Gene clustering by latent semantic indexing of medline abstracts
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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

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Gene Clustering by Latent Semantic Indexing of MEDLINE Abstracts

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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

Outline

  • Brief Overview of Biomedical Literature Mining

  • The Gene Clustering Problem

  • Latent Semantic Indexing

  • Experiments

  • Conclusions and Discussions


Biomedical literature mining brief overview

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 mining brief overview cont

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

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 model for gene clustering

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

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

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. S02 is the matrix of eigenvalues of XX (or X X).


Svd cont

SVD (cont.)

  • The diagonal elements of S0 are constructed to be positive and ordered in decreasing magnitude.


Svd cont1

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 cont2

SVD (cont.)

  • Xhat is the best least-square-fit to X with rank k.


Illustration

Illustration

The first eigenvector

The second eigenvector

(taken from “A Tutorial on PCA” by Lindsay Smith)


Lsa with svd

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

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

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


Results cont

Results (cont.)


Results cont1

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

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

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

The End

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