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information: beautiful


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


design + visualization

EMBO Journal 2011 Cover Contest

non-scientific image entry - fiber optics - honourable mention

For the EMBO Journal 2011 Cover Contest, I prepared two entries, one for the scientific category and one for the non-scientific category.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The non-scientific entry is abstract photo of fiber optics. The scientific entry was an information graphic showing a hive panel of genomic annotations in human, mouse and dog genomes. The hive panel is based on the use of the newly introduced hive plot.

About the EMBO Journal Cover Contest

The EMBO Journal non-scientific cover prize is awarded for the most interesting and beautiful image made outside the lab. Contestants may submit, for example, photos or artistic impressions of wildlife animals, plants or landscapes. Particularly welcome will also be hand or computer-generated paintings or drawings (or photographs of other works of art) related to a biological or molecular biological topic.

The EMBO Journal scientific cover prize is awarded for the most captivating and thought-provoking contribution depicting a piece of molecular biology research. Entries can include light or electron micrographs, 3D reconstructions or models of biological specimen or molecules, spectacular artefacts collected in the lab, original new views of lab equipment (but not of colleagues!), or other research-based images to be of interest to molecular biologists.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Examples of scientific cover image winners from previous years. My Circos image (top left) won the 2010 scientfic image cover category. (see more)

2011 Contest and Image Status

The 2011 winners have been announced. The scientific image winner was Heiti Paves, who submitted a confocal image of an Arabidopsis thaliana anther filled with pollen grains. The non-scientific winner was Dieter Lampl, with his "Blue Ice" photo — a glacier in Los Glaciares National Park in Patagonia.

My non-scientific entry (photo of fiber optics) received honourable mention and was included in the Favourites of the Jury gallery.


non-scientific image entry - fiber Optics

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Four genomes — The illustration, originally part of a poster, shows syntenic relationships between human, chimpanzee, mouse and zebrafish genomes. Curved links encode sequence similarity and outer data tracks represent consensus similarity statistics and orthologous genes. The cover image shows a detail of a visualization prepared with the free genome comparison tool, Circos. (EMBO Journal - Best Scientific Cover - 2010)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
My 2011 non-scientific fiber optic entry appears in a gallery of a small selection of images that were shortlisted by the jury of The EMBO Journal Cover Contest 2011. Images were selected with the aim of highlighting the diversity and quality of submissions in both, the scientific and non-scientific categories of the contest. (view gallery)

My non-scientific entry was an abstract image photo of fiber optics. It received honourable mention and were included in the Favourites of the Jury gallery.

The motivation and technical details behind these photos are described here.

The other entry, a scientific image, was an information graphic showing a hive panel of genomic annotations in human, mouse and dog genomes, based on the use of the newly introduced hive plot.

My submission of a large Circos figure for its cover (see right), which was originally designed for a poster that introduced Circos, was awarded the 2010 EMBO Journal best scientific cover prize.

entry details

Some time ago, I did a personal project of photos of fiber optic strands. These worked out well. I had not done anything with these images, and thought they would make a competitive entry into the cover contest.

Fiber optic lamp photos / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
My first attempt at photographing fiber optic lamp strands. These images were bundled into a set called Diving Horror, because of their likeness to creepy tentacles of creatures of the deep. (more images on flickr.)

I revisited the fiber optic lamp with a higher resolution camera (Canon 5D — original images were from a Canon 20D) and a dedicated macro lens (Sigma 150mm f2.8 EX APO DG HSM Macro) (original images were shot with the Canon EF 24-70L).

From these new images, shown below, I created five EMBO Journal cover submissions.

Fiber optic lamp photos / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Second attempt at photographing fiber optic lamp strands. (more images on flickr.)

The submissions would render on the cover as shown below.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Photos of fiber optic lamp strands. (More images on flickr.)

2011 EMBO Journal cover contest — fiber optic lamp submission images

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2011 EMBO Cover contest submission — macro photograph of fiber optic lamp strands. (More images on flickr.)

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2011 EMBO Cover contest submission — macro photograph of fiber optic lamp strands. (More images on flickr.)

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2011 EMBO Cover contest submission — macro photograph of fiber optic lamp strands. (More images on flickr.)

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2011 EMBO Cover contest submission — macro photograph of fiber optic lamp strands. (More images on flickr.)

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2011 EMBO Cover contest submission — macro photograph of fiber optic lamp strands. (More images on flickr.)

VIEW ALL

news + thoughts

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



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