[scikit-learn] Note of appreciation to Scikit-learn team

[Brown J.B.]

To all organizers, developers, and maintainers involved in the Scikit-learn
project,

I would like to share a recent article that researchers from MIT, ETH, and
Kyoto University (myself) have published about building efficient models
for drug discovery and pharmaceutical data mining.

In short, it demonstrates through replicate experiment that neither big
data nor complex AI such as deep learning are necessary for efficient drug
discovery, and that active learning can guide/assist decision making
processes in the real world.

The paper's success is underpinned by the use of Scikit-learn's
RandomForestClassifier implementation combined with other techniques
developed in the work.
Therefore, it is a by-product of the volunteerism, hard work, and
dedication by those involved in scikit-learn.

As the senior author of this study, I wish to share my great appreciation
for your efforts.
While I am strongly limited in time and can barely contribute to this
community, I cannot thank all of you enough for your work - it has made an
impact.

We are working on theoretical extensions of the work now, as well as
pushing the technology forward in applied discovery sciences (in
agricultural, pharmaceutical, and medical areas).  In the theory and
real-world applications, scikit-learn is indispensible.

We have made the paper open access, and hope that such will inspire this
community as well as those in applied sciences.
You will see that the open source software community has been listed in the
Acknowledgments.
Certainly, we would welcome even the most casual of comments about the
paper.

The paper can be retrieved from here:
http://www.future-science.com/doi/abs/10.4155/fmc-2016-0197

With kindest regards and sincere appreciation,
J.B. Brown
Kyoto University Graduate School of Medicine
Junior Associate Professor and Principal Investigator
http://statlsi.med.kyoto-u.ac.jp/~jbbrown

PS - To those of you involved in the matplotlib, scipy, and numpy projects,
your forwarding of this to those projects would be appreciated.  They were
also critical.
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