approach really enables the new field of location proteomics, which
describes and relates the location of proteins within cells," said
Murphy, a professor of biological sciences, machine learning, and
biomedical engineering. "This work should provide a more thorough
understanding of cellular processes that underlie disease."
Using this approach to spot a protein cluster could help scientists
identify a common protein structure that enables those proteins to
gather in one part of the cell, according to Murphy. Getting this
information is critical to foil a disease like cancer, where you might
want to identify and disable part of a tumor cell's machinery needed to
process proteins for cancer growth.
"Our tool represents a step forward because it is based on standardized
features and not on features chosen by the human eye, which is
unreliable. By automating the clustering of proteins inside cell
images, we also can study thousands of images fast, objectively and
without error," Murphy said.
Murphy's tool has two key components. One is a set of subcellular
location features (SLFs) that describe a protein's location in a cell
image. SLFs measure both simple and complex aspects of proteins, such
as shape, texture, edge qualities and contrast against background
features. Like fingerprints, a protein's SLFs act as a unique set of
identifiers. Using a set of established SLFs, Murphy then developed