Experimental Methods in Computer Taxonomy

Beers, Russell J.; Lockhart, W. R.

doi:10.1099/00221287-28-4-633

Cited by 47 publications

(26 citation statements)

References 4 publications

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“…When these same data were scored by a method which permits two strains 'negative' for a given property to be considered similar (Beers & Lockhart, 1962), rather different results were obtained. Table 1 indicates the method of scoring (11) used to obtain the groupings shown in Fig.…”

Section: Comparison Of Computer Methods 647mentioning

confidence: 99%

“…1, they still leave much to be desired. Even though 'negative' comparisons are used, the total number of features involved in computation of S is not constant (and is thus a source of inconsistency) unless certain classes of quantitative data are handled in a special manner (Beers & Lockhart, 1962 It was suggested by Beers & Lockhart (1982) that distance ( D = log,l/S) rather than similarity might well be adopted as the primary measure of relationships among organisms. The data from Fig-3 were converted from S t o D values; the resulting diagram is shown in Fig+ 4.…”

Section: Comparison Of Computer Methods 647mentioning

confidence: 99%

“…4, perhaps offers no important advantage. In some attempts to diagram the threedimensional relationships among these strains, however, we found it necessary to use D. There at least are sound theoretical reasons for adopting this parameter, and calculation of D rather than S would not be difficult (Beers & Lockhart, 1962). It should be pointed out also that Fig.…”

Section: Comparison Of Computer Methods 651mentioning

confidence: 99%

“…Seventy-one properties of these organisms were determined, and the resulting data scored in different ways (according to various proposed techniques) before being submitted to an appropriate computer program for calculation of similarity (S) values. These comparative studies indicated that better division of organisms into mutually similar groups can be achieved when data about properties which may have several alternative expressions are handled in the manner proposed by Beers & Lockhart (1962). The number of comparisons which contribute to individual similarity values should be held constant by adequate treatment of quantitative data and by adoption of scoring methods which permit comparisons between ' negative ' properties.…”

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confidence: 99%

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A Comparison of Methods for Computer Taxonomy

Beers

Fisher

Megraw

et al. 1962

Journal of General Microbiology

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StrMMARYThe systematic relationships among 54 strains of bacteria, representing principally the genera Achromobacter, Aerobacter, Alcaligenes, Eschmichia, Mima, Pseudomonas, Serratia and Streptococcus, were examined by computer methods. Seventy-one properties of these organisms were determined, and the resulting data scored in different ways (according to various proposed techniques) before being submitted to an appropriate computer program for calculation of similarity (S) values. These comparative studies indicated that better division of organisms into mutually similar groups can be achieved when data about properties which may have several alternative expressions are handled in the manner proposed by Beers & Lockhart (1962). The number of comparisons which contribute to individual similarity values should be held constant by adequate treatment of quantitative data and by adoption of scoring methods which permit comparisons between ' negative ' properties. It may be useful to employ distance (D = log,l/S) rather than similarity as the primary measure of relationships among groups of organisms.

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Section: Comparison Of Computer Methods 647mentioning

confidence: 99%

Section: Comparison Of Computer Methods 647mentioning

confidence: 99%

Section: Comparison Of Computer Methods 651mentioning

confidence: 99%

mentioning

confidence: 99%

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A Comparison of Methods for Computer Taxonomy

Beers

Fisher

Megraw

et al. 1962

Journal of General Microbiology

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“…They used a multivariate analysis (Beers & Lockhart, 1962;Sneath, 1957) to demonstrate that the genus consists of well-defined species and an unusual absence of intermediate forms. This method, however, has the disadvantage that it does not analyse directly the relationships between all the species of the genus Thiobacillus.…”

Section: Introductionmentioning

confidence: 99%

Deoxyribonucleic Acid Base Composition and Taxonomy of Thiobacilli and some Nitrifying Bacteria

Jackson

Moriarty

Nicholas

1968

Journal of General Microbiology

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The DNA base composition of 14 authentic strains of the genus Thiobacillus, determined by caesium chloride density gradient centrifugation, was found to vary from 5 I to 68 mole yo guanine + cytosine (G + C). The mole % G + C values for the various species are as follows : T. thiooxidans and T. concretivorus 5 1-52, T. neopolitanus and T. ferrooxidans 56-57, and T. thiopurus, T. thiocyanoxidans, T. denitrijicans, T. novellus and T. trautweinii 62-68. Another group of chemoautotrophic bacteria, the nitrifiers Nitrosornonas europaea and Nitrobacter agilis, were found to have a G + C content of 52 and 65 %, respectively. The results are compared with other types of taxonomic studies made with these and related bacteria.

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A method to implement continuous characters in digital identification keys that estimates the probability of an annotation

Tyrrell

2019

Appl Plant Sci

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Premise Species identification is vital to many disciplines. Digital technology has improved identification tools, but the direct use of characters with continuous states has yet to be fully realized. To achieve full use of continuous characters for identification, I propose a classifier that calculates a posterior probability (degree of belief) in possible name assignments and an estimate of the relative evidence for the candidate annotations. Methods A model for a species is defined using continuous morphological characters, and an algorithm for identification with a naive Bayesian classifier, using the model, is presented. A method of estimating the strength of evidence for candidate species is also described. Results The proposed method is applied in two example identifications: native vs. invasive Myriophyllum in North America and vegetative Rhipidocladum bamboos in Mexico. In each instance, the new method provides a probability and estimate of the strength of the probability to enhance the name assignment in situations where taxa are difficult to differentiate using discrete character states. Discussion Naive Bayesian classifiers take advantage of the predictive information inherent in continuous morphological characters. Application of this methodology to plant taxonomy advances our ability to leverage digital technology for improved interactive taxonomic identifications.

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Experimental Methods in Computer Taxonomy

Cited by 47 publications

References 4 publications

A Comparison of Methods for Computer Taxonomy

A Comparison of Methods for Computer Taxonomy

Deoxyribonucleic Acid Base Composition and Taxonomy of Thiobacilli and some Nitrifying Bacteria

A method to implement continuous characters in digital identification keys that estimates the probability of an annotation

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