Martin Krzywinski / Genome Sciences Center / mkweb.bcgsc.ca Martin Krzywinski / Genome Sciences Center / mkweb.bcgsc.ca - contact me Martin Krzywinski / Genome Sciences Center / mkweb.bcgsc.ca on Twitter Martin Krzywinski / Genome Sciences Center / mkweb.bcgsc.ca - Lumondo Photography Martin Krzywinski / Genome Sciences Center / mkweb.bcgsc.ca - Pi Art Martin Krzywinski / Genome Sciences Center / mkweb.bcgsc.ca - Hilbertonians - Creatures on the Hilbert Curve
Without an after or a when.Papercut feat. Maiken Sundbycan you hear the rain?more quotes

dust: fun



In Silico Flurries: Computing a world of snow. Scientific American. 23 December 2017


visualization + design

Scientific American Graphic Science - Martin Krzywinski. / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca

Working with senior graphics editor at Scientific American Jen Christiansen, I have designed three Graphic Science visualizations for the magazine.

Scientific American Graphic Science - Martin Krzywinski. / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
December 2015. Composition of bacteria in household dust.
Scientific American Graphic Science - Martin Krzywinski. / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
June 2015. Relationship between genes and traits.
Scientific American Graphic Science - Martin Krzywinski. / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
September 2014. Similarity of human, Denisovan, chimp, bonobo, and gorilla genomes.

Men and Women Alter a Home's Bacteria Differently

An analysis of dust reveals how the presence of men, women, dogs and cats affects the variety of bacteria in a household

December 2015, Scientific American Volume 313, Issue 6

This collaboration with Jeanine Hunnicutt explored differences in household dust bacteria based on the gender and pet status of the occupants.

We have also written about the making of the graphic, for those interested in how these things come together.

Graphic by Martin Krzywinski and Barbara Jeanine Hunnicutt.

Catalogue of bacteria shapes by Barbara Jeanine Hunnicutt.

Source: Barberan A et al. (2015) The ecology of microscopic life in household dust. Proc. R. Soc. B 282: 20151139.

Scientific American Graphic Science - Martin Krzywinski. / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca

A Road Map to the "Volume Control" of Genes

Genes, traits and disease are linked in complex and surprising ways

June 2015, Scientific American Volume 312, Issue 6

Because sometimes only a network hairball will do.

Graphic by Martin Krzywinski.

Source: Integrative analysis of 111 reference human epigenomes. (2015) Nature 518:317.

Scientific American Graphic Science - Martin Krzywinski. / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca

Tiny Genetic Differences between Humans and Other Primates Pervade the Genome

Genome comparisons reveal the DNA that distinguishes Homo sapiens from its kin

September 2014, Scientific American Volume 311, Issue 3

A Scientific American blog entry "A Monkey's Blueprint" accompanies this piece. I also have a more detailed description with links to data sources.

You can also read more about Hilbert curves and creatures that live on it, Hilbertonians.

This design won a bronze award at Malofiej 23. For more information about Malofiej, see the SA Visual blog entry "There's No Infographic without Info (and other Lessons from Malofiej)".

Graphic by Martin Krzywinski, illustrations by Portia Sloan Rollings.

Scientific American Graphic Science - Martin Krzywinski. / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
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news + thoughts

Machine learning: supervised methods (SVM & kNN)

Thu 18-01-2018
Supervised learning algorithms extract general principles from observed examples guided by a specific prediction objective.

We examine two very common supervised machine learning methods: linear support vector machines (SVM) and k-nearest neighbors (kNN).

SVM is often less computationally demanding than kNN and is easier to interpret, but it can identify only a limited set of patterns. On the other hand, kNN can find very complex patterns, but its output is more challenging to interpret.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Machine learning: supervised methods (SVM & kNN). (read)

We illustrate SVM using a data set in which points fall into two categories, which are separated in SVM by a straight line "margin". SVM can be tuned using a parameter that influences the width and location of the margin, permitting points to fall within the margin or on the wrong side of the margin. We then show how kNN relaxes explicit boundary definitions, such as the straight line in SVM, and how kNN too can be tuned to create more robust classification.

Bzdok, D., Krzywinski, M. & Altman, N. (2018) Points of Significance: Machine learning: a primer. Nature Methods 15:5–6.

Background reading

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

Human Versus Machine

Tue 16-01-2018
Balancing subjective design with objective optimization.

In a Nature graphics blog article, I present my process behind designing the stark black-and-white Nature 10 cover.

Nature 10, 18 December 2017

Machine learning: a primer

Thu 18-01-2018
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 16-01-2018
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)

The work is described as a wintertime tale in In Silico Flurries: Computing a world of snow and co-authored with Jake Lever in the Scientific American SA Blog.

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