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BioSystems: Science & Companies
BioSystems is a monthly peer-reviewed scientific journal covering experimental, computational, and theoretical research that links biology, evolution, and the information processing sciences. It was established in 1967 as Currents in Modern Biology by Robert G. Grenell and published by North-Holland Publishing Company out of Amsterdam until North-Holland…
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BioSystems topic overview
The analysis highlights Science and Companies as prominent areas in the source structure around BioSystems.
Related topics
42
Source areas
3
Connected nodes
45
Extracted relationships
79
Concept neighborhoods
19
Bridge connections
45
What this topic covers Research coverage
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Abstracting and indexing · 18 topics
Overview · 14 topics
Special issues · 10 topics
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Publisher
Elsevier
CODEN
BSYMBO
Discipline
Systems biology, evolution, computer modeling, information processing
Edited by
Abir Igamberdiev
Former names
Currents in Modern Biology; Currents in Modern Biology: Bio Systems
Frequency
Monthly
Explore all related topics Closing gaps
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
How BioSystems connects Entity context
Entity context groups extracted connections by relationship type. Counts show recurrence in the analyzed source; the examples are research signals, not claims about importance.
The extracted context around BioSystems shows recurring relationship patterns in the source. For example, BioSystems → Abir, Bibcode, Boris Kozo-Polyansky, Editorial, George, Gordon, Igamberdiev, Konstantin Merezhkovsky, Lock-gray-alt-2, Lock-green, Lock-red-alt-2, Lynn Margulis, Mikhailovsky, PMID, Progressive Evolution, Richard, Symbiogenesis, The, Wikisource-logo Another extracted example is BioSystems → Abir, Bibcode, Biological, Biological Thermodynamics, Bridging, Cottam, Elek, English, Ervin Bauer, George, Gábor, Igamberdiev, Mikhailovsky, Miklós, Müller, PMID, Ron, The. Use these groups to spot repeated connection types before inspecting the individual relationships.
Relationships are represented as Subject → Predicate → Object triples. Confidence describes extraction strength, not the real-world importance of a relationship; lower-confidence rows may be contextual rather than explicit.
TTTA extracted 79 structured relationships around BioSystems. Examples in this analysis include BioSystems → CODEN → BSYMBO and BioSystems → Discipline → Systems biology, evolution, computer modeling, information processing. The table shows each extracted connection, where it came from and its confidence.
Subject
Predicate
Object
Confidence
Src
BioSystems
CODEN
BSYMBO
1.00
infobox
BioSystems
Discipline
Systems biology, evolution, computer modeling, information processing
1.00
infobox
BioSystems
Edited by
Abir Igamberdiev
1.00
infobox
BioSystems
Former names
Currents in Modern Biology; Currents in Modern Biology: Bio Systems
Concept neighborhoods group terms that sit near one another in the extracted graph. They are useful for discovering adjacent language and context, but proximity does not by itself imply a factual relationship.
The concept neighborhoods around BioSystems bring nearby vocabulary together. In this analysis, examples include Bibcode, Doi and Biology. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
BioSystems
Bibcode
Doi
Biology
Journal
Bio
Information
Links
Monthly
Processing
Systems
Currents
Evolution
biosystems
Bibcode
Doi
Biology
Journal
Bio
Information
Links
Monthly
Processing
Systems
Currents
Evolution
biology
Currents
Journal
Modern
Theoretical
Special
Bio
Information
Language
Links
Monthly
Processing
Systems
evolution
Information
Links
Monthly
Processing
Articles
Symbiogenesis
Abir
Igamberdiev
Special
Bio
Elsevier
Factor
journal citation reports
Citation
Factor
Impact
Journal
Theoretical
Articles
Evolution
George
Lynn
Symbiogenesis
Biological
Abir
computational biology
Currents
Journal
Modern
Theoretical
Special
Bio
Information
Language
Links
Monthly
Processing
Systems
biological abstracts
Computation
Thermodynamics
Abir
Igamberdiev
Special
Language
Articles
Biology
English
Journal
Bio
Elsevier
information processing
Links
Monthly
Processing
Bio
Elsevier
Factor
Impact
Language
Systems
Articles
Computation
Currents
Connections between topic areas Semantic bridges
Semantic bridges connect parts of the graph that would otherwise be more separate. A bridge is a structural signal in this analysis, not a claim that the two topic areas are equivalent.
For BioSystems, one of the stronger structural bridges in this analysis connects BioSystems with Abstracting and indexing. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.
BioSystems
Nodes46
Edges45
Triples79
Avg. degree1.96
Density0.043478
Components1
Source & methodology
TTTA analyzes the structure around BioSystems to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Science & Companies, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
