Towards a semantic approach to numerical tree inference in phylogenetics

Vogt, Lars

doi:10.1111/cla.12195

Cited by 25 publications

(40 citation statements)

References 129 publications

(244 reference statements)

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“…4), all triple statements about its properties and its subparts and their properties would no longer be connected to the main graph, and thus would be disconnected from the semantic representation of the corresponding OTU. This demonstrates their ontological dependency (for a detailed discussion of semantic instance anatomies see Vogt, 2016Vogt, , 2017a; for an alternative way of semantically representing the anatomical organization of organisms see Semantic Phenotypes, which is a class-based Entity-Quality approach, Balhoff et al, 2013).…”

Section: Ontology-basedmentioning

confidence: 93%

The logical basis for coding ontologically dependent characters

Vogt

2017

Cladistics

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The coding of dependent morphological characters represents a major methodological problem in phylogenetics. Based on a distinction of semantic and ontological logical character dependency, I suggest how inapplicables can be treated properly and introduce rules of mutually dependent character states, which specify how character states of one character determine character states in its ontologically dependent characters. Using various examples, I discuss a set of general rules that applies independently of whether the ontological dependency results from property instantiation, parthood or subsumption. When implemented in a matrix editor, these rules would significantly facilitate the coding procedure, speed up coding of large matrices and increase overall consistency. If implemented in algorithms for tree reconstruction, the rules would prevent inconsistent reconstructions of ancestral states, which is a common problem with inapplicables. In the second part of the paper I set out to explore the potential of using a semantic framework and semantic techniques for automatically detecting instances of ontological dependency and specific cases of semantic dependency and how they can be applied for automatically coding character state values for ontologically dependent characters using the general rules discussed in the first part of the paper. This approach utilizes graph‐based semantic representations of instance anatomy, which represent ontology‐based descriptions of the anatomical organization of individual organisms.

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Section: Ontology-basedmentioning

confidence: 93%

The logical basis for coding ontologically dependent characters

Vogt

2017

Cladistics

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“…Another proposed approach was to represent hypotheses for the homology of anatomical entities outside of formalized ontologies; the ontology itself could remain homology-neutral [28], also see [1][2][3]. Anatomical entities would be defined on the basis of spatio-structural properties that would allow their unambiguous identification and re-identification exclusively on the basis of anatomy [1]. This approach is further justified by the fact that at least some homology hypotheses are too weak or controversial to be embedded in the ontology in the same way as the hardened knowledge concerning the types and parts of anatomical structures.…”

Section: How To Accommodate Homology Within Ontologies?mentioning

confidence: 99%

“…They do not expect the query to return structures for parts (e.g., 'humerus', 'femur') of the serial homologs, historical homologs (e.g., 'pectoral fin') of the serial homologs, or developmental precursors (e.g., 'hindlimb bud', 'forelimb bud') of the serial homologs. 1 Based on developmental comparisons with fossil early tetrapods, including Ichthyostega and Eryops, at least one study [49] considered the prehallux, i.e., the ossification anterior to the 'big toe' in frogs (anurans), to be a bona fide digit. That is, the homolog of the prehallux in anurans is pedal digit 1 in Ichthyostega and Eryops.…”

Section: Competency Questionmentioning

confidence: 99%

“…The longer tradition of comparative anatomy has also revealed extensive conservation, with the homologies between the jaw bones of fishes and the inner ear bones of mammals as a quintessential example. The complexity of anatomical data, however, has been an impediment to standardization and computation, and many of the critical tasks rely on manual inspection of the data and human judgment [1]. Advances in this area have been made using semantic reasoning, but these have not explicitly incorporated nor evaluated homology reasoning.…”

Section: Introductionmentioning

confidence: 99%

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A logical model of homology for comparative biology

Mabee

Balhoff

Dahdul

et al. 2019

Preprint

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There is a growing body of research on the evolution of anatomy in a wide variety of organisms. Discoveries in this field could be greatly accelerated by computational methods and resources that enable these findings to be compared across different studies and different organisms and linked with the genes responsible for anatomical modifications. Homology is a key concept in comparative anatomy; two important types are historical homology (the similarity of organisms due to common ancestry) and serial homology (the similarity of repeated structures within an organism). We explored how to most effectively represent historical and serial homology across anatomical structures to facilitate computational reasoning. We assembled a collection of homology assertions from the literature with a set of taxon phenotypes for vertebrate fins and limbs from the Phenoscape Knowledgebase (KB). Using six competency questions, we evaluated the reasoning ramifications of two logical models: the Reciprocal Existential Axioms Homology Model (REA) and the Ancestral Value Axioms Homology Model (AVA). Both models returned the user-expected results for all but one historical homology query and all serial homology queries. Additionally, for each competency question, the AVA model returns the search term and any subtypes. We identify some challenges of implementing complete homology queries due to limitations of OWL reasoning. This work lays the foundation for homology reasoning to be incorporated into other ontology-based tools, such as those that enable synthetic supermatrix construction and candidate gene discovery.

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“…This could, for instance, be used for automatic assessment and measurement of semantic similarity between different semantic graphs, which would provide new means for analyzing all kinds of data from the life sciences (e.g., Vogt 2016Vogt , 2017, submitted a).…”

Section: Sciencesmentioning

confidence: 99%

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

Vogt

2017

Preprint

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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.PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.2429v2 | CC BY 4.0 Open Access |

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Towards a semantic approach to numerical tree inference in phylogenetics

Cited by 25 publications

References 129 publications

The logical basis for coding ontologically dependent characters

The logical basis for coding ontologically dependent characters

A logical model of homology for comparative biology

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

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