Leveraging latent patterns in the study of living systems

The development of high-throughput techniques to observe and perturb biological systems has led to remarkable progress in the last several decades. From the tremendous amounts of data being accumulated, new opportunities have emerged, including the possibility of finding latent patterns in high-dimensional variables that are reflective of underlying biological processes. While these methods have led to countless discoveries and innovations, it is clear there is much more we could learn by measuring and perturbing at far greater scales. Here, I advance methods to understand and utilize latent patterns in new types of high-dimensional data. I devise a method of analyzing networks of 'frequency interactions' in 16S/18S time series data, showing that these can be used to identify microbial communities and associated environmental factors.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Computational and Systems Biology Program

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Cleary, Brian(Brian Lowman)

Advisor dc:contributor.advisor

• Aviv Regev and Eric S. Lander.

• Computational and Systems Biology Program.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/122720

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/122720