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Trance opera—Spente le Stellebe dramaticmore quotes

words: fun


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

These are maladies sampled from my list of about 883,500 English unwords.

Medical jargon has never been so opaque.

phobias

Don't feel like going to that party? It could be a conveniently timed attack of ictophobia or myconomascophobia.

—9—
abophobia
beophobia
cgyphobia
deophobia
epophobia
etaphobia
kanphobia
miophobia
—10—
alraphobia
beexphobia
bemophobia
cemophobia
cetophobia
dalophobia
degephobia
eccophobia
eedophobia
ficaphobia
galophobia
ganyphobia
helophobia
hilophobia
ictophobia
illyphobia
kelophobia
manophobia
menophobia
oxiophobia
perophobia
pisophobia
tegophobia
tenophobia
—11—
acctophobia
alonophobia
beonyphobia
beprephobia
caanophobia
cachophobia
deginphobia
deinephobia
ecquephobia
egycaphobia
geceophobia
geneophobia
inymophobia
iponephobia
kennophobia
lleiophobia
mactophobia
maenephobia
oeriophobia
optigphobia
paliophobia
parrophobia
reglophobia
scavophobia
starophobia
taloaphobia
teonaphobia
—12—
antotophobia
begatophobia
birroophobia
cabolophobia
canchophobia
dachoiphobia
debriophobia
elimiaphobia
enconophobia
fansoaphobia
fatenophobia
gedicophobia
gercomphobia
heralephobia
hyscrophobia
inchrophobia
ingonophobia
konetophobia
lamphophobia
lepinophobia
menoxiphobia
mezalophobia
neponophobia
oppetophobia
parkiophobia
preotaphobia
saconophobia
temphophobia
tilophphobia
uemanaphobia
vobitophobia
—13—
achythophobia
acteliophobia
becrosophobia
benoprophobia
cembaniphobia
chegenophobia
dambotophobia
denyarophobia
ecenytophobia
eerichophobia
fatenylphobia
galarsophobia
gannotophobia
hotoidophobia
iconopephobia
inbictophobia
kentylophobia
mallygophobia
manismyphobia
nonditophobia
oophalaphobia
ototarophobia
peltosaphobia
penconophobia
rechapophobia
rechorophobia
stimotophobia
stomosophobia
testanophobia
thidotophobia
vertulophobia
—14—
ancometophobia
antionsyphobia
cenctopaphobia
cheinonophobia
dempabiophobia
denolaryphobia
ecenytrophobia
edalycrophobia
gebrocomphobia
geoparyophobia
heegoscophobia
hindancophobia
ichicotyphobia
ideuschophobia
maromaniphobia
myolegrophobia
octonenophobia
onechotophobia
persectophobia
phetholyphobia

And here are some very long fears. I'm sure that one of these is the fear of long fears.

fires in the brain

Those thoughts you're having might not be yours. It could be talmatomania.

—7—
demania
gomania
namania
—8—
adomania
diomania
dipmania
fermania
giomania
maomania
peomania
thomania
—9—
adiamania
agnymania
benamania
brepmania
calemania
capumania
dipamania
disomania
egzomania
expomania
facomania
galamania
gamemania
hytomania
iglimania
ihyomania
ledimania
macemania
mallmania
najumania
pedamania
poelmania
susimania
tegomania
tigymania
uztimania
vicimania
—10—
agniomania
ampiamania
besmomania
ceedomania
centomania
deglimania
deykomania
eodaimania
fathemania
fertomania
galarmania
geolemania
inasamania
inorimania
leliomania
maaromania
mariomania
nedramania
nodiomania
plolomania
precamania
sereimania
tachymania
tattomania
viliomania
—11—
acculamania
antisumania
beedromania
beranimania
cemchomania
chetiomania
degonomania
demitemania
endonomania
eyleromania
flodiomania
gastdomania
gelyzymania
ingatomania
lamphomania
lamphymania
malatomania
maracamania
opostomania
perciomania
prathomania
teouromania
tieanomania
—12—
anchodymania
anchonomania
cirminomania
conchromania
degotrsmania
demminomania
encoctomania
eyotoxomania
gremgormania
mardisimania
mibeliomania
nenadiomania
parmulamania
pestriomania
talmatomania
thyminomania
wirgilamania
—13—
bepporanmania
berandromania
caconylymania
calebitomania
deborphomania
icepatromania
inferonomania
megazidomania
pecatisomania
peribacomania
seergenamania
stentosomania

stuff is definitely wrong

I'm not sure what's going on, but it might be an acute attack of ophalosis or that chronic icabulosis you've been struggling with.

—5—
cosis
gosis
iosis
mosis
—6—
axosis
buosis
byosis
ciosis
coosis
diosis
ecosis
egosis
feosis
geosis
idosis
irosis
keosis
meosis
moosis
pathyc
pathym
taosis
tiosis
—7—
algiang
algiate
bedosis
beeosis
calgias
cavosis
dehosis
didosis
edosist
eemosis
facosis
ferosis
galosis
gasosis
hedosis
hemosis
ichosis
idaosis
madosis
magosis
odiosis
odlosis
panosis
pecosis
roposis
saxosis
tacosis
talosis
vinosis
vorosis
—8—
acshosis
ademosis
bemposis
benosise
cactosis
caliosis
deagosis
demiosis
ecosises
egglosis
fattosis
fedlosis
gareosis
gariosis
harrosis
helposis
icerosis
ichnosis
leliosis
macosism
mactosis
nefkosis
neocosis
obbiosis
ocirosis
pathybic
pathymia
rechosis
tachosis
tendosis
—9—
acctrosis
acyclosis
balolosis
befalgian
cacacosis
cafulosis
danknosis
decarosis
echenosis
econeosis
falgnosis
fanulosis
gabarosis
galoxosis
heepathyl
hegriosis
icemosism
ichapathy
kelutosis
kenatosis
lamomosis
lebytosis
mabacosis
macalosis
nenylosis
nercrosis
odiosises
olohosism
panosises
partosism
quoetosis
regoosism
rhagnosis
saconosis
saumalgia
tacopathy
talthosis
undylosis
vhilalgia
vhinnosis
—10—
acaporosis
aceutiosis
bariakosis
becrotosis
cabesmosis
cachinosis
daiocrosis
daiphyosis
ecraniosis
eeminalgia
falogiosis
fedophosis
gaggonosis
gamancosis
heltonosis
heniflosis
icabulosis
icayulosis
kasphosise
kemoptosis
lephanosis
maccolosis
macosistic
namancosis
neocosises
oditopathy
omosinosis
palanrosis
panhidosis
rechoposis
rechosises
segiphosis
setactosis
tahradosis
taronosism
unodylosis
vhitatosis
vhoripathy
—11—
acdioenosis
actypyrosis
ballfrosise
becosistate
cacymalosis
caliphiosis
dalmatosise
dalomicosis
econeosises
ecrpharosis
facunolosis
fadiettosis
gacochrosis
gadersiosis
heleignosis
helipolosis
ibbiognosis
ichabolosis
jabimarosis
kedamirosis
kelodialgia
lelidalosis
macolobosis
macomycosis
nacocynosis
nectianosis
octoperosis
octorsposis
palevomosis
palygulosis
recocolosis
reglogyosis
sabrichosis
saconosises
teclagnosis
tellodrosis
untochosise
vurocylosis
—12—
abzibellosis
achythoposis
bakiomatosis
bedolignosis
cabolophosis
caconoacosis
dansepicosis
dantachrosis
ecchonylosis
ecctometosis
fansoaphosis
fentivorosis
gaelopyrosis
ganactomosis
hecthlinosis
hedophonosis
icaneoptosis
icepleitosis
karocalcosis
keloecatosis
lelemosisine
lelephotosis
macophagosis
maerozooosis
naniquinosis
nescroblosis
octorschosis
olethydrosis
pastiosising
pastiosismes
rechopatosis
rechopyrosis
segamethosis
seprorinosis
taronosismic
tedophirosis
venotormosis
vicypartosis
—13—
abobiolanosis
abrophyclosis
bailligirosis
beetheritosis
caccoparcosis
caconylycosis
decantophosis
decretharosis
ecccombulosis
ecyphenolosis
fansoecosises
feritoverosis
gaoduroniosis
gecivericosis
helatosteosis
heneotermosis
icenaronrosis
icernicycosis
kaumebedrosis
kelumbeenosis
lamphymbiosis
lelemosiseous
maelmicolosis
mafectermosis
neodylemnosis
noriotyphosis
octonepidosis
oepetymstosis
paccurognosis
paliascilosis
rerexomatosis
rimipathylene
stammomycosis
stolosibeosis
tadiotennosis
talhagunnosis
uneloplerosis
unmedunulosis
vhoriolecosis
vyrteobulosis
xlocrodynosis
—14—
acciritorrosis
accorbotolosis
bacocentamosis
becrotenicosis
caliphogniosis
calmicocenosis
danduocardosis
dantachromosis
ectinoliphosis
eggenopertosis
fematosishalle
fenhhaocamosis
galettonemosis
gaslogastrosis
hendimorphosis
henoptermeosis
icliotoidlosis
iddomethemosis
karapiapulosis
kichymarenosis
maccombuscosis
macophomacosis
nercrodidiosis
nondexicalosis
omestospidosis
ooracholosises
pacolimidrosis
pastinooctosis
qansinquirosis
rechanucenosis
saxinifactosis
secrotopolosis
tachomorphosis
tanadavicuosis
vericatorrosis
vhioductylosis

it's probably just age

Most of these are natural conditions associated with aging. I know mediatopathy definitely is and it always makes my bellotalgia flare up.

—7—
byalgia
—8—
alealgia
dymalgia
gelalgia
mydalgia
myzalgia
tinalgia
—9—
aditalgia
coomalgia
cyonalgia
diptalgia
drycalgia
egyralgia
enaralgia
inoralgia
irivalgia
mochalgia
myolalgia
saumalgia
sempalgia
vhilalgia
—10—
antoralgia
camoxalgia
ceavoalgia
detotalgia
dogosalgia
eeminalgia
goemoalgia
hotodalgia
ictetalgia
opennalgia
paniyalgia
pumonalgia
—11—
bellotalgia
bontaralgia
cicroyalgia
cinodyalgia
digratalgia
diodymalgia
eppersalgia
kelodialgia
progetalgia
pyrponalgia
surianalgia
—12—
artaramalgia
ceminonalgia
chempemalgia
degoestalgia
dehyzydalgia
fidetisalgia
genaroyalgia
geolaycalgia
manchomalgia
oorcastalgia
—13—
addigothalgia
conomethalgia
coronydralgia
doiocystalgia
epalanthalgia
keneopatalgia
manciscialgia
merycromalgia
olettrosalgia
oxosinchalgia
phiorhicalgia
proodustalgia

—7—
gepathy
mipathy
pupathy
—8—
dempathy
deopathy
geopathy
oxypathy
penpathy
—9—
antopathy
betopathy
bilypathy
cypopathy
detopathy
dipapathy
endopathy
finypathy
henopathy
ichapathy
inrepathy
manopathy
melopathy
notopathy
orcopathy
oviopathy
phiopathy
prempathy
tacopathy
tebopathy
—10—
acynopathy
aderipathy
biplopathy
cecropathy
cectopathy
deptopathy
deylopathy
egenopathy
eodyopathy
geneopathy
geolopathy
illyopathy
maliopathy
meddopathy
oditopathy
perropathy
phelopathy
teadopathy
vhoripathy
—11—
cherropathy
conchopathy
delphopathy
demylopathy
euchlopathy
helloopathy
omperopathy
phogiopathy
tyoptopathy
—12—
blatalopathy
cepigropathy
comyslopathy
ditheropathy
enceptopathy
mediatopathy
mompulopathy
orchypopathy
perrctopathy
prophropathy
—13—
chaldipopathy
chillitypathy
driboatopathy
miteligipathy

VIEW ALL

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.