A 12-year-old Tennessee girl has created an algorithm to solve a problem that other scientists have been working on for years. She is one of 10 finalists in the 2016 Discovery Education 3M Young Scientist Challenge.

The contest aims to recognise students in grades 5 to 8 (ages 12 to 15) who use scientific thinking to create a video about an idea they have to improve lives on a community or global scale.

An algorithm that allows doctors to determine mutations in DNA

Sofia Tomov, a home-schooled student who has taken multiple computer science classes online, created a project that would code an algorithm (the Apostolico-Giancarlo algorithm, which she put on multiple processors) to help determine if patients have any mutations in their genomes.

Scientists could first sequence the patient's genome and then use Tomov's programme to scan for any mutations, which could then be checked against databases that match the mutations to specific side effects.

This aids the medical industry in the creation of more customised medications to minimise potential side effects of prescription drugs. This has significant implications in healthcare, as there has been research pinpointing that adverse reactions to prescription drugs are tied with having a stroke as the fourth-leading cause of death in the U.S.

It is a long-standing problem without a valid solution, until now. Although scientists have established that there are genetic mutations that are associated with negative reactions for particular drugs, “…knowing the mutation only solves half the problem,” Tomov explains in a video outlining her project.

“Doctors need to know if their patients have particular mutations. They need to find one mutation in a genome of billions bases,” Tomov says. "So far they have not found a feasible solution because the algorithms are taking too long to run on a 6 billion base-long genome … for patients in emergency situations such as a heart attack or a seizure, this is a huge health risk," she adds.

Excited to meet her mentor in person

Tomov and the other finalists were each paired with a 3M scientist for a one-on-one mentorship to turn their concepts into a prototype. They each will also receive $1,000.

They will each share their projects in St. Paul, Minnesota, and compete for a grand prize of $25,000, a trip to watch a taping of a Discovery Network show and the title of "America's Top Young Scientist" at the final contest.

"I'm really excited to meet the other finalists and to be able to meet my mentor in person," Tomov says of her assigned mentor, John Henderson, who helps her with her project via Skype.

My father helped me review my code and he helped explain some of the concepts like parallel computing and algorithms. And my mom always finds more resources, some excellent resources about my project," Tomov says. Her mother is a teacher and her father works in the computer science field.

Tomov has stated that the hardest part of the project was in building out the code for the algorithm. She is committed to the optimisation of the algorithm, to make it even faster at determining whether a person has the genetic mutation.

Ambitious teen wants to start machine learning company

Thinking way beyond the competition, Tomov says, "I envision this as being extremely widespread, that it could be implemented in hospitals and a genome sequence could become a routine part of someone's medical data.”

She already has an extensive resume – she has published a children's book, filed a provisional patent, which is still being processed, for a drug disposal device that would prevent drugs from contaminating the water supply, excelled in the ACT, passed two AP courses and is enrolled in three more AP courses this year at the University of Tennessee. She is not a full-time student, but she has been accepted as a part-time visiting student.

With her sizable academic head start, Tomov expressed her ambition to get a Ph.D. in computer science and to start her own company in machine learning.

"[Machine learning] is a type of computer science that analyses large data sets to help make predictions, so it has applications in energy and medicine," Tomov says. MIMS

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