1. BioQA - A question answering system for the biomedical domain
Luis Tari

2. Question Answering (QA)
What is QA?
“QA is an interactive human computer process that encompasses understanding a user information need, typically expressed in a natural language query; retrieving relevant documents, data, or knowledge from selected sources; extracting, qualifying and prioritizing available answers from these sources; and presenting and explaining responses in an effective manner.”
Cited from “New Directions in Question Answering”
Why QA?
One of the ultimate goals in AI (human-level AI, Turing’s test, …)
A move beyond keyword query, finding what we really want to know

3. QA How is QA different from a search engine? QA Search Engine
Check out QA
Search Engine
Queries in Natural Language (Questions)
Queries based on keywords
Present answers to users
Users find the answers from retrieved results
Some natural language process is used to determine answers
Mostly keywords and ranking to retrieve results

4. Text Retrieval Conference (TREC)
An annual activity of information retrieval (IR) research sponsored by the National Institute for Standards and Technology (NIST).
TREC is organized into “tracks” of common interest.
Research groups work on a common source of data and a common set of queries or tasks.
The goal is to allow comparisons across systems and approaches in a research-oriented, collegial manner.

5. TREC Genomics Track
TREC Genomics Track focuses on the retrieval of information from biomedical literature.
Ad-hoc retrieval on a set of 4.5 millions of articles, in which 25% of them have no abstracts.
50 topics (queries) organized in 5 templates

6. TREC Genomics Templates
Find articles describing standard methods or protocols for doing some sort of experiment or procedure.
Find articles describing the role of a gene involved in a given disease.
Find articles describing the role of a gene in a specific biological process.
Find articles describing interactions (e.g., promote, suppress, inhibit, etc.) between two or more genes in the function of an organ or in a disease.
Find articles describing one or more mutations of a given gene and its biological impact.

7. BioQA A QA system for the biomedical domain
A great deal of genomics information resources are available
Entrez Gene, PubMed, UniProt, Gene Ontology, UMLS, many many more…
BioQA utilizes some of the genomics resources, whereas a generic QA does not
Keyword search is not enough
Consider the following examples

8. Example 1
Suppose as a biologist, I want to know the role of the gene interferon beta in the disease multiple sclerosis.
Query to PubMed:
“interferon beta” AND “multiple sclerosis”
Oops… interferon beta IS also the name of a treatment. I’m not a medical doctor so I don’t really care….
bad example…

9. Example 2 Query: “interferon beta” AND “multiple sclerosis”
good example
Hmm… this is more like what I am looking for….

10. Objectives of BioQA Phase 1 Phase 2 Phase 3
Retrieve relevant articles with respect to the specific needs of user’s questions
Phase 2
Extract and present answers to the users
Phase 3
Answer questions that require simple reasoning

11. BioQA Prototype Offline Subsystem

12. BioQA Prototype Online Subsystem

13. Main Components of BioQA Phase 1
Question Processing and Query Formation
Entity Recognition
Indexing
Pronoun Resolution
Extraction
Ranking

14. Question Processing and Query Formation
Process questions so that keywords are extracted to form queries for retrieval
Incorporate synonyms for the keywords
Consider the question:
“What is the role of PRNP in mad cow disease?”
First idea
Get all the nouns from the question
But we do not want a query that includes “role”
Second idea
Identify all the entities from the question and treat them as keywords
But what if we are unable to identify some of the entities?

15. Question Processing and Query Formation
Third idea – making use of dependency grammar (Link Grammar)
keyword(N2) :- noun(N1), noun(N2), Mp(N1,X), J(X,N2).
In the following example, N1= “role” and N2= “X” in the question Xp
| MVp |
| Ost | |
+---Ws--+Ss*w Ds-+-Mp-+J+ +J+ |
| | | | | | | | | |
LEFT-WALL what is.v the role.n of X in Y ?

16. Entity Recognition To recognize gene symbols, disease names
Lots of resources on
gene symbols: Entrez Gene, HUGO, …
disease names: MeSH, UMLS, …
Why is Entity Recognition still an issue?
“CDC28” can be written as “Cdc28”, “Cdc28p”, “cdc-28”
“hairy” is a gene name
“GSS” is a synonym of “PRNP”, but “GSS” itself is also a gene which is unrelated to “PRNP”!
Two tasks
Recognize gene names given a biomedical article
Generate gene symbol synonyms and variants given a gene symbol in a query
Notice that “Cdc28”, “Cdc28p”, “cdc-28” are not synonyms of “CDC28”, so they are not listed in dictionaries such as Entrez Gene

17. Entity Recognition Various approaches:
Machine learning techniques to recognize names on the basis of their characteristic features
Dictionary-based methods with generation of variants
Dictionary-based + Part-of-Speech methods
Rule-based methods
Some of the best Entity Taggers:
ABNER
GAPSCORE

18. Anaphora Resolution Pronominal Anaphors Sortal Anaphors
Resolving third-person pronouns and reflexive pronouns
Example: “BRCA1 interacts with Smad2. It also interacts with Smad3.”
Sortal Anaphors
“In this report, we show that virus infection of cells results in a dramatic hyperacetylation of histones H3 and H4 that is localized to the IFN-beta promoter. … Thus, coactivator-mediated localized hyperacetylation of histones may play a crucial role in inducible gene expression. [PMID: ]
Which histones?

19. Anaphora Resolution
“Ethanol was found to inhibit the function of this chimeric receptor in a manner similar to that of nACh alpha 7 receptors. Because the inhibition transfers with the amino-terminal domain of the receptor, the observations suggest that the amino-terminal domain of the receptor is involved in the inhibition.” [PMID: ]

20. Extraction To extract knowledge from text
Knowledge such as protein-protein interactions, gene-disease relations, …
Can be used in presenting answers
Extracting protein-protein interactions
“Mitotic cyclin (Clb2)-bound Cdc28 (Cdk1 homolog) directly phosphorylated Swe1 and this modification served as a priming step to promote subsequent Cdc5-dependent Swe1 hyperphosphorylation and degradation.” [PMID: ]
Should extract the following interactions from the above text:
Cdc28 binds Clb2
Swe1 is phosphorylated by Clb2-Cdc28 complex
Cdc5 is involved in Swe1 phosphorylation.

21. Extraction Extraction of other relations
“… Furthermore, PACT colocalized with viral replication complex in the infected cells. Thus the observed effect of PACT is novel and PACT is involved in the regulation of viral replication …” [PMID: ]
Should extract the following relations from the above text:
PACT colocalized with viral replication complex in the infected cells
PACT is involved in the regulation of viral replication

22. Extraction Two main directions towards extraction: However,
Cooccurrence
Identify entities that co-occur within abstracts
Frequency-based scoring scheme to rank the extracted relationships
NLP
Combine the analysis of syntax and semantics
Using extraction rules that are implemented manually or learned automatically from annotated corpus
However,
Cooccurrences sometimes do not actually mean correct relations
Cannot infer directional relationships from cooccurrences

23. Hard Lessons learned from TREC
Synonyms from gene dictionary is NOT enough
Generating gene symbol variants is essential
One query is not enough to do the job
Generating query variants, which are slight variations of the original query.
For instance, the query “inhibitory synoptic transmission” can have the variants “synoptic transmission” and “inhibitory transmission”.

24. more…. Abstracts related to a gene family can be relevant as well
Suppose we want to know about the gene COPII, we may want to know COP, COPI as well
Abstracts can merely mention an entity as an example
e.g. [PMID ]: GSTM1 is mentioned to be related to breast cancer as an example, but article is about GSTM1 and alcoholism.

25. Future Components Structural Feedback Answer Presentation
Semantics of Words
Simple Reasoning using Domain Knowledge

26. Structural Feedback Problem:
Can we use the underlying “structures” among the relevant articles to improve the retrieval process? [IBM]
Goal: To learn the “structures” of abstracts that are identified as relevant.
Idea: Learn the structure of articles (such as common words, MeSH terms)
identified to be relevant by domain experts
identified to be relevant by users

27. Answer Presentation
To present answers to users in a precise and concise manner
Current Status: relevant “answers” are presented to the users in the form of abstracts
Problem: Not concise enough for users
Ideas:
Retrieve small passage of text, based on proximity of keywords [LCC02] and simple cosine similarity between sentences [Singapore05].
Extraction using NLP
Use text summarization techniques to present answers [PSB06].

28. Semantics of Words
WordNet – a resource that provides synonyms of words in different senses; relations between words
Question:
“What is the role of IDE in Alzheimer’s Disease?”
Abstract (PMID: ):
“… IDE plays in the degradation and clearance of human amyloid beta from migroglial cells and neurons …”
Semantic relation between “role” and “play” [from WordNet]:
role: function, purpose, role, use
play: is_a(play_use)
So we can say “role”, “play”, “use” are related.
Answer: The role of IDE is in the degradation and clearance of human amyloid beta from migroglial cells and neurons.

29. Simple Reasoning using Domain Knowledge (Example 1)
Question:
“Does IDE play a role in Alzheimer’s Disease (AD)?”
Retrieved Abstract (PMID: ):
“… The insulin degrading enzyme (IDE) is an attractive candidate gene since previous studies have identified a possible role that IDE plays in the degradation and clearance of human amyloid beta from migroglial cells and neurons …”
Domain knowledge:
AD is a nervous system disease.
Neurons are related to the nervous system.
Answer: Yes, IDE plays a role in AD because AD is a nervous system disease and IDE plays in the degradation and clearance of human amyloid beta from migroglial cells and neurons.

30. Simple Reasoning using Domain Knowledge (Example 2)
Question: Does MMS2 involve in cancer?
Domain Knowledge about MMS2
MMS2 is known to be involved in biological processes such as cell proliferation and the ubiquitin cycle, based on the Gene Ontology.
Cell Proliferation – cell growth
Ubiquitin cycle – regulating proteins' half-lives
Either way, that is deviating from the normal half- life, and that is not a good thing.

31. Simple Reasoning using Domain Knowledge (Example 2 cont.)
Domain Knowledge about cancer
Abnormal growth of tissues
Sometimes in cancer, we find that the ubiquitin cycle is deregulated, leading to certain proteins having extra long or extra short half-lives.
Answer: Yes. Since MMS2 is involved in regulating cell proliferation and ubiquitin cycle, MMS2 is possibly involved in cancer.
Challenges:
How to represent such knowledge
Where to get such domain knowledge

32. Potential Projects Learning Answer Presentation Extraction
Structural Feedback
Rules for describing keywords in questions
Answer Presentation
Passage retrieval, extraction
Extraction
gene-disease, gene-biological process relations
Sortal Resolution
Semantics of Words

33. References
Literature mining for the biologist: from information retrieval to biological discovery. Lars Juhl Jensen, Jasmin Saric and Peer Bork. Nature Reviews Genetics 7, (February 2006).
Anaphora Resolution
Anaphora Resolution in Biomedical Literature. Jose Castano, Jason Zhang, James Pustejovsky.
Extraction of Gene-Disease Relations
Association of genes to genetically inherited diseases using data mining. Perez-Iratxeta C, Bork P, Andrade MA. Nature Genetics 31, (2002).
G2D: A Tool for Mining Genes Associated to Disease. Perez-Iratxeta C, Wjst M, Bork P, Andrade MA. BMC Genetics 6, 45 (2005).
Extraction of Gene-Disease Relations from Medline Using Domain Dictionaries and Machine Learning. Hong-Woo Chun, Yoshimasa Tsuruoka, Jin-Dong Kim, Rie Shiba, Naoki Nagata, Teruyoshi Hishiki, and Jun'ichi Tsujii. PSB 2006.
Structural Feedback
[IBM] Rie Kubota Ando, Mark Dredze, Tong Zhang. TREC 2005 Genomics Track Experiments at IBM Watson.

34. References Answer Presentation WordNet Resources
[LCC02] Dan I. Moldovan, Mihai Surdeanu: On the Role of Information Retrieval and Information Extraction in Question Answering Systems. SCIE 2002:
[Singapore05] Hang Cui, Renxu Sun, Keya Li, Min-Yen Kan and Tat-Seng Chua, Question Answering Passage Retrieval Using Dependency Relations, In Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development of Information Retrieval (SIGIR 2005), Salvador, Brazil, August , 2005.
[PSB06] Zhiyong Lu, K. Bretonnel Cohen, and Lawrence Hunter. Finding GeneRIFs via Gene Ontology Annotations. To appear in PSB 2006.
WordNet Resources
[WordNetSim] Pedersen, Patwardhan, and Michelizzi. WordNet::Similarity - Measuring the Relatedness of Concepts. Appears in the Proceedings of the Nineteenth National Conference on Artificial Intelligence (AAAI-04), July 25-29, 2004, San Jose, CA (Intelligent Systems Demonstration).
[SenseRelate] Michelizzi. Semantic Relatedness Applied to All Words Sense Disambiguation. Master of Science Thesis, Department of Computer Science, University of Minnesota, Duluth, July, 2005.