Poetry is just the evidence of life. If your life is burning well, poetry is just the ashburn somethingmore quotes

# words: fun

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

The unwords are words that are not in a language. Here I provide lists of such words, generated using a neural network.

Who knows? You may be the first to read these newly minted words and, armed with this knowlege, you can finally manufacture that exotic malady you've been looking for to help you get out of (or into) a party.

If you're a writer, these lists are a great resource for fictitious character names, places, animals, and cars.

I have a separate page of neologisms that I have created.

## generating new words, names and places

The word lists were generated using the char-rnn recurrent neural network. Depending on the list the inputs were a large English dictionary, first names, names of countries or drug names.

This process is fun and the output is often believably hilarious. The application of char-rnn to the creation of new paint names, such as turdly and Ronching blue, is what motivated me to try this out for myself. Ronch on!

So, if you want to explore Conchar and Pobacia and Sinistan or wonder what kind of symptoms are associated with myconomascophobia or are curious about what a cakmiran would look like, explore the lists in depth and use a new word tonight.

## Previous efforts

I've previously used Markov Chains to generate Tripsum, random ramblings of the mad Donald Trump, or Hitchum, the random ramblings of the brilliant Christopher Hitchens.

## Quick Examples

Here are a few short lists to get you started. They're all sampled from my list of about 883,500 English unwords.

### fields of study

Absolutely perfect for that time you don't really want to tell someone what you do. Shut down a conversation quickly with necronology or start one with jabimanology.

—7—
agology
beology
deology
egology
feology
giology
idology
phology
teology
viology
—8—
belology
cagology
damology
ecpology
fenology
hedology
ingology
macology
nezology
oppology
pacology
senology
tanology
undology
—9—
acceology
bectology
cantology
daniology
egotology
fanzology
gaenology
heltology
icerology
lellology
maerology
neevology
ochrology
palvology
regmology
sentology
taltology
uricology
veleology
—10—
acctiology
bebolology
decheology
ecotiology
faranology
galfiology
hegosology
ibberology
kerimology
lekanology
nedanology
odidiology
pacreology
regonology
saconology
tanzoology
unfelology
verevology
warryology
—11—
acestnology
baomasology
cacarrology
damakhology
eabidiology
fammocology
gactomology
icedorology
kannesology
leamonology
magoulology
necronology
orchitology
parcimology
rassamology
spetitology
talmatology
untectology
vertulology
wireulology
zarcidology
—12—
bailiomology
cacometology
daiomatology
ecyphenology
finaginology
gaarozoology
hematizology
icernoxology
jabimanology
kenamidology
macopicology
noidentology
octuditology
palamanology
recestiology
spactomology
tamanthology
velarivology
—13—
acctohicology
beonephnology
caconyonology
damalanuology
ecerchenology
gecheepsology
helopatrology
idolotamology
kiliagorology
lamolatrology
nonemartology
omphonomology
perculonology
seperahiology
tepromorology
unsophenology
velarithology

### trump's best words

More tremendous than before, here is a sneak peek at the President's new vocabulary. He was recently seen acting reassoritious, though aides worry about his gabalkerous tendencies.

—7—
acanous
bateous
cabrous
dachous
ecelous
fallous
gabuous
harious
iberous
kangous
lelious
maceous
nagious
obbious
pahyous
querous
ratwous
scatous
taimous
utugous
veysous
wirbous
zabrous
—8—
abellous
bacerous
caborous
dachious
eablious
facomous
gaarious
havamous
ibbicous
jabimous
kamulous
lamanous
mabilous
namamous
octerous
pacorous
quawnous
raralous
sigurous
tacheous
ufturous
vhodious
weybrous
yepenous
—9—
abrybrous
baltylous
cabellous
dactinous
eccarious
facuelous
harrazous
icabulous
jankarous
kantalous
lammanous
mabecious
nabocious
pacculous
quebunous
ramperous
sacculous
tacallous
udnygnous
vebageous
whioteous
yiembrous
—10—
ablepelous
bailligous
cabalizous
dabsterous
eacopagous
gacaphuous
hasikerous
iabiaceous
jabimarous
kaguicgous
lamphylous
macaritous
naantalous
ocobarious
pacolagous
quacidious
rardaceous
saconogous
tabitolous
uebbaceous
veeaterous
waniferous
yridyovous
—11—
abbuliglous
bailouheous
caccantrous
dabsiparous
ebamicarous
falaealpous
gabalkerous
harspillous
icacalcious
jenkatapous
kedamatrous
lacluncrous
maanigerous
nabitarious
oacreparous
pactavinous
quedyismous
reangrevous
sabriconous
tacopathous
ueduraceous
velelaucous
warniferous
yupliferous
—12—
abrybraceous
bailligirous
cabstiferous
dabnasaceous
eabycerinous
fanatombrous
gactitarious
harsperstous
icaneopylous
jacugnaceous
kabridiflous
lanundineous
maagilineous
nacocarphous
oachophagous
pacanigenous
rambreaulous
sackniferous
tachyphonous
ulonollurous
vacoraageous
weloniaceous
zyanisudious
—13—
absiaphaceous
cabolophagous
dachariaceous
eccaralculous
facoschaceous
gacultiaceous
helaptoconous
icancraperous
jejuctogenous
karaseerheous
leaageistrous
macalopathous
namanocoalous
oacantraceous
pabliporagous
quaculiferous
reassoritious
saconophorous
tacalasmatous
unceanigerous
vegnuriferous

### get thee to a fefery!

Building on covfefe, here are the words containing fefe that the neural network discovered, without covfefe being in the training set.

*fefe*: befefeeper brefefent brefefer cofefesce fefee fefeing fefenially fefenialness fefenian fefenianity fefeniate fefeoridy feferate feferated feferating feferats feferial feferiality feferially feferialness feferic feferies feferonious feferoptable fefery fefes gafefeyed gufefer hifefeed tifeferreuse

### personal values

What do you hold dearest to your heart? You may not want to answer that question but you don't want to say nothing. Assess your moral superiority by loudly professing your views on abobionalism.

—6—
ablism
banism
cacism
defism
echism
famism
hihism
ichism
katism
lipism
oacism
piaism
savism
tatism
udsism
vhrism
wipism
—7—
abatism
baggism
cabeism
dabyism
ecelism
famyism
gaatism
hekkism
icerism
kaggism
leliism
maatism
oinyism
paarism
redrism
sabyism
tagsism
vettism
wannism
zyarism
—8—
abxecism
bachrism
cabitism
dailyism
eacoxism
falerism
gacolism
haufoism
icastism
jabicism
kecarism
mabreism
naimdism
oinicism
pakerism
quedyism
ramblism
saxicism
uejogism
vavbeism
warryism
zerryism
—9—
abamerism
bacoinism
cablacism
dacantism
ecarcuism
falgonism
gaabilism
hafletism
icemagism
jabergism
kanmitism
lambinism
mackelism
nammalism
oabateism
pacoleism
ramemaism
saboirism
tacialism
uejoinism
velingism
werialism
—10—
aboboidism
basozacism
cablionism
daffialism
eccynatism
facomanism
gaariotism
helgralism
icastivism
jabicalism
lalaippism
maagillism
pacodotism
ramanguism
sacagenism
tabuxalism
uemitalism
vefflenism
whindanism
—11—
abariiarism
barkepolism
cabelierism
dabsolitism
eactubilism
falgmantism
gacticalism
harrigonism
icanepotism
jakekannism
kanemortism
lacloparism
mabinianism
nairsianism
oachanokism
pacanialism
rechapolism
secolastism
tacdicalism
unmantsmism
venianchism
weistentism
zabinianism
—12—
abobionalism
balfationism
caboonialism
dabnationism
eacesicalism
famiticanism
gabrinantism
hedorelicism
icasyntheism
kanmiphagism
lelipetylism
mackbelloism
nalasmandism
omphonophism
paargomelism
reamoplagism
scuntylinism
tachyphonism
uequitionism
vabippareism
wircationism

### who are these people?

It's hard to know who you are. And even harder admitting it in public. Pick something safe from this list of identities. Be the gabdologist you've always wanted.

—6—
accist
bebist
cacist
dakist
egyist
farist
ichist
jamist
kawist
nlhist
oacist
revist
samist
tawist
uelist
vidist
wgeist
—7—
abolist
baggist
cabrist
dabvist
ebalist
falgist
gaboist
heddist
iccoist
jabrist
kaggist
leltist
maclist
neerist
olanist
palpist
redoist
sogyist
tabuist
uejoist
vequist
wannist
zaivist
—8—
ablarist
bachrist
cacknist
dabsaist
ecercist
faletist
gachlist
haufoist
iccomist
jacklist
kalarist
lepanist
maculist
nenalist
odiarist
paintist
reashist
saxinist
uejogist
velewist
wandaist
—9—
acarenist
baelikist
cabourist
dactinist
ecgartist
falgonist
hcmedrist
icabilist
jelimyist
kaggitist
lammetist
mackemist
nacldoist
oberalist
pablinist
quahhoist
rastarist
sabrilist
tablanist
udnygnist
veestoist
wellhoist
—10—
abcrappist
cacherlist
dabuschist
eccularist
facomanist
gachtraist
ibbicarist
jalatalist
kalanalist
maccrecist
najumanist
oacologist
pacolikist
recharmist
sabamagist
talmangist
uemitalist
vegonexist
whelentist
—11—
acceologist
baiskallist
cabodianist
daffudalist
eccbietrist
fanationist
gabdologist
harrigonist
icefomalist
kedamietist
lebsynsmist
naniquinist
ocpenvalist
pacanialist
recharmwist
seckneolist
tacdicalist
uncherylist
velelaucist
wenmologist
zyanisthist
—12—
abutationist
becorrhygist
cacomelanist
dabnationist
eacesicalist
fanineralist
gactionalist
hedephardist
ibaidalomist
kaththedrist
lanronatrist
macalomanist
nalasmandist
oinylandrist
pacreologist
quahentalist
reamoplagist
scatometrist
tamalogicist
undongiodist
vaxgallowist
whaslugnoist

### time for action

So, what are your plans for the future? If you're one of those who feel the question is unanswerable with the current option of words, consider fallupify as a course of action. And if you're feeling less productive but still want to appear like you're doing something, go ahead and nonerify.

—6—
abdify
baeify
cafify
dalify
endify
falify
galify
havify
ichify
kerify
leaify
maeify
nozify
oodify
palify
rasify
saxify
talify
untify
velify
—7—
afffify
bektify
cackify
dankify
eddeify
fandify
gachify
haurify
icacify
jympify
kersify
maccify
necrify
orchify
pelnify
quacify
reanify
seerify
tactify
vettify
whizify
—8—
beennify
cacktify
daishify
encutify
falutify
ganprify
hemarify
inchoify
keethify
ledidify
nonerify
oliquify
paltrify
reassify
sebanify
tavetify
unducify
veridify
—9—
bedaunify
caesonify
debissify
ecquilify
fallupify
gaaggrify
helantify
idrobrify
lacrusify
magvalify
omphonify
panratify
rejursify
sedfigify
tabjacify
uncepnify
wipencify
—10—
baltharify
caccretify
degiwhsify
enbificify
flodistify
gedlecrify
hevamilify
icertilify
leliponify
mannentify
nirgresify
oframarify
pencomnify
reflectify
temerdrify
uncmencify
wontistify
—11—
bemiflacify
calapaulify
deflestrify
ghakabilify
incandacify
manciculify
outsentrify
precrictify
rewerricify
thoullutify
ungargunify
—12—
anticulptify
bennebersify
cenorshanify
disommencify
eodyshotrify
michthillify
prirosaurify

### no whimpers here, just bangs—here come the –omes

I've previously been reference as making fun —omes in the New York Times article "Ome," the Sound of the Scientific Universe Expanding.

Well, now I have even more —omes to ridicule. Hilariously, femome and manome are options!

—6—
agrome
balome
bdbome
cacome
canome
darome
debome
eccome
eedome
farome
femome
gamome
garome
hedome
horome
indome
infome
konome
lamome
manome
octome
outome
pacome
palome
refome
theome
tibome
untome
venome
—7—
battome
bectome
callome
cannome
decrome
dedrome
ecthome
edihome
fammome
faysome
hargome
hidbome
icisome
inclome
jansome
kabrome
kawsome
lammome
maarome
nefiome
norcome
pagwome
pastome
rowcome
ruphome
sentome
soysome
tachome
taksome
unclome
veesome
vootome
weysome
—8—
abrosome
abzizome
becksome
becplome
caanrome
caconome
dacktome
dedesome
ecottome
eedotome
fandsome
farksome
gabarome
gabosome
heamsome
hedesome
ichanome
ichilome
kelatome
kelaxome
lailsome
lentsome
maarsome
machsome
nalatome
oleinome
opeccome
paimsome
palesome
ratesome
reffrome
sefisome
sepreome
tacosome
tebanome
unchrome
voortome
wiserome
—9—
acdeasome
acleusome
baudylome
beconsome
caliocome
caliosome
dactogome
dalatcome
eccistome
ecpostome
facoscome
fanersome
gaberrome
gaenesome
heezatome
helaptome
icesimome
iddicrome
kannisome
kauffsome
leneanome
londarome
mabersome
macrasome
necrotome
noenesome
orphecome
palmetome
paloktome
reciitome
retrotome
sepostome
slondmome
tarrocome
tealocome
udnygrome
undretome
veltosome
vewedsome
whealsome
whieltome
—10—
alonoptome
angiagrome
bejurgsome
belarizome
cabblesome
cabroorome
danamasome
decreotome
egieuctome
enoccotome
fanzoflome
fattstrome
gamomonome
hecalotome
helapptome
incanizome
inclostome
jabimatome
kainersome
karochrome
lelitoxome
macenolome
mackahsome
nondyctome
nontentome
oholostome
omyrhysome
panapesome
pecoefsome
reclustome
regpensome
seerensome
segionsome
teasodrome
unberwrome
unchlesome
verevisome
vhroustome
whimsesome
wirblosome
—11—
acctiphsome
acynotopome
bebrolysome
beeltectome
calcomotome
dakesthrome
danjoirsome
ecpoplesome
eganulitome
fepelessome
fertostrome
gacreiovome
gactorrhome
hansboysome
hefrodesome
indaloctome
indanactome
mackmotsome
malaversome
nenacestome
noranectome
omynymysome
oppolionome
pacuderhome
paliplesome
risionfsome
spiguissome
stiltlesome
teentrotome
tenolustome
ungrefysome
unraxtchome
venianizome
vinacectome
wortarisome
—12—
acctiomyxome
actiprectome
basziversome
bebromyerome
calcoriotome
candochotome
deconnersome
decturessome
ecophytotome
egytherotome
fodbirdisome
fordharksome
gellardesome
gellmarksome
hernoelotome
hotchielsome
incyprossome
internantome
kalachiteome
keonylminome
lamphylotome
mackbanrsome
mafantectome
ochrophotome
octidraitome
pathymiotome
pecthenizome
rillpeypwome
rimentorrome
spotupersome
tarilocosome
thyrapioxome
undrarthrome

VIEW ALL

# Ensemble methods: Bagging and random forests

Mon 16-10-2017
Many heads are better than one.

We introduce two common ensemble methods: bagging and random forests. Both of these methods repeat a statistical analysis on a bootstrap sample to improve the accuracy of the predictor. Our column shows these methods as applied to Classification and Regression Trees.

Nature Methods Points of Significance column: Ensemble methods: Bagging and random forests. (read)

For example, we can sample the space of values more finely when using bagging with regression trees because each sample has potentially different boundaries at which the tree splits.

Random forests generate a large number of trees by not only generating bootstrap samples but also randomly choosing which predictor variables are considered at each split in the tree.

Krzywinski, M. & Altman, N. (2017) Points of Significance: Ensemble methods: bagging and random forests. Nature Methods 14:933–934.

Krzywinski, M. & Altman, N. (2017) Points of Significance: Classification and regression trees. Nature Methods 14:757–758.

# Classification and regression trees

Mon 16-10-2017
Decision trees are a powerful but simple prediction method.

Decision trees classify data by splitting it along the predictor axes into partitions with homogeneous values of the dependent variable. Unlike logistic or linear regression, CART does not develop a prediction equation. Instead, data are predicted by a series of binary decisions based on the boundaries of the splits. Decision trees are very effective and the resulting rules are readily interpreted.

Trees can be built using different metrics that measure how well the splits divide up the data classes: Gini index, entropy or misclassification error.

Nature Methods Points of Significance column: Classification and decision trees. (read)

When the predictor variable is quantitative and not categorical, regression trees are used. Here, the data are still split but now the predictor variable is estimated by the average within the split boundaries. Tree growth can be controlled using the complexity parameter, a measure of the relative improvement of each new split.

Individual trees can be very sensitive to minor changes in the data and even better prediction can be achieved by exploiting this variability. Using ensemble methods, we can grow multiple trees from the same data.

Krzywinski, M. & Altman, N. (2017) Points of Significance: Classification and regression trees. Nature Methods 14:757–758.

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

Altman, N. & Krzywinski, M. (2015) Points of Significance: Multiple Linear Regression Nature Methods 12:1103-1104.

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

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Model Selection and Overfitting. Nature Methods 13:703-704.

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Regularization. Nature Methods 13:803-804.

# Personal Oncogenomics Program 5 Year Anniversary Art

Wed 26-07-2017

The artwork was created in collaboration with my colleagues at the Genome Sciences Center to celebrate the 5 year anniversary of the Personalized Oncogenomics Program (POG).

5 Years of Personalized Oncogenomics Program at Canada's Michael Smith Genome Sciences Centre. The poster shows 545 cancer cases. (left) Cases ordered chronologically by case number. (right) Cases grouped by diagnosis (tissue type) and then by similarity within group.

The Personal Oncogenomics Program (POG) is a collaborative research study including many BC Cancer Agency oncologists, pathologists and other clinicians along with Canada's Michael Smith Genome Sciences Centre with support from BC Cancer Foundation.

The aim of the program is to sequence, analyze and compare the genome of each patient's cancer—the entire DNA and RNA inside tumor cells— in order to understand what is enabling it to identify less toxic and more effective treatment options.

# Principal component analysis

Thu 06-07-2017
PCA helps you interpret your data, but it will not always find the important patterns.

Principal component analysis (PCA) simplifies the complexity in high-dimensional data by reducing its number of dimensions.

Nature Methods Points of Significance column: Principal component analysis. (read)

To retain trend and patterns in the reduced representation, PCA finds linear combinations of canonical dimensions that maximize the variance of the projection of the data.

PCA is helpful in visualizing high-dimensional data and scatter plots based on 2-dimensional PCA can reveal clusters.

Altman, N. & Krzywinski, M. (2017) Points of Significance: Principal component analysis. Nature Methods 14:641–642.

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

# $k$ 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.