Latent Semantic Indexing and Probabilistic (Bayesian) Information Retrieval.
Gene Clustering by Latent Semantic Indexing of MEDLINE Abstracts
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Transcript of Gene Clustering by Latent Semantic Indexing of MEDLINE Abstracts
Gene Clustering by Latent Semantic Indexing of MEDLINE Abstracts
Ramin Homayouni, Kevin Heinrich, Lai Wei, and Michael W. Berry
University of Tennessee
presented by J. Jiang
Outline
Brief Overview of Biomedical Literature Mining
The Gene Clustering Problem Latent Semantic Indexing Experiments Conclusions and Discussions
Biomedical Literature MiningBrief Overview
Goal: to find useful information from the large amount of biomedical literature
Tasks include: Identifying relevant literature for a given gene/protein Connecting genes with diseases Grouping genes/proteins by functions Reconstructing and predicting gene networks
(ISMB 05’ Tutorial Proposal, H. Shatkay)
Biomedical Literature MiningBrief Overview (cont.)
Approaches: IE & NLP: entities, relations, facts, etc. Many methods
rely on co-occurrences of genes/proteins. IR: text categorization and summarization, etc. Hybrid: combining multiple techniques
Challenges include: No fixed nomenclature or sentence structure Indirect links Etc.
The Gene Clustering Problem
To group genes based on their functions Previous work:
Co-occurrence of gene symbols to extract gene relationships
Implicit textual relationships Gene clustering using functional information in
annotated indices or MEDLINE abstracts
Vector Space Modelfor Gene Clustering
Glenisson et al., 2003 Bag-of-words, vector space model Cosine similarity K-medoids algorithm
This paper tries to improve the vector representation of documents using LSA.
Background: LSA
First studied by Deerwester et al., Indexing by Latent Semantic Analysis, J Info Sci, 1990
Motivation: inaccuracy of term matching due to polysemy and synonomy
Assumption: existence of latent semantic structure (“artificial concepts”)
Dimension reduction. Keep the most important dimensions. Similar to PCA.
Singular Value Decomposition
d documents, t terms (in general, t >> d) d t matrix X = [xij], where xij denotes the frequency
of term j in document i X can be decomposed as:
X = T0S0D0,where columns of T0 are the eigenvectors of XX, and columns of D0 are the eigenvectors of X X. S0 is diagonal. S0
2 is the matrix of eigenvalues of XX (or X X).
SVD (cont.)
The diagonal elements of S0 are constructed to be positive and ordered in decreasing magnitude.
SVD (cont.)
The eigenvector with the largest eigenvalue represents the dimension along which the variance of the data is maximized.
Keep the k largest elements in S0, remove other elements, and remove corresponding columns (eigenvectors) in T0 and D0, X can be approximated by:
X Xhat = TSD.
SVD (cont.)
Xhat is the best least-square-fit to X with rank k.
Illustration
The first eigenvector
The second eigenvector
(taken from “A Tutorial on PCA” by Lindsay Smith)
LSA with SVD
Terms are represented by rows of Xhat and documents are represented by columns of Xhat in the reduced space.
Doc-to-doc similarity:
Xhat Xhat = DS2D = DS(DS) . Query is represented as pseudo-document:
Dq = Xq TS-1,
where Xq is the query vector in the original space. Dq is like a row of D.
Query-to-doc similarity:
DqS (DS) .
Experiments
50 genes in (1) development, (2) Alzheimer Disease, and (3) Cancer Biology are selected
Gene-document: concatenation of abstracts known to be related the gene
Gene-document represented as vectors:
Experiments (cont.)
Keyword query and accession number query Reelin signaling pathway GO classification terms and human disease Direct genes and indirect genes Hierarchical Clustering
Results
Results (cont.)
Results (cont.)
Tried 5, 25, and 50 dimensions. 50 is shown to perform the best.
Tried reducing the numbers of abstracts of Reelin genes. Claimed that AP was not significantly reduced when 50% abstracts were removed.
Claimed that hierarchical clustering agrees with biological relationships.
Discussions
Pros Gene clustering by textual information. Applied LSA to biomedical literature. Indirect linkage
can be found through latent concepts. Cons
Requires human annotation to construct gene-documents. Not applicable to new domain.
Genes in the experiments are carefully chosen in 3 categories. How does the method perform in general?
Other gene clustering methods?
References
S. Deerwester et al. (1990). Indexing by Latent Semantic Analysis. Journal of the American Society for Information Science, 41-6, 391-407.
M.A. Gerolami (2004). Latent Semantic Analysis A General Tutorial Introduction. http://ir.dcs.gla.ac.uk/oldseminars/Girolami.ppt
H. Shatkay (2005). ISMB 05’ Tutorial Proposal. http://www.iscb.org/ismb2005/tutorials/pm10.pdf
H. Shatkay & R. Feldman (2004). Mining the Biomedical Literature in the Genomic Era: An Overview. Journal of Computational Biology, 10-6, 821-855.
The End
Questions? Thank you!