December 2017 ToxSci: Combining Computational Approaches and Laboratory Research to Advance Toxicology



The current issue of Toxicological Sciences (December 2017, Vol. 160, Issue 2) includes articles that will be of great interest and importance to you. In this issue, ToxSci Editor-in-Chief (EIC) Gary W. Miller encourages you to take note of the articles that address the importance of combining computational approaches and high-quality laboratory research to advance toxicology.

“I recall conducting my first scientific studies, first in high school, then in college. The very first studies required a calculator to determine averages, but there was not a computer involved at any time. In college, computers were more important, but merely to “type” up the manuscript; they were not necessary for experimentation. Move forward a couple of decades and it is hard to fathom how one would perform experiments without the aid of computers to design primers, process streaming data, perform sophisticated statistical analyses, and generate scientific artwork for figures. As noted in this issue, it is becoming common to perform toxicological research using only computational approaches, such as machine learning, random forest, and neural networks. When combined with high-quality laboratory research, such approaches will be critical to the advances of toxicology research. To keep abreast of these advances, I encourage you to look inside ToxSci for the most influential research in the field of toxicology.”

The four Editor's Highlights in this issue were prepared by ToxSci Associate Editors and EIC Dr. Miller as follows:  PPARα Activation Modulates Bile Acid Excretion, Jeffrey M. Peters; Random Forests, Neural Networks, and Alkaloids, Jerry Campbell; PCB126 Exposure and Peripheral Vascular Disease, Lu Cai; and Endocrine Disruption at the Thyroid Receptor, Dr. Miller.

The mission of ToxSci, the official journal of the Society of Toxicology, is to publish the most influential research in the field of toxicology.




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