Groups: knowledge spreadsheets for symbolic biocomputing by Michael Travers, Suzanne M. Paley, Jeff Shrager, Timothy A. Holland and Peter D. Karp.
Abstract:
Knowledge spreadsheets (KSs) are a visual tool for interactive data analysis and exploration. They differ from traditional spreadsheets in that rather than being oriented toward numeric data, they work with symbolic knowledge representation structures and provide operations that take into account the semantics of the application domain. ‘Groups’ is an implementation of KSs within the Pathway Tools system. Groups allows Pathway Tools users to define a group of objects (e.g. groups of genes or metabolites) from a Pathway/Genome Database. Groups can be transformed (e.g. by transforming a metabolite group to the group of pathways in which those metabolites are substrates); combined through set operations; analysed (e.g. through enrichment analysis); and visualized (e.g. by painting onto a metabolic map diagram). Users of the Pathway Tools-based BioCyc.org website have made extensive use of Groups, and an informal survey of Groups users suggests that Groups has achieved the goal of allowing biologists themselves to perform some data manipulations that previously would have required the assistance of a programmer.
Database URL: BioCyc.org.
Not my area so a biologist would have to comment on the substantive aspects of using these particular knowledge spreadsheets.
But there is much in this article that could be applied more broadly.
From the introduction:
A long-standing problem in computing is that of providing non-programmers with intuitive, yet powerful tools for manipulating and analysing sets of entities. For example, a number of bioinformatics database websites provide users with powerful tools for composing database queries, but once a user obtains the query results, they are largely on their own. What if a user wants to store the query results for future reference, or combine them with other query results, or transform the results, or share them with a colleague? Sets of entities of interest arise in other contexts for life scientists, such as the entities that are identified as significantly perturbed in a high-throughput experiment (e.g. a set of differentially occurring metabolites), or a set of genes of interest that emerge from an experimental investigation.
We observe that spreadsheets have become a dominant form of end-user programming and data analysis for scientists. Although traditional spreadsheets provide a compelling interaction model, and are excellent tools for the manipulation of the tables of numbers that are typical of accounting and data analysis problems, they are less easily used with the complex symbolic computations typical of symbolic biocomputing. For example, they cannot perform semantic transformations such as converting a gene list to the list of pathways the genes act in.
We coined the term knowledge spreadsheet (KS) to describe spreadsheets that are characterized by their ability to manipulate semantic objects and relationships instead of just numbers and strings. Both traditional spreadsheets and KSs represent data in tabular structures, but in a KS the contents of a cell will typically be an object from a knowledge base (KB) [such as a MetaCyc (1) frame or a URI entity from an RDF store]. Given that a column in a KS will typically contain objects of the same ontological type, a KS can offer high-level semantically knowledgeable operations on the data. For example, given a group with a column of metabolites, a semantic operation could create a parallel column in which each cell contained the reactions that produced that metabolite. Another difference between our implementation of KSs and traditional spreadsheets is that cells in our KSs can contain multiple values.
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Can you think of any domain that would not benefit from better handling of “semantic objects?”
As you read the article closely, any number of ideas or techniques for manipulating “semantic objects” will come to mind.