Linked Life Data

19520185

Source:http://linkedlifedata.com/resource/pubmed/id/19520185

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
15-16
pubmed:dateCreated
2009-8-3
pubmed:abstractText
During the past decade, computational technologies have become well integrated in the modern drug design process and have gained in influence. They have dramatically revolutionized the way in which we approach drug discovery, leading to the explosive growth in the amount of chemical and biological data that are typically multidimensional in structure. As a result, the irresistible rush towards using computational approaches has focused on dimensionality reduction and the convenient representation of high-dimensional data sets. This has, in turn, led to the development of advanced machine-learning algorithms. In this review we describe a variety of conceptually different mapping techniques that have attracted the attention of researchers because they allow analysis of complex multidimensional data in an intuitively comprehensible visual manner.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
1878-5832
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
14
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
767-75
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Computational mapping tools for drug discovery.
pubmed:affiliation
ChemDiv Inc., 6605 Nancy Ridge Drive, San Diego, CA 92121, USA.
pubmed:publicationType
Journal Article, Review