Academic Commons

Articles

Protein Ranking by Semi-Supervised Network Propagation

Weston, Jason; Kuang, Rui; Leslie, Christina; Noble, William

Background: Biologists regularly search DNA or protein databases for sequences that share an evolutionary or functional relationship with a given query sequence. Traditional search methods, such as BLAST and PSI-BLAST, focus on detecting statistically significant pairwise sequence alignments and often miss more subtle sequence similarity. Recent work in the machine learning community has shown that exploiting the global structure of the network defined by these pairwise similarities can help detect more remote relationships than a purely local measure.

Methods: We review RankProp, a ranking algorithm that exploits the global network structure of similarity relationships among proteins in a database by performing a diffusion operation on a protein similarity network with weighted edges. The original RankProp algorithm is unsupervised. Here, we describe a semi-supervised version of the algorithm that uses labeled examples. Three possible ways of incorporating label information are considered: (i) as a validation set for model selection, (ii) to learn a new network, by choosing which transfer function to use for a given query, and (iii) to estimate edge weights, which measure the probability of inferring structural similarity.

Results: Benchmarked on a human-curated database of protein structures, the original RankProp algorithm provides significant improvement over local network search algorithms such as PSI-BLAST. Furthermore, we show here that labeled data can be used to learn a network without any need for estimating parameters of the transfer function, and that diffusion on this learned network produces better results than the original RankProp algorithm with a fixed network.

Conclusion: In order to gain maximal information from a network, labeled and unlabeled data should be used to extract both local and global structure.

Files

  • thumnail for 1471-2105-7-S1-S10.xml 1471-2105-7-S1-S10.xml binary/octet-stream 55.1 KB Download File
  • thumnail for 1471-2105-7-S1-S10.pdf 1471-2105-7-S1-S10.pdf binary/octet-stream 412 KB Download File

Also Published In

Title
BMC Bioinformatics
DOI
https://doi.org/10.1186/1471-2105-7-S1-S10

More About This Work

Academic Units
Center for Computational Learning Systems
Computer Science
Published Here
September 9, 2014
Academic Commons provides global access to research and scholarship produced at Columbia University, Barnard College, Teachers College, Union Theological Seminary and Jewish Theological Seminary. Academic Commons is managed by the Columbia University Libraries.