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# Calculate the Matching Score for Microorganisms

Source: `R/mo_matching_score.R`
`mo_matching_score.Rd`

This algorithm is used by `as.mo()` and all the `mo_*` functions to determine the most probable match of taxonomic records based on user input.

## Usage

    mo_matching_score(x, n)

## Arguments

x

  Any user input value(s).

n

  A full taxonomic name, that exists in `microorganisms$fullname`.

## Note

This algorithm was originally developed in 2018 and subsequently described in: Berends MS et al. (2022). AMR: An R Package for Working with Antimicrobial Resistance Data. Journal of Statistical Software, 104(3), 1-31; doi:10.18637/jss.v104.i03 .

Later, the work of Bartlett A et al. about bacterial pathogens infecting humans (2022, doi:10.1099/mic.0.001269 ) was incorporated, and optimalisations to the algorithm were made.

## Matching Score for Microorganisms

With ambiguous user input in `as.mo()` and all the `mo_*` functions, the returned results are chosen based on their matching score using `mo_matching_score()`. This matching score \(m\), is calculated as:

$$m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \textrm{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}$$

where:

- \(x\) is the user input;

- \(n\) is a taxonomic name (genus, species, and subspecies);

- \(l_n\) is the length of \(n\);

- \(lev\) is the Levenshtein distance function (counting any insertion as 1, and any deletion or substitution as 2) that is needed to change \(x\) into \(n\);

- \(p_n\) is the human pathogenic prevalence group of \(n\), as described below;

- \(k_n\) is the taxonomic domain ('kingdom' until taxonomic reclassification of 2024) of \(n\), set as Bacteria = 1, Fungi = 1.25, Protozoa = 1.5, Chromista = 1.75, Archaea = 2, others = 3.

The grouping into human pathogenic prevalence \(p\) is based on recent work from Bartlett et al. (2022, doi:10.1099/mic.0.001269 ) who extensively studied medical-scientific literature to categorise all bacterial species into these groups:

- Established, if a taxonomic species has infected at least three persons in three or more references. These records have `prevalence = 1.15` in the microorganisms data set;

- Putative, if a taxonomic species has fewer than three known cases. These records have `prevalence = 1.25` in the microorganisms data set.

Furthermore,

- Genera from the World Health Organization's (WHO) Priority Pathogen List have `prevalence = 1.0` in the microorganisms data set;

- Any genus present in the established list also has `prevalence = 1.15` in the microorganisms data set;

- Any other genus present in the putative list has `prevalence = 1.25` in the microorganisms data set;

- Any other species or subspecies of which the genus is present in the two aforementioned groups, has `prevalence = 1.5` in the microorganisms data set;

- Any non-bacterial genus, species or subspecies of which the genus is present in the following list, has `prevalence = 1.25` in the microorganisms data set: Absidia, Acanthamoeba, Acremonium, Actinomucor, Aedes, Alternaria, Amoeba, Ancylostoma, Angiostrongylus, Anisakis, Anopheles, Apophysomyces, Arthroderma, Aspergillus, Aureobasidium, Basidiobolus, Beauveria, Bipolaris, Blastobotrys, Blastocystis, Blastomyces, Candida, Capillaria, Chaetomium, Chilomastix, Chrysonilia, Chrysosporium, Cladophialophora, Cladosporium, Clavispora, Coccidioides, Cokeromyces, Conidiobolus, Coniochaeta, Contracaecum, Cordylobia, Cryptococcus, Cryptosporidium, Cunninghamella, Curvularia, Cyberlindnera, Debaryozyma, Demodex, Dermatobia, Dientamoeba, Diphyllobothrium, Dirofilaria, Echinostoma, Entamoeba, Enterobius, Epidermophyton, Exidia, Exophiala, Exserohilum, Fasciola, Fonsecaea, Fusarium, Geotrichum, Giardia, Graphium, Haloarcula, Halobacterium, Halococcus, Hansenula, Hendersonula, Heterophyes, Histomonas, Histoplasma, Hortaea, Hymenolepis, Hypomyces, Hysterothylacium, Kloeckera, Kluyveromyces, Kodamaea, Lacazia, Leishmania, Lichtheimia, Lodderomyces, Lomentospora, Madurella, Malassezia, Malbranchea, Metagonimus, Meyerozyma, Microascus, Microsporidium, Microsporum, Millerozyma, Mortierella, Mucor, Mycocentrospora, Nannizzia, Necator, Nectria, Ochroconis, Oesophagostomum, Oidiodendron, Opisthorchis, Paecilomyces, Paracoccidioides, Pediculus, Penicillium, Phaeoacremonium, Phaeomoniella, Phialophora, Phlebotomus, Phoma, Pichia, Piedraia, Pithomyces, Pityrosporum, Plasmodium, Pneumocystis, Pseudallescheria, Pseudoscopulariopsis, Pseudoterranova, Pulex, Purpureocillium, Quambalaria, Rhinocladiella, Rhizomucor, Rhizopus, Rhodotorula, Saccharomyces, Saksenaea, Saprochaete, Sarcoptes, Scedosporium, Schistosoma, Schizophyllum, Schizosaccharomyces, Scolecobasidium, Scopulariopsis, Scytalidium, Spirometra, Sporobolomyces, Sporopachydermia, Sporothrix, Sporotrichum, Stachybotrys, Strongyloides, Syncephalastrum, Syngamus, Taenia, Talaromyces, Teleomorph, Toxocara, Trichinella, Trichobilharzia, Trichoderma, Trichomonas, Trichophyton, Trichosporon, Trichostrongylus, Trichuris, Tritirachium, Trombicula, Trypanosoma, Tunga, Ulocladium, Ustilago, Verticillium, Wallemia, Wangiella, Wickerhamomyces, Wuchereria, Yarrowia, or Zygosaccharomyces;

- All other records have `prevalence = 2.0` in the microorganisms data set.

When calculating the matching score, all characters in \(x\) and \(n\) are ignored that are other than A-Z, a-z, 0-9, spaces and parentheses.

All matches are sorted descending on their matching score and for all user input values, the top match will be returned. This will lead to the effect that e.g., `"E. coli"` will return the microbial ID of Escherichia coli (\(m = 0.688\), a highly prevalent microorganism found in humans) and not Entamoeba coli (\(m = 0.381\), a less prevalent microorganism in humans), although the latter would alphabetically come first.

## Download Our Reference Data

All reference data sets in the AMR package - including information on microorganisms, antimicrobials, and clinical breakpoints - are freely available for download in multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, and Stata.

For maximum compatibility, we also provide machine-readable, tab-separated plain text files suitable for use in any software, including laboratory information systems.

Visit our website for direct download links, or explore the actual files in our GitHub repository.

## Examples

    mo_reset_session()
    #> ℹ Reset 17 previously matched input values.

    as.mo("E. coli")
    #> Class <mo>
    #> [1] B_ESCHR_COLI
    mo_uncertainties()
    #> Matching scores are based on the resemblance between the input and the full
    #> taxonomic name, and the pathogenicity in humans. See `mo_matching_score()`.
    #> Colour keys:  0.000-0.549  0.550-0.649  0.650-0.749  0.750-1.000
    #> -------------------------------------------------------------------------------
    #> "E. coli" -> Escherichia coli (B_ESCHR_COLI, 0.688)
    #> Also matched: Enterococcus crotali (0.650), Escherichia coli coli (0.643),
    #> Escherichia coli expressing (0.611), Enterobacter cowanii (0.600), Enterococcus
    #> columbae (0.595), Enterococcus camelliae (0.591), Enterococcus casseliflavus
    #> (0.577), Enterobacter cloacae cloacae (0.571), Enterobacter cloacae complex
    #> (0.571), and Enterobacter cloacae dissolvens (0.565)
    #>
    #> ℹ Only the first 10 other matches of each record are shown. Run ``
    #>   `print(mo_uncertainties(), n = ...)` `` to view more entries, or save
    #>   `mo_uncertainties()` to an object.

    mo_matching_score(
      x = "E. coli",
      n = c("Escherichia coli", "Entamoeba coli")
    )
    #> [1] 0.6875000 0.3809524