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# art is science is art

EMBO Practical Course: Bioinformatics and Genome Analysis, 5–17 June 2017.

# Dark Matter of the English Language—the unwords

Words are easy, like the wind;
Faithful friends are hard to find.
—William Shakespeare

## unnames

These are names generated from the US Census list of names using a char-rnn recurrent neural network.

The names generated by the network appear neither in the list of names nor in a 479,000 list of English words. The names may be words or names in another language, however.

### female first names that don't exist

Your friends discouraged you from naming your first daughter "Ginavietta Xilly Anganelel" but you didn't listen. When you named your second daughter "Nabule Yama Janda" everyone wanted to know what your secret to having such successful children was.

Below are the alphabetically first 3–10 letter female unnames for each letter. In some cases, no names of a given length were generated for a given letter.

—3—
Bei
Cac
Cau
Daa
Deu
Edz
Ele
Fea
Fri
Hea
Hhi
Ied
Ien
Jea
Kau
Kec
Ldo
Leb
Maz
Mec
Nie
Nin
Oro
Ota
Reu
Ric
Seu
Sia
Tix
Tuu
Uan
Uid
Vea
Wie
Wil
Xai
Xon
Yka
Yra
—4—
Aaya
Abal
Bbhy
Beli
Cani
Caro
Daee
Dayn
Eann
Ebha
Gori
Guda
Hael
Hari
Idek
Idla
Joga
Joon
Kace
Laan
Laha
Nian
Olce
Olly
Phry
Pide
Qisy
Qoly
Rari
Rary
Sabi
Saes
Tany
Tary
Ucte
Uida
Vatt
Vean
Waid
Xiem
Yama
Yamn
Zibi
Ziun
—5—
Babyl
Balbo
Caccy
Eddra
Ededr
Fibee
Fleei
Gelan
Guita
Hacie
Haela
Idale
Idena
Janda
Jerly
Kaaly
Kacee
Laala
Lacee
Maala
Mabie
Nayle
Nelli
Olako
Olise
Payna
Phaci
Qinsy
Qolee
Raane
Racey
Sacti
Sacul
Tabbr
Uanga
Uayda
Vaale
Wagwa
Walyg
Xilly
Xiwda
Yahye
—6—
Alelee
Babela
Bajbie
Caccye
Cacell
Dakith
Edalla
Edelah
Feliey
Felike
Garlee
Geldie
Haishe
Haline
Idelig
Idelle
Jaccey
Jatqie
Kaceey
Kacele
Laceie
Maarae
Maarla
Nabule
Olchee
Olisha
Pamber
Parell
Qoesha
Qoleen
Rabina
Rabymi
Sachie
Sacola
Tafbie
Tamima
Ulieta
Ullena
Vandie
Waghel
Wandie
Xaique
Xillia
Yaketo
Yameka
—7—
Alenlis
Alissea
Barelah
Barmeta
Cacalla
Caccayc
Dalecee
Dalleer
Ebeccii
Eeenera
Farleen
Ferreda
Ganalel
Griagne
Harlean
Hayceda
Iellina
Ienetka
Jaqquil
Kaariko
Kabjine
Labelle
Labrice
Nachlee
Naqoena
Ollisha
Oralore
Panelte
Paricel
Qilonga
Qlianna
Rabette
Racelie
Sacelie
Sacelle
Tamarie
Tamarke
Ualacie
Uibelle
Vanelte
Waylena
Wazlein
Yakkina
—8—
Aleretha
Allalera
Bamberah
Battynkb
Caccelle
Cacellen
Dacheele
Dameline
Eetenere
Eethelie
Feairice
Gaannele
Gelneria
Hacylone
Hecticie
Iachelie
Ilabetth
Jacquine
Jaqqueyn
Kabrenee
Kacalyne
Laloytha
Langella
Maarmila
Mabylere
Orotenne
Parleeta
Parmicia
Quettine
Rachilde
Racierda
Saaleych
Saccelle
Tasharia
Tathrika
Uuguetta
Uussuida
Valtonda
Vassicha
Wapreida
Willenee
Yaumette
Yeholaki
—9—
Anganelel
Bathueyna
Bealyakha
Caccalren
Caleniqsa
Dalerisha
Dannerele
Eferwrace
Elaberosh
Genelnice
Helmarita
Hemaricia
Ieanerise
Ilbebette
Jatquelyn
Kacalenne
Kacelynen
Lasheudde
Laverethe
Macarelze
Macbalica
Nompterla
Porpencia
Ramancina
Rarashera
Saccellne
Sanelline
Thashinda
Tizkiqhie
Ususuista
Uussautti
Velletita
Vellotina
—10—
Camalincia
Ccarleetta
Deliqheeda
Elatoresha
Elisamerie
Ginavietta
Iimameline
Ilollinina
Karestanet
Kariamarie
Lelagrelie
Lelerateta
Maccelline
Maceannica
Retaqyelle
Saraquetta
Shelolesne
—11—
Cciccinelda
Cclarleette
Elisazetlie
Elisebethle
Ikekzikeina
Ilizeblelle
Kimbhrresty
Lichiabetta
Liebetreide
Mamiammalan
Marianceran
Sherleenene
Sisselletta

### male first names that don't exist

You name your first child "Babton Laarco Tabrit". You name your second "Ferandulde Hommanloco Kictortick". Both see infinite success in life and you wonder why you haven't discovered neural networks sooner.

Below are the alphabetically first 3–10 letter male unnames for each letter. In some cases, no names of a given length were generated for a given letter.

—3—
Aan
Bil
Bre
Cas
Ces
Daa
Dax
Ede
Eey
Har
Hhe
Ial
Iir
Jac
Jal
Kel
Kib
Lal
Lel
Mah
Meh
Nal
Nas
Oid
Oon
Phy
Pys
Roz
Ruf
Sas
Sih
Tes
Tey
Vay
Ven
Wal
Wil
Zes
Zin
—4—
Baan
Cald
Calg
Daal
Eard
Ebax
Farn
Felb
Gaht
Gart
Haan
Haco
Iane
Idae
Jaan
Jace
Kaan
Khen
Laan
Maab
Nald
Nall
Obby
Odan
Peit
Piar
Qide
Raal
Saag
Saan
Tacy
Tany
Vaen
Vaes
Waci
Waco
Ytih
—5—
Aanle
Aaton
Baane
Baart
Cabis
Cailh
Daamo
Daano
Eamon
Earis
Famry
Fandy
Gacon
Gahey
Hagre
Idail
Idris
Jacer
Karry
Keris
Laale
Laber
Maaro
Mabin
Naalo
Oaris
Ohale
Palio
Paric
Qebin
Qikel
Rabey
Sacon
Tacie
Talet
Uusse
Vaeld
Valen
Wacer
Zilal
Zloyn
—6—
Aabird
Aareno
Babton
Daapis
Dabron
Earrel
Earrre
Fabery
Faicey
Gaarrh
Haares
Habide
Ienlir
Igamar
Jaalil
Jabron
Kebitt
Kelmar
Laarco
Laarin
Maccel
Maccol
Nablan
Nacell
Ohepto
Olerrh
Paciul
Pakdon
Qicias
Qrekon
Rabwin
Saando
Tabrit
Tactan
Ulande
Uoseol
Vachon
Vacors
Waaren
Wabton
Xiklel
Zesian
—7—
Balnend
Barcick
Caliulo
Daalius
Daarrol
Eanondo
Earesle
Falbeus
Faloric
Ganunle
Garlard
Haameno
Habrenc
Icoolse
Idonald
Jaendie
Jajuian
Kodavio
Korgell
Laarnel
Laarrec
Maccalo
Machual
Nabtumo
Nachale
Oimolan
Ollisee
Paberto
Palducb
Quitius
Sacholh
Tahinte
Vacelle
Vagallo
Wabbent
Wacivey
Zewrave
—8—
Aarnounf
Aarruleu
Balibhat
Baravile
Carelcic
Carkocce
Dalevice
Danilian
Earrinto
Eberepto
Farricco
Gaurlnih
Gegirald
Handerus
Harelcce
Januipan
Jarcebph
Korancin
Lalenicd
Maccelce
Macchely
Nachaane
Nalaneil
Parlicco
Parreico
Randlold
Rantozer
Sachasce
Sactonae
Talentin
Tavintey
Vernilve
Vernnche
Wacellio
Waldrand
Ziliasen
—9—
Aldanoldf
Aldresdis
Berganton
Carlercca
Carmencan
Darriscce
Dauguslus
Edgaronte
Eeletento
Flandinco
Flilnendy
Galrinand
Gerarmovo
Hefarordo
Helaphhey
Jeenforue
Jeffersol
Lannendan
Lanuullan
Marricice
Marridcce
Nathanaal
Oberverto
Qoaberucc
Rallisten
Rardusler
Salcieley
Salvinten
Teliberel
Tewraslel
Wiccelele
Willofvis
—10—
Alfandrone
Atthaaneel
Brantisard
Castushart
Caucerucce
Eeverielti
Elerdrolde
Ferandulde
Flarericco
Hommanloco
Kictortick
Licoonicio
Llenelvind
Nattonanal
Oriccoomon
Rarvondard
Renaldordo
Sawvarcsas
Wengortwen
—11—
Ccrickuctof
Llantonlolm
Lunuslinzus
Micckelammy
Triddatrerd
Waldinawwan

VIEW ALL

# $k$ index: a weightlighting and Crossfit performance measure

Wed 07-06-2017

Similar to the $h$ index in publishing, the $k$ index is a measure of fitness performance.

To achieve a $k$ index for a movement you must perform $k$ unbroken reps at $k$% 1RM.

The expected value for the $k$ index is probably somewhere in the range of $k = 26$ to $k=35$, with higher values progressively more difficult to achieve.

In my $k$ index introduction article I provide detailed explanation, rep scheme table and WOD example.

# Dark Matter of the English Language—the unwords

Wed 07-06-2017

I've applied the char-rnn recurrent neural network to generate new words, names of drugs and countries.

The effect is intriguing and facetious—yes, those are real words.

But these are not: necronology, abobionalism, gabdologist, and nonerify.

These places only exist in the mind: Conchar and Pobacia, Hzuuland, New Kain, Rabibus and Megee Islands, Sentip and Sitina, Sinistan and Urzenia.

And these are the imaginary afflictions of the imagination: ictophobia, myconomascophobia, and talmatomania.

And these, of the body: ophalosis, icabulosis, mediatopathy and bellotalgia.

Want to name your baby? Or someone else's baby? Try Ginavietta Xilly Anganelel or Ferandulde Hommanloco Kictortick.

When taking new therapeutics, never mix salivac and labromine. And don't forget that abadarone is best taken on an empty stomach.

And nothing increases the chance of getting that grant funded than proposing the study of a new –ome! We really need someone to looking into the femome and manome.

# Dark Matter of the Genome—the nullomers

Wed 31-05-2017

An exploration of things that are missing in the human genome. The nullomers.

Julia Herold, Stefan Kurtz and Robert Giegerich. Efficient computation of absent words in genomic sequences. BMC Bioinformatics (2008) 9:167

# Clustering

Wed 31-05-2017
Clustering finds patterns in data—whether they are there or not.

We've already seen how data can be grouped into classes in our series on classifiers. In this column, we look at how data can be grouped by similarity in an unsupervised way.

Nature Methods Points of Significance column: Clustering. (read)

We look at two common clustering approaches: $k$-means and hierarchical clustering. All clustering methods share the same approach: they first calculate similarity and then use it to group objects into clusters. The details of the methods, and outputs, vary widely.

Altman, N. & Krzywinski, M. (2017) Points of Significance: Clustering. Nature Methods 14:545–546.

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Logistic regression. Nature Methods 13:541-542.

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Classifier evaluation. Nature Methods 13:603-604.

# What's wrong with pie charts?

Thu 25-05-2017

In this redesign of a pie chart figure from a Nature Medicine article [1], I look at how to organize and present a large number of categories.

I first discuss some of the benefits of a pie chart—there are few and specific—and its shortcomings—there are few but fundamental.

I then walk through the redesign process by showing how the tumor categories can be shown more clearly if they are first aggregated into a small number groups.

(bottom left) Figure 2b from Zehir et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. (2017) Nature Medicine doi:10.1038/nm.4333

# Tabular Data

Tue 11-04-2017
Tabulating the number of objects in categories of interest dates back to the earliest records of commerce and population censuses.

After 30 columns, this is our first one without a single figure. Sometimes a table is all you need.

In this column, we discuss nominal categorical data, in which data points are assigned to categories in which there is no implied order. We introduce one-way and two-way tables and the $\chi^2$ and Fisher's exact tests.

Altman, N. & Krzywinski, M. (2017) Points of Significance: Tabular data. Nature Methods 14:329–330.