How 23andMe is Bringing You Genetic Health, Education, and Research

23andMe logo, Source

One company that has been gaining a lot of attention recently is 23andMe.  23andMe (named after the 23 human DNA chromosomes and you) is a genetic testing company that can take a saliva sample that you mail them, perform numerous genetics tests, and give you the results to tell you things about you genetic health (whether you have a higher risk for certain diseases or whether you may pass on disease genes to your children), your ancestry, and your potential responses to drugs.  This is a cool idea for a company because it has important potential implications in education, research, and medicine.

Personalization of Medicine and the Science of Scale: A Summary of the 8th Annual ITMAT Symposium

Yesterday was the second and last day of the Institute for Translational Medicine & Therapeutics (ITMAT) 8th Annual International Symposium here at the University of Pennsylvania.  ITMAT is a group that promotes translational research and medical applications around some of the Philadelphia medical campuses.  What is especially cool about this symposium is that it is always an international symposium which features participants from many institutions from all around the world.  The theme of this year's symposium was "Harnessing the Paradox: Personalization and the Science of Scale" and it was all about using huge data sets and high-throughput techniques to improve patient care and our general understanding of medicine.  The three general themes of the meeting that I attended were the general biomedical research landscape, microbiomics, and metabolomics/metabolism (with a focus on cancer).  The fourth, which I missed, was about emerging technologies and concepts in translation.

Standard Error vs Standard Deviation, and Some Other Practical Statistics Stuff You Want to Know

Introduction

In most professional settings, and especially in the sciences, it is important to know a bit of statistics.  I say that this is particularly true for scientists because our jobs are centered around discovering and describing natural phenomena, and we rely on statistics to help us understand these.  Using inappropriate statistical methods, or interpreting statics incorrectly, can either result in missing interesting trends in data, or in making unjustified conclusions by mistake.  Because this is such an important topic, I want to highlight some major statistical points that all scientists (and professionals in general) should be aware of.  I will try to be brief here, but these topics can get pretty involved so I will also provide directions to more comprehensive literature for further reading in my Works Cited.  My goal here is only to hit some high points of commonly used statistics.