Finding Functional Gene Relationships Using the Semantic Gene Organizer (SGO) Kevin Heinrich...
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Transcript of Finding Functional Gene Relationships Using the Semantic Gene Organizer (SGO) Kevin Heinrich...
Finding Functional Gene Relationships Using the Semantic
Gene Organizer (SGO)
Kevin Heinrich
Master’s Defense
July 16, 2004
Outline
• Problem / Goals
• Related Work
• Information Retrieval– Vector Space Model– Latent Semantic Indexing (LSI)
• Biological Databases
• SGO Use & Results
Problem
• Biological tools are creating vast amounts of data.
• Current techniques are time-consuming and expensive.
• Want to know phenotype (function) from genotype (structure/sequence).
Goals
• Develop a tool to aid researchers in finding and understanding functional gene relationships.
• Use information that covers whole genome, e.g. literature.
Related Work
• Jenssen et al. (2001) developed PubGene.– Literature network– Assigns functional association if there is a co-
occurrence of gene symbols
• Wilkinson and Huberman (2004) expanded this idea to find communities of related genes.
• Yandell and Majoros (2002) use natural language processing techniques to identify nature of relationships.
Related Work
• Most all literature-based techniques rely on term co-occurrence.
• What about gene aliases?
• Solution: Apply a more robust technique.
Information RetrievalVector Space Model
• Documents are parsed into tokens.
• Tokens are assigned a weight of, wij, of ith token in jth document.
• An m x n term-by-document matrix, A, is created where
– Documents are m-dimensional vectors.– Tokens are n-dimensional vectors.
ijwA
Information RetrievalTerm Weights
• Term weights are the product of a local and global component
• tf
• idf
• idf2
jiijij dglw
ijij fl
jij
jij
i f
f
g
1log2 j
iji f
ng
Information RetrievalTerm Weights (cont’d)
• log-entropy
• Goal is to give distinguishing terms more weight.
n
pp
g jijij
i2
2
log
log
1
ijij fl 1log
jij
ijij f
fp
Information RetrievalQuery & Similarity
• Queries are represented by a pseudo-document vector
• Similarity is the cosine of the angle between document vectors.
mgggq ,,, 210
m
kk
m
kkj
m
kkjk
j
jjj
gw
wg
dq
dqdqsim
1
2
1
2
1cos,
Information RetrievalLatent Semantic Indexing (LSI)
LSI performs a truncated SVD on
A = UΣVT
• U is the m x n matrix of eigenvectors of AAT
• VT is the r x n matrix of eigenvectors of ATA• Σ is the r x r diagonal matrix containing the r nonnegative
singular values of A• r is the rank of A
A rank-k approximation is given by Ak = UkΣkVkT
Information RetrievalLSI (cont’d)
• Document-to-document similarity is
• Queries are projected into low-rank approximation space
TkkkkTk VVAA
10
kkTUqq
Information RetrievalLSI (cont’d)
• Scaled document vectors can be computed once and stored for quick retrieval.
• The lower-dimensional space forces queries and documents to be compared in a more conceptual manner and saves storage.
• Choice of number of factors is an open question.
• End Effect: LSI can find similarities between documents that have no term co-occurrence.
Information RetrievalEvaluation Measures
• Precision – ratio of relevant returned documents to the total number of returned documents.
• Recall – ratio of relevant returned documents to the total number of relevant documents.
• Goal is to have high precision at all levels of recall.
• Systems are often evaluated by average precision (AP), which is the average of 11 interpolated precision values at the decile ranges.
Biological DatabasesMEDLINE
• MEDLINE (NLM)– Contains 14+ million references to journal
articles with a concentration in medicine– Span over 4,600 journals worldwide– 1966 to present– ~500,000 citations added annually– Each citation is manually indexed with MeSH
terms.
Biological DatabasesPubMed
• PubMed– Retrieves articles from MEDLINE and other
journals.– Can be queried via any combination of
attributes.
Biological DatabasesLocusLink
• NCBI human-curated database• Single query interface to a comprehensive
directory for genes and gene reference sequences for key genomes.
• Provides links to related records in PubMed and other citations when applicable.
• Provides RefSeq Summary of gene function and links to key MEDLINE citations relevant to each gene.
Biological DatabasesOverview
• MEDLINE has lots information– Not all articles relate to genes– Gene terminology problem
• LocusLink does not cover all relevant citations, but a representative few.
Biological DatabasesGene Document Construction
• Concatenate titles and abstracts of MEDLINE citations cross-referenced in Human, Rat, and Mouse LocusLink entries.
• Sequencing abstracts included – noise
• LocusLink references are not comprehensive, so recall of all relevant abstracts is not guaranteed.
SGO
• Primarily uses LSI to rank genes.
• Enables user to specify query method– Gene query– Keyword query– Number of factors– Show latent matches
• Saves previous query sessions.
SGOInterface
SGOInterface (cont’d)
SGOTrees
• Unfortunately, ranked lists mean little to biologists.
• Pairwise distances can be formed into a matrix
where is the similarity between documents i and j
ijdD
ijijd cos1
ijcos
SGOTrees (cont’d)
• Fitch-Margoliash (1967) method in PHYLIP is applied to D to generate hierarchical trees.
• Thresholds can be applied to self-similarity matrix to produce graphs.
SGOHierarchical Tree
SGOGraph or Nodal Tree
SGOCoding Issues
• Web interface – must be interactive– Queries are processed on click– Document collections are parsed offline– Trees are constructed offline
• Storage will eventually become an issue.
ResultsTest Data Set
• 50 gene test data set was constructed.– Alzheimer’s Disease– Cancer– Development
• Reelin signaling pathway used as basis for evaluation– 5 primary genes (directly
associated)– 7 secondary genes (indirectly
associated)
ResultsPrimary AP
• AP for 5 primary genes– 61% for 5 factors– 84% for 25 factors– 84% for 50 factors
ResultsSecondary AP
• AP for 12 secondary genes– 53% for 5 factors– 59% for 25 factors– 61% for 50 factors
ResultsComparison
• LSI comparable to tf-idf for 5 primary genes• Far superior to tf-idf for 12 second genes
– PubMed co-citation identifies 2 of the 7 indirectly related genes
– Abstract overlap of LocusLink citations fails to identify any indirectly related genes
• tf-idf fails on many keyword queries
• Tested on Gene Ontology classifications (not shown)– Similar tendencies are observed
ResultsAbstract Representation
• To simulate scaling up, decrease representation of reelin-related genes
• AP of 47% on 20,856 Human LocusLink abstracts
ResultsHierarchical Tree
ResultsHierarchical Tree
ResultsHierarchical Tree
Conclusions
• SGO allows genes to be compared to each other and to keyword (function).
• SGO identifies latent relationships with promising accuracy.
• SGO is not meant to replace existing technologies, but to assist researchers– Verify current results– Direct future exploration
Future Work
• Scale up to entire genome
• Document construction
• Incorporate structural or other information for multi-modal similarity
• Test other models e.g. NMF, QR, etc.
• Interactive tree building
• Keep collections current