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I want to join Gaël in thanking you for saying thanks.<br>
It's great to see appreciation of the work that the scientific
python community does.<br>
I don't think I've seen anyone cite scipy in their research work,
even though it is the backbone for so many papers.<br>
It's important for us that the academic environment recognizes
software contributions, because many<br>
of us rely on academic funding to do this work.<br>
<br>
Best,<br>
Andy<br>
<br>
<div class="moz-cite-prefix">On 03/22/2017 01:10 AM, Brown J.B.
wrote:<br>
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<div>To all organizers, developers,
and maintainers involved in the
Scikit-learn project,<br>
<br>
</div>
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.<br>
<br>
</div>
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.<br>
<br>
</div>
The paper's success is underpinned by
the use of Scikit-learn's
RandomForestClassifier implementation
combined with other techniques developed
in the work.<br>
</div>
Therefore, it is a by-product of the
volunteerism, hard work, and dedication by
those involved in scikit-learn.<br>
<br>
</div>
As the senior author of this study, I wish
to share my great appreciation for your
efforts.<br>
</div>
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.<br>
<br>
</div>
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.<br>
<br>
</div>
We have made the paper open access, and hope that
such will inspire this community as well as those
in applied sciences.<br>
You will see that the open source software
community has been listed in the Acknowledgments.<br>
</div>
Certainly, we would welcome even the most casual of
comments about the paper.<br>
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<div><br>
</div>
The paper can be retrieved from here:<br>
<a moz-do-not-send="true"
href="http://www.future-science.com/doi/abs/10.4155/fmc-2016-0197">http://www.future-science.com/doi/abs/10.4155/fmc-2016-0197</a><br>
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With kindest regards and sincere appreciation,<br>
</div>
J.B. Brown<br>
</div>
Kyoto University Graduate School of Medicine<br>
Junior Associate Professor and Principal Investigator<br>
</div>
<a moz-do-not-send="true"
href="http://statlsi.med.kyoto-u.ac.jp/%7Ejbbrown">http://statlsi.med.kyoto-u.ac.jp/~jbbrown</a><br>
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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.<br>
</div>
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