1. Predicting Gene Functions from Text Using a Cross-Species Approach
Emilia Stoica and Marti Hearst School of Information University of California, Berkeley
Research Supported by NSF DBI and a gift from Genentech

2. Goal
Annotate genes with functional information derived from journal articles.

3. Gene Ontology (GO)
Gene Ontology (GO) controlled vocabulary for functional annotation
~ 17,600 terms (circa July 2004)
Organized into 3 distinct acyclic graphs
molecular functions
biological processes
cellular locations
More general terms are “parents” of less general terms:
development (GO: ) is the parent of embryonic development (GO: )

4. Challenges GO tokens might not appear explicitly
Example: PubMed
GO: : negative regulation of cell proliferation
Occurs as: inhibition of cell proliferation
GO tokens might not occur contiguously
Example: PubMed ,
GO: :
G-protein coupled receptor protein signaling pathway
Occurs as:
Results indicate that CCR1-mediated responses are regulated …in the signaling pathway, by receptor phosphorylation at the level of receptor G/protein coupling … CCR1 binds MIP-1 alpha.

5. Challenges
The simplest strategy (assigning GO codes to genes simply because the GO tokens occur near the gene) yields a large number of false positives.
Issues:
The text does not contain evidence to support the annotation,
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.

6. Challenges
GO contains hints about what kinds of evidence are required for annotation, e.g.:
The text should mention co-purification, co-immunoprecipitation experiments
Requiring these evidence terms does not seem to improve algorithms.

7. Related Work Mainly in the context of BioCreative competition (2004)
Chiang and Yu 2003, 2004:
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 Craven 2004, 2005:
Learn a statistical model for each GO code (which words are likely to co-occur in the paragraphs containing GO codes);
Decide among candidates via a multinomial Naïve Bayes classifier
Rice et al. 2004:
Train an SVM for each GO code.
Target genes assigned best-scoring GO code.

8. Related Work, cont. Couto et al. 2004 Verspoor et al. 2004
Determine if the “information content” of the matching GO terms is larger than for all the candidate GO terms.
Verspoor et al. 2004
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 2004:
Treat each document as a query to be categorized
Create a score based on a combination of pattern matching and TF*IDF weighting
Annotate gene with top-scoring GO codes.

9. Our Approach Two main contributions:
Use cross-species information (CSM)
Check for biological (in) consistencies (CSC)

10. Cross-Species Match Main Idea
Use orthologous genes
[Genes of different species that have evolved directly from a common ancestor.]
Assumption:
Since there is an overlap between the genomes of the two species, their orthologs may share some functions, and consequently some GO codes
Idea: to predict GO codes for target genes in target species, use the GO codes assigned to their orthologous genes
We use Mouse vs. Human genes

11. General procedure Analyze text at sentence level
Eliminate stop words, punctuation characters and divide the text into tokens using space as delimiter
Normalize and match different variations of gene names using the algorithm of Bhalotia et al.’03
For every sentence that contains the target gene:
A GO code is matched if the sentence contains a percentage of GO tokens larger than a threshold (0.75 for CSM and 1 for CSC)

12. Cross Species Match Algorithm
CSM(g, a): For a target gene g, search in article a for only the GO codes annotated to its ortholog
If at least 75% of the GO code terms are found in a sentence containing the gene name, the code is matched.
Note: we must eliminate annotations of orthologs marked with IEA and ISS codes to avoid circular references.

13. Cross-Species Correlation Main Idea
Observation:
Since GO codes indicate gene function, it is logical for some to often co-occur in annotations and for others to rarely do so.
Assumption:
If one GO code tends to occur in the orthologous genes’ annotations when another one does not, then assume the second is not a valid assignment for the target species
Example:
If text seems to contain evidence for rRNA transcription (GO: ) nucleolus (GO: ) and extracellular (GO: ), then extracellular is suspicious.
The algorithm identifies the “suspicious” cases.

14. Cross-Species Correlation 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

15. Cross-Species Correlation Algorithm
M(g,a) = GO codes matched in article a for gene g
O(g) = GO codes assigned to the ortholog of g
o = size of O(g), p = percentage (0.2)
For every potentially matching GO code GO1 in M(g,a)
For every GO code GO2 in O(g)
Count how often X2(GO1,GO2) is significant
If this count is < p*o then assume GO1 is not valid.
Else assign GO1 to g

16. Information Flow

17. Evaluation using BioCreative
Task 2.2:
Annotate 138 human genes with GO codes using 99 full text articles;
For each annotation, provide the passage of text that the annotation was based upon.
Annotations from participants were manually judged by human curators
A prediction was considered “perfect” if the text passage
contained the gene name, and
provided evidence for annotating the gene with the GO code

18. Results on BioCreative
Our research was conducted after the competition had past, so our annotations could not be judged by the same curators
Used the “perfect predictions”
(unfair to our system; ignores relevant predictions we find that other systems do not)
Our prediction is correct if it matches a perfect prediction (e.g., vhl is annotated with transcription (GO: ) in PubMed “vhl inhibits transcription elongation, mRNA stability and PKC activity”)

19. BioCreative Results CSM+CSC 0.24 51 (0.21) 0.23 System Precision
TP (Recall)
F-measure
CSM
0.36
16 (0.07)
0.11
CSC
0.18
44 (0.19)
CSM+CSC
0.24
51 (0.21)
0.23
Ray and Craven
0.21
52 (0.22)
0.22
Chiang and Yu
0.33
37 (0.16)
Ehler and Ruch
0.12
78 (0.33)
Couto et al.
0.09
58 (0.25)
0.13
Verspoor et al.
0.06
19 (0.08)
0.07
Rice et al.
0.04
0.05

20. Results on Larger Dataset
A much larger test set has been made publicly available by Chiang and Yu.
EBI human test set
4,410 genes
13,626 GO code annotations
MGI mouse test set
2,188 genes
6,338 GO code annotations
Note that Chiang and Yu used the same data for both training and testing.

21. 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
0.29
0.03
0.06
CSM+CSC
0.16
0.09
0.12
Chiang and Yu
0.32
0.11
MGI
0.33
0.05
CSC+CSC
0.17
0.14

22. 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-measures than other solutions
Future 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

23. Thank you! http://biotext.berkeley.edu
Research Supported by NSF DBI and a gift from Genentech