Linked Life Data

20180268

Source:http://linkedlifedata.com/resource/pubmed/id/20180268

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2010-2-24
pubmed:abstractText
We have recently proposed novel kernel functions, called base-pairing profile local alignment (BPLA) kernels for discrimination and detection of functional RNA sequences using SVMs. We employ STRAL's scoring function which takes into account sequence similarities as well as upstream and downstream base-pairing probabilities, which enables us to model secondary structures of RNA sequences. In this paper, we develop a method for optimizing hyperparameters of BPLA kernels with respect to discrimination accuracy using a gradient-based optimization technique. Our experiments show that the proposed method can find a nearly optimal set of parameters much faster than the grid search on all parameter combinations.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
0919-9454
pubmed:author
pubmed:issnType
Print
pubmed:volume
23
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
128-38
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Gradient-based optimization of hyperparameters for base-pairing profile local alignment kernels.
pubmed:affiliation
Japan Biological Informatics Consortium (JBIC), 2-45 Aomi, Koto-ku, Tokyo 135-8073, Japan. sato-kengo@aist.go.jp
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't