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Sun is on my face ...a beautiful day without you.Royskoppbe apartmore quotes

poetry: beautiful


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


art + literature

daily quotation server archives

In the late 90’s I started (a good decade for starts) a daily quotation server project at www.quoteserver.ca. The domain is now defunct—some pages are partially viewable at the Way Back Machine.

Below is the list of quotes I had collected by the end of the life of the project. Most are about love—duh—and a few are jolly jests from funny trenches. You know, that place where mustard gas makes your eyes water.

The quotes weren’t scraped from quote archives—each is meaningful and hand-picked.

the quote archive

And now for full list of 1,600 other things worth reading. Such as everything Dorothy Parker has written and ... yes, even the Pinky and Brain quotes, which are a special kind of special.

Quote collections about love, heart, desire, life, death, god, mind, science.

Feeling lucky? Read 10 random quotes. Well, will you, punk?

A subset of random quotes

224
Sarcasm: barbed ire.
237
Anybody who sleeps like a baby doesn’t have one.
265
Most of us carry our own stumbling block around with us; we camouflage it with a hat.
667
I was angry with my friend
I told my wrath, my wrath did end.
I was angry with my foe:
I told it not, my wrath did grow.
William Blake
A Poison Tree
723
What are the wild waves saying
Sister, the whole day long,
That ever amid our playing,
I hear but their low lone song?
Joseph Edwards Carpenter
What are the Wild Waves Saying?
734
Cunning is the dark sanctuary of incapacity.
Earl of Chesterfield
1051
It doesn’t much signify whom one marries, for one is
sure to find next morning that it was someone else.
Samuel Rogers
Table Talk
1071
Jaques: I do not like her name.
Orlando: There was no thought of pleasing you when
she was christened.
William Shakespeare
As You Like It, I.vii.283
1079
I can get no remedy against this consumption of the purse: borrowing only lingers and lingers it out, but the disease is incurable.
William Shakespeare
Henry IV, Part II, I.ii.268
1517
Girls we love for what they are; young men for what they promise to be.
Johann Wolfgang von Goethe
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