Project Info


Discover Hidden Data Patterns in Microbiome for Improved Human Health

Hua Wang | huawang@mines.edu

Although communities of microorganisms, originally referred to as microbiota, have been studied for a long time, the field has taken off in 2002 with the advent of metagenomics, which for the first time equipped us with a way to “see” the incredible diversity of species around us — too small to see with or own eyes. Now, the microbiome is not only of interest in environmental samples, but also within our own bodies. Particularly, the gut microbiome has skyrocketed as a source for potential disease biomarkers and treatment options, not only with drastically altered microbiomes in obese individuals, but also in patients with cancer, irritable bowel syndrome, diabetes, asthma and many others.

More Information

The data description and how microbiome data works in our bodies can be found at the below websites:
http://americangut.org/how-it-works/
http://humanfoodproject.com/americangut/

Several papers related to this project can be found at the below links:
https://www.nature.com/articles/nature05414.pdf
https://www.nature.com/articles/4441022a.pdf
https://ac.els-cdn.com/S0140673614604608/1-s2.0-S0140673614604608-main.pdf?_tid=54509d9d-f5b8-4086-8b2a-7f8b985056f3&acdnat=1521650265_e4baa69340e16893c54feb7735c06bd8

Grand Engineering Challenge: Advance health informatics

Student Preparation


Qualifications

Students are expected to take CSCI 261, 262 before taking this project. It would be good if the student have already taken CSCI 303, 358, 404, 470, but this is not required.

Time Commitment

40 hours/month

Skills/Techniques Gained

1. The students will learn the skills to perform data processing and management.
2. The students will be involved my research team to perform research on machine learning and data mining.
3. The students will be involved into scientific paper writing for the results from this project.
4. The students will have chance to work together with my collaborators in industry.
In a word, after the training in this project by successfully completing the assigned research tasks, the student is expected to be ready for pursing a graduate degree in the area of machine learning, data mining, or artificial intelligence, or a broader area of computer science.

Mentoring Plan

1. One orientation meeting is planned at the beginning of the project, in which the undergraduate students will be introduced to the research team. The project and research culture of the faculty’s research team will be introduced to the undergraduate students in the meeting.
2. Technical seminars within the research team are planned, once per week. In every meeting, the undergraduate students will present a research paper relevant to the project and lead discussions on it with the faculty and the graduate students in the research team.
3. Professional development sessions within the research team are planned, once per week. In every meeting, the faculty or the graduate students in the research team will examine the progress of the project and the recent research results, exchange the ideas with the undergraduate students, and help them
develop research skills, including algorithm development, experimental design, scientific results evaluation, paper writing, and so on.
4. A poster session will be conducted at the end of the project in which the results of this project will be presented to the research teams of the faculty, the Computer Science Department, and the collaborators of the faculty.