Consensus Techniques and the Comparison of Taxonomic Trees

1972 ◽  
Vol 21 (4) ◽  
pp. 390 ◽  
Author(s):  
Edward N. Adams III
Keyword(s):  
Taxonomic Trees

On Comparing the Shapes of Taxonomic Trees

1973 ◽  
Vol 22 (1) ◽  
pp. 50 ◽  
Author(s):  
James S. Farris
Keyword(s):  
Taxonomic Trees

A Further Note on Symmetry of Taxonomic Trees

1996 ◽  
Vol 45 (3) ◽  
pp. 385-390 ◽  
Author(s):  
Donald H. Colless
Keyword(s):  
Taxonomic Trees

scholarly journals Training Machines to Identify Species using GBIF-mediated Datasets

2019 ◽  
Vol 3 ◽  
Author(s):  
Tim Robertson ◽  
Serge Belongie ◽  
Hartwig Adam ◽  
Christine Kaeser-Chen ◽  
Chenyang Zhang ◽  
...  
Keyword(s):  
Machine Vision ◽  
Character Trait ◽  
The Internet ◽  
Citation Practice ◽  
Visual Characteristics ◽  
Citation Graph ◽  
Application Developers ◽  
Taxonomic Trees ◽  
Occurrence Records ◽  
Refining Machine

Advances in machine vision technology are rapidly enabling new and innovative uses within the field of biodiversity. Computers are now able to use images to identify tens of thousands of species across a wide range of taxonomic groups in real time, notably demonstrated by iNaturalist.org, which suggests species IDs to users (https://www.inaturalist.org/pages/computer_vision_demo) as they create observation records. Soon it will be commonplace to detect species in video feeds or use the camera in a mobile device to search for species-related content on the Internet. The Global Biodiversity Information Facility (GBIF) has an important role to play in advancing and improving this technology, whether in terms of data, collaboration across teams, or citation practice. But in the short term, the most important role may relate to initiating a cultural shift in accepted practices for the use of GBIF-mediated data for training of artificial intelligence (AI). “Training datasets” play a critical role in achieving species recognition capability in any machine vision system. These datasets compile representative images containing the explicit, verifiable identifications of the species they include. High-powered computers run algorithms on these training datasets, analysing the imagery and building complex models that characterize defining features for each species or taxonomic group. Researchers can, in turn, apply the resulting models to new images, determining what species or group they likely contain. Current research in machine vision is exploring (a) the use of location and date information to further improve model results, (b) identification methods beyond species-level into attribute, character, trait, or part-level ID, with an eye toward human interpretability, and (c) expertise modeling for improved determination of “research grade” images and metadata. The GBIF community has amassed one of the largest datasets of labelled species images available on the internet: more than 33 million species occurrence records in GBIF.org have one or more images (https://www.gbif.org/occurrence/gallery). Machine vision models, when integrated into the data collection tools in use across the GBIF network, can improve the user experience. For example, in citizen science applications like iNaturalist, automated species suggestion helps even novice users contribute occurrence records to GBIF. Perhaps most importantly, GBIF has implemented uniform (and open) data licensing, established guidelines on citation and provided consistent methods for tracking data use through the Digital Object Identifiers (DOI) citation chain. GBIF would like to build on the lessons learned in these activities while striving to assist with this technology research and increase its power and availability. We envisage an approach as follows: To assist in developing and refining machine vision models, GBIF plans to provide training datasets, taking effort to ensure license and citation practice are respected. The training datasets will be issued with a DOI, and the contributing datasets will be linked through the DOI citation graph. To assist application developers, Google and Visipedia plan to build and publish openly-licensed models and tutorials for how to adapt them for localized use. Together we will strive to ensure that data is being used responsibly and transparently, to close the gap between machine vision scientists, application developers, and users and to share taxonomic trees capturing the taxon rank to which machine vision models can identify with confidence based on an image’s visual characteristics. To assist in developing and refining machine vision models, GBIF plans to provide training datasets, taking effort to ensure license and citation practice are respected. The training datasets will be issued with a DOI, and the contributing datasets will be linked through the DOI citation graph. To assist application developers, Google and Visipedia plan to build and publish openly-licensed models and tutorials for how to adapt them for localized use. Together we will strive to ensure that data is being used responsibly and transparently, to close the gap between machine vision scientists, application developers, and users and to share taxonomic trees capturing the taxon rank to which machine vision models can identify with confidence based on an image’s visual characteristics.

Consensus Techniques and the Comparison of Taxonomic Trees

1972 ◽  
Vol 21 (4) ◽  
pp. 390-397 ◽  
Author(s):  
E. N. Adams
Keyword(s):  
Taxonomic Trees

scholarly journals BLANT—fast graphlet sampling tool

2019 ◽  
Vol 35 (24) ◽  
pp. 5363-5364
Author(s):  
Sridevi Maharaj ◽  
Brennan Tracy ◽  
Wayne B Hayes
Keyword(s):  
Functional Similarity ◽  
Supplementary Information ◽  
Local Alignment ◽  
Supplementary Data ◽  
Input Graph ◽  
Sequence Alignments ◽  
Ppi Networks ◽  
Local Sequence ◽  
Taxonomic Trees

Abstract Summary BLAST creates local sequence alignments by first building a database of small k-letter sub-sequences called k-mers. Identical k-mers from different regions provide ‘seeds’ for longer local alignments. This seed-and-extend heuristic makes BLAST extremely fast and has led to its almost exclusive use despite the existence of more accurate, but slower, algorithms. In this paper, we introduce the Basic Local Alignment for Networks Tool (BLANT). BLANT is the analog of BLAST, but for networks: given an input graph, it samples small, induced, k-node sub-graphs called k-graphlets. Graphlets have been used to classify networks, quantify structure, align networks both locally and globally, identify topology-function relationships and build taxonomic trees without the use of sequences. Given an input network, BLANT produces millions of graphlet samples in seconds—orders of magnitude faster than existing methods. BLANT offers sampled graphlets in various forms: distributions of graphlets or their orbits; graphlet degree or graphlet orbit degree vectors, the latter being compatible with ORCA; or an index to be used as the basis for seed-and-extend local alignments. We demonstrate BLANT’s usefelness by using its indexing mode to find functional similarity between yeast and human PPI networks. Availability and implementation BLANT is written in C and is available at https://github.com/waynebhayes/BLANT/releases. Supplementary information Supplementary data are available at Bioinformatics online.

Logical Inference Based on Incomplete and/or Fuzzy Ontologies

2011 ◽  
pp. 1-16 ◽  
Author(s):  
Juliusz L. Kulikowski
Keyword(s):  
Decision Making ◽  
Knowledge Representation ◽  
Domain Knowledge ◽  
Computer Systems ◽  
Logical Inference ◽  
Formal Representation ◽  
Universal Form ◽  
Definition Of ◽  
Taxonomic Trees

In this chapter, a concept of using incomplete or fuzzy ontologies in decision making is presented. A definition of ontology and of ontological models is given, as well as their formal representation by taxonomic trees, bi-partite graphs, multigraphs, relations, super-relations and hyper-relations. The definitions of the corresponding mathematical notions are also given. Then, the concept of ontologies representing incomplete or uncertain domain knowledge is presented. This concept is illustrated by an example of decision making in medicine. The aim of this chapter is to give an outlook on the possibility of ontological models extension in order to use them as an effective and universal form of domain knowledge representation in computer systems supporting decision making in various application areas.

scholarly journals Statistical Object Data Analysis of Taxonomic Trees from Human Microbiome Data

PLoS ONE ◽  
2012 ◽  
Vol 7 (11) ◽  
pp. e48996 ◽  
Author(s):  
Patricio S. La Rosa ◽  
Berkley Shands ◽  
Elena Deych ◽  
Yanjiao Zhou ◽  
Erica Sodergren ◽  
...  
Keyword(s):  
Data Analysis ◽  
Human Microbiome ◽  
Microbiome Data ◽  
Taxonomic Trees ◽  
Statistical Object

scholarly journals Genetic distances and taxonomic trees between goats of Ceará State (Brazil) and goats of the Mediterranean region (Europe and Africa)

2000 ◽  
Vol 23 (1) ◽  
pp. 121-125 ◽  
Author(s):  
Théa M.M. Machado ◽  
Mohamed Chakir ◽  
Jean Jacques Lauvergne
Keyword(s):  
Genetic Distance ◽  
Mediterranean Region ◽  
Genetic Distances ◽  
The State ◽  
Northeastern Brazil ◽  
North African ◽  
Mediterranean Populations ◽  
Allelic Frequencies ◽  
Taxonomic Trees ◽  
European Populations

Goats of an undefined breed (called UDB) from the State of Ceará, northeastern Brazil (N = 447), European Mediterranean goats (N = 3,847) and African Mediterranean goats (N = 325) were compared to establish genetic distances and taxonomic trees. Allelic frequencies in each population for presence or absence of the following traits were used: horns, reduced ears, long hair, wattles, beard, roan color, brown eumelanin and eumelanic standard pigmentation. The genetic distance, applying the method developed by Nei (1972), was: least between goats from different meso-regions of the State of Ceará (0.0008 to 0.0120); small between all UDB of Ceará and French goats of Rove and Haute Roya (0.0236 and 0.0459); greater between all UDB of Ceará and northern Spanish goats (0.1166), and greatest between all UDB of Ceará and northern African goats (Moroccan of Drâa, Rhâali and Zagora), Balkan goats (Sakhar from Bulgaria and Macedonia from Greece) and some insular Mediterranean populations (Corsica, Sicily and Sardinia), which ranged from 0.1237 to 0.2714. Brazilian UDB goats are more closely related to Continental and Western European populations than to North African, Balkan or Insular Mediterranean populations.

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