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

visualization + design

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

December 2015. Composition of bacteria in household dust.
June 2015. Relationship between genes and traits.
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.

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.

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.

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Statistics vs Machine Learning

Tue 03-04-2018
We conclude our series on Machine Learning with a comparison of two approaches: classical statistical inference and machine learning. The boundary between them is subject to debate, but important generalizations can be made.

Inference creates a mathematical model of the datageneration process to formalize understanding or test a hypothesis about how the system behaves. Prediction aims at forecasting unobserved outcomes or future behavior. Typically we want to do both and know how biological processes work and what will happen next. Inference and ML are complementary in pointing us to biologically meaningful conclusions.

Nature Methods Points of Significance column: Statistics vs machine learning. (read)

Statistics asks us to choose a model that incorporates our knowledge of the system, and ML requires us to choose a predictive algorithm by relying on its empirical capabilities. Justification for an inference model typically rests on whether we feel it adequately captures the essence of the system. The choice of pattern-learning algorithms often depends on measures of past performance in similar scenarios.

Bzdok, D., Krzywinski, M. & Altman, N. (2018) Points of Significance: Statistics vs machine learning. Nature Methods 15:233–234.

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

Bzdok, D., Krzywinski, M. & Altman, N. (2017) Points of Significance: Machine learning: supervised methods. Nature Methods 15:5–6.

Happy 2018 $\pi$ Day—Boonies, burbs and boutiques of $\pi$

Wed 14-03-2018

Celebrate $\pi$ Day (March 14th) and go to brand new places. Together with Jake Lever, this year we shrink the world and play with road maps.

Streets are seamlessly streets from across the world. Finally, a halva shop on the same block!

A great 10 km run loop between Istanbul, Copenhagen, San Francisco and Dublin. Stop off for halva, smørrebrød, espresso and a Guinness on the way. (details)

Intriguing and personal patterns of urban development for each city appear in the Boonies, Burbs and Boutiques series.

In the Boonies, Burbs and Boutiques of $\pi$ we draw progressively denser patches using the digit sequence 159 to inform density. (details)

No color—just lines. Lines from Marrakesh, Prague, Istanbul, Nice and other destinations for the mind and the heart.

Roads from cities rearranged according to the digits of $\pi$. (details)

The art is featured in the Pi City on the Scientific American SA Visual blog.

Check out art from previous years: 2013 $\pi$ Day and 2014 $\pi$ Day, 2015 $\pi$ Day, 2016 $\pi$ Day and 2017 $\pi$ Day.

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.

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.