Opportunities ◽  
Planning Methods ◽  
Information Framework

This chapter investigates the challenges and opportunities associated with planning for a competitive city. The chapter is based on the assumption that a healthy city is a fundamental prerequisite for a competitive city. Thus, it is critical to examine the local determinants of health and factor these into any planning efforts. The main focus of the chapter is on e-health planning by utilising Web-based geographic decision support systems. The proposed novel decision support system would provide a powerful and effective platform for stakeholders to access essential data for decision-making purposes. The chapter also highlights the need for a comprehensive information framework to guide the process of planning for healthy cities. Additionally, it discusses the prospects and constraints of such an approach. In summary, this chapter outlines the potential insights of using an information science-based framework and suggests practical planning methods as part of a broader e-health approach for improving the health characteristics of competitive cities.

scholarly journals CausalMGM: an interactive web-based causal discovery tool

2020 ◽  
Vol 48 (W1) ◽  
pp. W597-W602 ◽  
Author(s):  
Xiaoyu Ge ◽  
Vineet K Raghu ◽  
Panos K Chrysanthis ◽  
Panayiotis V Benos
Keyword(s):  
Observational Data ◽  
High Throughput Sequencing ◽  
Graphical Model ◽  
The Cancer Genome Atlas ◽  
Causal Discovery ◽  
Automated Identification ◽  
Online Data ◽  
Data Repositories ◽  
Web Based ◽  
Cause And Effect

Abstract High-throughput sequencing and the availability of large online data repositories (e.g. The Cancer Genome Atlas and Trans-Omics for Precision Medicine) have the potential to revolutionize systems biology by enabling researchers to study interactions between data from different modalities (i.e. genetic, genomic, clinical, behavioral, etc.). Currently, data mining and statistical approaches are confined to identifying correlates in these datasets, but researchers are often interested in identifying cause-and-effect relationships. Causal discovery methods were developed to infer such cause-and-effect relationships from observational data. Though these algorithms have had demonstrated successes in several biomedical applications, they are difficult to use for non-experts. So, there is a need for web-based tools to make causal discovery methods accessible. Here, we present CausalMGM (http://causalmgm.org/), the first web-based causal discovery tool that enables researchers to find cause-and-effect relationships from observational data. Web-based CausalMGM consists of three data analysis tools: (i) feature selection and clustering; (ii) automated identification of cause-and-effect relationships via a graphical model; and (iii) interactive visualization of the learned causal (directed) graph. We demonstrate how CausalMGM enables an end-to-end exploratory analysis of biomedical datasets, giving researchers a clearer picture of its capabilities.

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