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words: meaningful


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


language + fiction

Dark Matter of the English Language—the unwords

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

unwords

This is the biggest list—887,716 English unwords, generated from a list of 479,000 list of English words.

Medically compelling words, defined by some interesting suffixes (e.g. phobia), are presented in the undiseases section.

The list of unwords is filtered to exclude any hyphenation and apostrophes. I've made no attempt to stem the words—for example, to remove what appear to be plural forms.

—3—
abh
baf
cah
daf
ebz
fal
gaa
hea
ibb
jaa
kaa
laa
maq
nai
oaa
paa
qqu
raz
sbz
tah
uea
vae
wel
xni
yab
zaa
—4—
abae
bace
caak
dabn
eacc
faha
gaag
hadr
ibal
jabo
kaah
laep
maal
naan
oaah
paan
qeit
raef
sabm
taak
ubed
vace
wanr
xake
yada
zaac
—5—
ababo
babne
caang
dabna
eable
fadus
gaaba
habwe
iaced
jaacm
kaaab
ladde
maaco
naars
oaads
paabs
qeisa
radlo
sabil
tabal
udsel
vaard
wance
xibne
yebon
zaach
—6—
aachan
babold
caabah
dabnar
eables
facont
gaaada
habway
iacect
jaaday
kaadey
laclic
maakin
nabens
oaagee
paabay
quaben
raldos
sabane
taaque
udnygs
vabara
wamfly
xennis
yeamyl
zaaced
—7—
aamreid
bacadil
caamale
daajarg
eabemic
facomas
gaabags
habwest
iacects
jaabate
kabadis
laclics
maakite
naaquan
oaalake
paaband
qeitage
raemony
sabanal
tabater
ubecide
vabitat
walcama
xeaiate
yazoris
zaacure
—8—
aachtony
bacacree
caamahoo
dabnasas
eabidion
facomate
gaabacte
habwoofs
iaccuate
jaabarly
kabalion
lacloses
maaglive
nabaneer
oaaldiza
paabands
qeinator
ramaness
sabaless
tabakton
uangorin
vabipans
wameless
xeywords
ydneress
zaachner
—9—
abackiest
bacaiting
caambaria
dabnaring
eablishes
facomania
gaaadedia
habwuiter
iacectium
jaadneway
kaahajout
laclidian
maacchial
nabaneest
oabarfort
paachting
qeinatory
raemoning
sabalaryl
taabotzle
udnyglyph
vabippary
wandwares
xendomate
yeesonian
zaachnick
—10—
aadophanic
bacaitings
caamahooed
daakarillo
eabialysis
facolatine
gaabustail
hacchotest
iabiaceous
jaabarness
kaagnation
lacloparia
maagillism
naantalous
oaadrooter
paacrasses
qturegrasm
raemonetts
sabalidase
tabiricate
uaragrable
vabipagean
wandwatark
xickinfert
yebonesses
zaacricker
—11—
aadophacone
bachplasted
caamahooing
dabnationer
eabidiology
facoscophic
gaablerment
hacchotized
ibbicarized
jaacoughter
kabstomment
lacloparian
maagilineal
nabionmaker
oabatrophic
paalligeret
quacidioned
raemonetted
sabalidated
tabisteculi
ubedability
vabipparies
wandwauning
yennishness
zaadishness

—12—
aadophaliums
bachplasters
caamatoscope
dabnariolate
eablansmidal
facolubility
gaablikeness
hadeconwight
iaceulineson
jabification
kabheseeding
lacluntation
maachanavite
nabionership
oabatrophoic
paalodochord
quacefulness
raemonetting
sabalidation
tabjaceously
udnygromissi
vabippareism
wannachanced
yepenousness
zaachnickesh
—13—
abaugescibies
bachanoloical
caamatoscopia
dabnationency
eablamanchium
facontagonite
gaaldrimahedy
hadiaboroness
iachomaniasis
jabimarometer
kabromedizone
lacrusization
maagilinolity
nabitariously
oacantraceous
paanolypaline
quaculiferous
rambreaulowed
sabanucelican
tabjaceousies
uemanociation
vabipparyment
wannachancing
xickingerline
yeryllinemism
zenbhidievine
—14—
abascressionly
bacecification
caamatoscopian
dabnationistic
eacenfigenised
facolubilities
gaarolatically
hacchotization
ibbicarization
jabimarosacily
kaihowfuklench
ladoculsionism
maakursismally
nactualization
oachuloglosynt
paalojiglactry
qansinquirosis
raemoologition
sabracoethygen
tabjaceousness
udnygromoidium
vaceheartedner
wannefictingly
yepfyrotarical
zalaedritellay
—15—
aberyatheberter
badainderedness
caanomaticnatic
dabstologically
eablaneonareous
facophthillahid
gaannificalpous
harrigonishment
ibbicarizations
jaberotherapeus
kabaelesiculous
lamphosescenter
maakuristically
nadastifferling
oachanosophagia
paaminguasechos
quaddractionism
rastarizingness
sabridaboletric
tacdicalization
uemalameisarian
vaceheartedness
wanenolaphonous
ynderapodometer
—16—
abriladothronous
bakoographically
cabalothpization
dactalisingulate
eachematocarpous
fahfoiestheskule
gactiometrically
hadiaboastowning
ibbiographically
jabimoatalophesy
kawaddauriaceous
lamolatrological
maarographically
nadarogenesomacy
ochydramodenomic
paalodorrhaphrin
quaddractimation
rameocrasiferous
saconopasinectic
tachocroytograph
uemanobiological
vegonsculonizing
wandwatalvenwood
yermacographical

—17—
abyranchiatically
baeloazronahumian
cabritometrically
dabsomelscescence
eaticurlegittrial
failonesheeptress
gaarozooenciously
haudacycocetolite
iceiomorphization
jabufedereschilde
kawcayanganasasim
lamphophoraldumal
mabilioistarthine
naniquinpuffyaire
oachryptostecrous
pablinopolization
reaptillification
sabucetarculation
tachyphoschistomy
uemalodeativeness
vegonsculvinalize
whimsellepsitrify
zhonoussitization
—18—
abyinosteracysting
bagoquincification
caccopstherization
dalatosephostomies
eacenfiginembiasis
fantrofingerniness
gaeragotisalgialis
hecalocorpossomies
icemohydrosemuline
jacunceuralization
kaitcholenthythris
leitocartronograph
mabersanthropalous
necrotimoperitoric
oachryptosphorenal
padlandfactiveness
quebunouscephalous
rearmidephenolitic
secrotomyzographic
tachycardometyrous
uenitanisticalness
vellheolinguachure
wanpimmenabilities
yermahvoacetoscoma
—19—
aberyatheldocration
bahandaclionishness
cacethcoalyscraphic
dalanaphorrhachehic
eccolatibidological
fansomosacsimatical
gadiophoressessiric
hadiaboastripangous
icancraticulosality
kelocericostosthene
leliarystallization
mabersanthrological
nercrodyomeberation
odersotenographeral
palanreoncounterite
reconsosentializing
semidezalomolegneal
tagatomotheremotean
unberwrightenedness
velciarcomaryphitis
wenigrassiprulation
—20—
abyogloystelmoiditic
bedaginettaceousness
cachofigarpossometer
dactalismandolitrate
eachtrandrologistric
fedophosphotological
ganroldinocontonosus
henromycorrhhosoidal
iceiolarchythmiamoad
jabimoamordohechmood
kedipercustitalities
macalcromanchamiency
nondenchistentiation
ochyolorenentontagen
palnareoblothiameric
quebrobiogrhecartise
rerepervimestination
scartiometricolastic
talmacoparphocentone
undiffosoliopenazing
velluquithoreterical

—21—
accantosapherochiasis
bemicoloblacontomatic
cachoegnscophystomies
dalificationlessorate
eachygasonaryocarcour
fedorographistrolisan
gadicoarchioscopelous
hedetotypolorphograph
incentularsomoterasia
madychopheniaccuronal
nodaterationalization
octudioflorouphotopia
pechovobbintropnonaic
rechmodechmometrogamo
sabrilostehyricullite
tartosophazoloopellin
undyltracerbandenesol
vereurocerbordohycent
—22—
agniotrophocyclethrine
begipothranmaceraceous
caconsochnecoloscephal
danduocephalomosomeric
egyperyphopothermosate
frachipothostereinitis
gasciocatiosocrasilian
indaaldesitrehenalgian
malcuptisemothanculous
nercroplerographically
oecraumatressesthenibe
peaslothecamonometanic
quebrobiogrhecaryminal
reflesimomolithrosilic
secrotopoidontromastic
tatinochlogacyphologue
undongrensibiliatlemen
—23—
acaraertepitulpeptation
bembenocolbogistromatal
caciceptopomethyleneous
decletopyrrachystolmial
eiyandsceprinainization
galuardicolaristrolcoma
icetoplexistrosthhrenia
lucarucorynchomazintrin
macnchydrogeneumoseable
nonertertabolialization
orchopyrimosalgarositis
pantancoloideculaminone
tanacondrachonomethanic

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news + thoughts

Machine learning: a primer

Tue 05-12-2017
Machine learning extracts patterns from data without explicit instructions.

In this primer, we focus on essential ML principles— a modeling strategy to let the data speak for themselves, to the extent possible.

The benefits of ML arise from its use of a large number of tuning parameters or weights, which control the algorithm’s complexity and are estimated from the data using numerical optimization. Often ML algorithms are motivated by heuristics such as models of interacting neurons or natural evolution—even if the underlying mechanism of the biological system being studied is substantially different. The utility of ML algorithms is typically assessed empirically by how well extracted patterns generalize to new observations.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Machine learning: a primer. (read)

We present a data scenario in which we fit to a model with 5 predictors using polynomials and show what to expect from ML when noise and sample size vary. We also demonstrate the consequences of excluding an important predictor or including a spurious one.

Bzdok, D., Krzywinski, M. & Altman, N. (2017) Points of Significance: Machine learning: a primer. Nature Methods 14:1119–1120.",

...more about the Points of Significance column

Snowflake simulation

Tue 14-11-2017
Symmetric, beautiful and unique.

Just in time for the season, I've simulated a snow-pile of snowflakes based on the Gravner-Griffeath model.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
A few of the beautiful snowflakes generated by the Gravner-Griffeath model. (explore)

Gravner, J. & Griffeath, D. (2007) Modeling Snow Crystal Growth II: A mesoscopic lattice map with plausible dynamics.

Genes that make us sick

Thu 02-11-2017
Where disease hides in the genome.

My illustration of the location of genes in the human genome that are implicated in disease appears in The Objects that Power the Global Economy, a book by Quartz.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The location of genes implicated in disease in the human genome, shown here as a spiral. (more...)

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.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
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.

Background reading

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

...more about the Points of Significance column

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.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
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.

Background reading

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.

...more about the Points of Significance column

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).

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
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.