Predicting Gene Functions from Text Using a Cross-Species Approach

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Predicting Gene Functions from Text Using a
Cross-Species Approach

• Emilia Stoica and Marti HearstSIMSUniversity of
  California, Berkeley

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Motivation

• Want to extract the functions of genes
  (functional annotation) from MEDLINE documents
• The marked accumulation of lipid droplets in
  LNCaP cells...is accompanied by an increase in
  phospholipid synthesis. The increase in PAP-2
  might be related to changes in lipid metabolism
  Since PAP-2 plays a pivotal role in the control
  of signal transduction by lipid mediator
  mediators, the ability of androgens to stimulate
  this enzyme in prostatic cells may provide
  opportunity for cross-talk between signaling
  pathways involving lipid mediators and androgens.

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Motivation

• Currently functional annotation is done by hand
  human curators read each document and annotate
  genes with functions based on evidence in text
• Goal automate functional annotation

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Gene Ontology (GO)

• Gene Ontology (GO) controlled vocabulary for
  functional annotation
• July 2005 17,600 terms (GO codes) organized in
  3 distinct acyclic graphs of molecular functions,
  biological processes and cellular locations
• More general terms are parents of less general
  terms development (GO0007275) is parent of
  embryonic development (GO0001756)

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Challenges

• GO tokens may not explicitly occur in text
  PubMed 10692450, negative regulation of cell
  proliferation (GO0008285), occurs as inhibition
  of cell proliferation
• GO tokens may not occur contiguous in text PubMed
  10734056, G-protein coupled receptor protein
  signaling pathway (GO0007186) Results
  indicate that CCR1-mediated responses are
  regulated in the signaling pathway, by receptor
  phosphorylation at the level of receptor
  G/protein couplingCCR1 binds MIP-1 alpha.

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Challenges

• Assigning GO codes to genes simply because the GO
  tokens occur in text yields a large number of
  false positives, because either
• the text does not contain evidence to support the
  annotation, or
• the text contains evidence for the annotation,
  but the curator knows the gene to be involved in
  a function that is more general or more specific
  than the GO code matched in text

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Challenges

• Evidence for annotation e.g., the text should
  mention co-purification, co-immunoprecipitation
  experiments
• Algorithms that take into account the evidence
  for annotation (e.g., annotate a gene with a GO
  code only if the text contains words like
  co-purification) do not perform any better than
  algorithms that ignore the evidence

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Related Work

• Mainly in the context of BioCreative competition
• Chiang and Yu find phrase patterns commonly used
  in sentences describing gene functions (e.g.,
  gene plays an important role in, gene is
  involved in) final assignments made with a
  Naïve Bayes classifier
• Ray and Cravenlearn a statistical model for each
  GO code (which words are likely to co-occur in
  the paragraphs containing GO codes) a
  multinomial Naïve Bayes classifier decides
  between candidates
• Rice et al. use SVM

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Related Work

• Couto et al. annotate a gene with a GO code if
  the information content of the GO code (computed
  as a function of words that match in text), is
  larger than the information content computed as a
  function of the GO tokens
• Verspoor et al. expand GO tokens with words that
  frequently co-occur in a training set use a
  categorizer that explores the structure of the
  Gene Ontology to find best hits
• Ehler and Ruch combine pattern matching and
  TFIDF weighting

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Main Idea

• To predict GO codes for target genes in target
  species, use the GO codes annotated to their
  orthologous genes (genes from a different species
  that have evolved directly from an ancestral
  gene)
• Assumption Since there is an overlap between the
  genomes of the two species, their orthologs may
  share some functions, and consequently some GO
  codes

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Main Idea
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General procedure

• Eliminate stop words, punctuation characters and
  divide the text into tokens using space as
  delimiter
• Analyze text at sentence level
• Normalize and match different variations of gene
  names using the algorithm of Bhalotia et al.
• For every sentence that has the target gene, we
  consider a GO code to be found if the sentence
  contains a percentage of GO tokens larger than a
  threshold (0.75 for CSM and 1 for CSC)

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CSM Algorithm

• CSM(g, a) For a target gene g we search in
  article a for only the GO codes annotated to its
  ortholog
• Eliminate annotations of orthologs marked with
  IEA and ISS codes to avoid circular reference

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CSC Algorithm

• General observation if two GO codes tend to
  occur together in a database, then a gene
  annotated with one GO code is likely to be
  annotated with the other one as well
• If one GO code tends to occur in the orthologous
  genes annotations when another one does not,
  then for the target species, these two GO codes
  may not be allowed to co-occur
• Example if text has rRNA transcription
  (GO0009303) nucleolus (GO0005737) and
  extracellular (GO0005576), then extracellular
  should be eliminated

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CSC Algorithm

• For every pair of GO codes in the orthologous
  genes database, compute a X2 coefficient
• N the total number of GO codes
• O11 of times the ortholog is annotated with
  both GO1 and GO2
• O12 of times the ortholog is annotated with
  GO1 but not GO2
• O21 of times the ortholog is annotated with
  GO2 but not GO1
• O12 of times the ortholog is not annotated
  with GO1 or GO2

X2
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CSC Algorithm

• M(g,a) GO codes matched in article a for gene g
• O(g) GO codes annotated to the ortholog of g
• o size of O(g), p percentage (0.2)
• CSC(g,a)
  for every GO1 in M(g,a)
  count
  0
  for every GO2 in O(g)
  if((X2(GO1,GO2)gt3.84) (GO1 ne GO2))
  count
• if(count gt po)
  add GO1 to CSC(g,a)

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Algorithm
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Results on BioCreative Dataset

• Task 2.2 Annotate 138 human genes with GO codes
  using 99 full text articles for each annotation,
  provide the passage of text the annotation was
  based upon
• Annotations from participants were manually
  judged by human curators
• A prediction was considered perfect if the
  passage of text contains the gene name and
  provides evidence for annotating the gene with
  the GO code

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Results on BioCreative Dataset

• Our research was conducted after the competition
  has past, so our annotations could not be judged
  by human curators
• We measure our performance using the perfect
  predictions other systems made (unfair to our
  system as we ignore relevant predictions we make
  that other systems do not find)
• Our prediction is correct if it matches a perfect
  prediction (e.g., vhl is annotated with
  transcription (GO0006350) in PubMed 12169961
  vhl inhibits transcription elongation, mRNA
  stability and PKC activity)

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BioCreative Results
System Precision TP (Recall) F-measure
CSM 0.364 16 (0.068) 0.114
CSC 0.182 44 (0.185) 0.178
CSMCSC 0.241 51 (0.215) 0.227
Ray and Craven 0.213 52 (0.219) 0.216
Chiang and Yu 0.327 37 (0.156) 0.211
Ehler and Ruch 0.123 78 (0.329) 0.179
Couto et al. 0.089 58 (0.245) 0.131
Verspoor et al. 0.055 19 (0.080) 0.065
Rice et al. 0.035 16 (0.068) 0.046
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Results on EBI Human and MGI datasets

• EBI human 4,410 genes and 5,714 abstracts
• MGI 2,188 genes and 1,947 abstracts

Dataset System Precision Recall F-measure
EBI CSM 0.289 0.033 0.060
CSMCSC 0.163 0.092 0.118
Chiang and Yu 0.318 0.063 0.105
MGI CSM 0.328 0.049 0.086
CSCCSC 0.168 0.121 0.140
Chiang and Yu 0.332 0.051 0.089
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Conclusions and Future Work

• We propose an algorithm that annotates genes with
  GO codes using the information available from
  other species
• Experimental results on three datasets show that
  our algorithm consistently achieves higher
  F-measure than other solutions
• Improvements to our algorithm
  - combine or use a voting
  scheme between the predictions our system makes
  and the predictions of a machine learning system
• - investigate how effective are other genes
  with sequences similar to the target gene (but
  not orthologous to the gene) for predicting the
  GO codes