Author
Sefer, Emre, Kingsford, C.
Publication Date
2021-05-01
Publication Place
-
Mary Ann Liebert, Inc.
Subject
Gene ontology, Metric labeling, Protein function prediction
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
1066-5277, 1066-5277
Record ID
5cb0f7ff-5992-4bfa-8d41-3b76567cb575
Library Location
Computer Science
Date
2021-05-01
Sample Text
Computational techniques have been successful at predicting protein function from relational data (functional or physical interactions). These techniques have been used to generate hypotheses and to direct experimental validation. With few exceptions, the task is modeled as multilabel classification problems where the labels (functions) are treated independently or semi-independently. However, databases such as the Gene Ontology provide information about the similarities between functions. We explore the use of the Metric Labeling combinatorial optimization problem to make use of heuristically computed distances between functions to make more accurate predictions of protein function in networks derived from both physical interactions and a combination of other data types. To do this, we give a new technique (based on convex optimization) for converting heuristic semimetric distances into a metric with minimum least-squared distortion (LSD). The Metric Labeling approach is shown to outperform five existing techniques for inferring function from networks. These results suggest that Metric Labeling is useful for protein function prediction, and that LSD minimization can help solve the problem of converting heuristic distances to a metric.
DOI
10.1089/cmb.2020.0425
Cilt
28
