The Molecular Biology Toolkit (MBT): a modular platform for developing molecular visualization applications
The Molecular Biology Toolkit (MBT): a modular platform for developing molecular visualization applications is a research paper published in BMC Bioinformatics (2005). On theSindex it has a DataRank of 0.852. It has been cited 292 times.
Abstract
BackgroundThe large amount of data that are currently produced in the biological sciences can no longer be explored and visualized efficiently with traditional, specialized software. Instead, new capabilities are needed that offer flexibility, rapid application development and deployment as standalone applications or available through the Web.ResultsWe describe a new software toolkit--the Molecular Biology Toolkit (MBT; http://mbt.sdsc.edu)--that enables fast development of applications for protein analysis and visualization. The toolkit is written in Java, thus offering platform-independence and Internet delivery capabilities. Several applications of the toolkit are introduced to illustrate the functionality that can be achieved.ConclusionsThe MBT provides a well-organized assortment of core classes that provide a uniform data model for the description of biological structures and automate most common tasks associated with the development of applications in the molecular sciences (data loading, derivation of typical structural information, visualization of sequence and standard structural entities).
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Open Access
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
DataRank Breakdown
Base Score Contribution
0.852
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
- Base score B(p)
- log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
- Network N(p)
- Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
- Damping factor d = 0.85
- DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
- Self-citations excluded
- Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.