A text-mining analysis of the human phenome

1
A text-mining analysis of the human phenome
European Journal of Human Genetics (2006) 14,
535-542

• Marc A van Driel1, Jorn Bruggeman2, Gert Vriend1,
  Han G Brunner,3 and Jack AM Leunissen2

1Centre for Molecular and Biomolecular
Informatics, Radboud University Nijmegenthe
Netherlands 2Department of Bioinformatics,
Wageningen University and Research Centre
3Department of Human Genetics, University Medical
Centre Nijmegen
Speaker Yu-Ching Fang Advisors Hsueh-Fen Juan
and Hsin-His Chen
2
Outline

• Introduction
• Methods
• Results
• Discussion

3
Introduction

• Functional annotation of genes is an important
  challenge once the sequence of a genome has been
  completed.
• Previous studies have correlated various
  attributes of human genes with the chance of
  causing a disease.

4
Introduction (cont.)

• But, few attempts have been made to
  systematically classify relationships between
  genes and proteins at the phenotype level.

5
Introduction (cont.)

• The Online Mendelian Inheritance in Man (OMIM)
  database contains human disease phenotype data
  and record-based textual information, one gene or
  one genetic disorder per record.
• Goal Systematic grouping of genes by their
  associated phenotypes from the OMIM database.

6
Methods The OMIM database

• Full text (TX) field 5132 (disease)/16357

7
Methods The OMIM database (cont.)

• Clinical synopsis (CS) field

8
Creation of feature vectors

• MeSH terms and their components are concepts.
• MeSH concepts serve as phenotype features
  characterizing OMIM records.
• Ex OMIM_1-gtMeSH_1,MeSH_2,

9
Refinement of the feature vectors

• MeSH concepts can be very broad like Eye or
  more specific like Retina.
• A concepts hierarchy that describes relationships
  such as Eye-Retina-Photoreceptors.
• Retina is a hyponym of Eye.

10
Refinement of the feature vectors (cont.)

• To ensure that the concepts eye and retina are
  recognized as similar, the MeSH hierarchy was
  used to encode this similarity in the feature
  vectors by increasing the value of all hypernyms.

rc relevance of concept c rc,counted count of
the concept c in a document rhypos relevance of
the concept cs hyponym nhypo,c the number of
the concept cs hyponyms
11
Refinement of the feature vectors (cont.)

• Example of concept expansion using the MeSH
  hierarchical structure.

12
Refinement of the feature vectors (cont.)

• Not all concepts in the OMIM records are equally
  informative.
• Ex retina pigment epithelium occurs rarely,
  and thus provides more specific information than
  very frequently terms such as Brain.
• Inverse document frequency measure

gwc inverse document frequency or global weight
of concept c N 5080 nc the number of records
that contain concept c
13
Refinement of the feature vectors (cont.)

• Not all OMIM records contain equally extensive
  descriptions (record size differences).
• These differences will make a comparison between
  records difficult because the diversity and the
  frequency of concepts in the larger records will
  exceed those in the smaller records.

rc relevance of concept c rmf the frequency of
the most occurring MeSH concept in that record
14
Comparing OMIM records

• The similarity between OMIM records can be
  quantified by comparing the feature vectors that
  are expanded and corrected.
• Similarities between feature vectors were
  determined by the cosines of their angles.

s(X,Y) the similarity between the feature
vectors X and Y xi, yi concept frequencies
15
Results Comparing OMIM records

• 5080/5132 OMIM records could match one or more
  MeSH terms.
• The 5080x5080 pair-wise feature vector
  similarities form phenomap (All to all
  similarities).

Most phenotype-phenotype pairs have a low
similarity score.
16
Comparing OMIM records - The best scores for all
phenotypes in the disease phenotype data set

• For each OMIM record, the most similar of the
  other 5079 records was identified.
• Moderately similar phenotype pairs might still
  yield reasonable hypotheses.

Ex Fibromuscular Dysplasia of Arteries and
Cardiomyopathy, Familial Hypertrophic have 0.31
similarity score
17
Comparing OMIM records (cont.)

• Conclusion The more phenotypes resemble each
  other, the more likely they are to share an
  interaction.

18
Discussion

• Developed a text-mining approach to map
  relationships between more than 5000 human
  genetic disease phenotypes from the OMIM
  database.
• Phenotype clustering reflects the modular nature
  of human disease genetics. Thus, the phenomap may
  be used to predict candidate genes for diseases.