bioqa - a question answering system for the biomedical domain luis tari
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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
QA How is QA different from a search engine?
Check out www.brainboost.com
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
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.
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
TREC Genomics Templates
1. Find articles describing standard methods or protocols for doing some sort of experiment or procedure.
2. Find articles describing the role of a gene involved in a given disease.
3. Find articles describing the role of a gene in a specific biological process.
4. 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.
5. Find articles describing one or more mutations of a given gene and its biological impact.
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
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….
Example 2 Query: “interferon beta” AND “multiple
sclerosis”
Hmm… this is more like what I am looking for….
Objectives of BioQA
Phase 1 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
BioQA PrototypeOnline Subsystem
Accept User’s Query
Database
Process User’s Query(Tag, Lucene Syntax,
Stem...)
Search Database (using Lucene indexes)
Filter result
Rank/Categorize result
Index
Present results to User
Allow user to modify/choose query patterns
Main Components of BioQA Phase 1
Question Processing and Query Formation Entity Recognition Indexing Pronoun Resolution Extraction Ranking
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?
Question Processing and Query Formation
Third idea – making use of dependency grammar (Link Grammar)
+-----------------Xp-----------------+ | +--------MVp--------+ | | +---Ost---+ | | +---Ws--+Ss*w+ +--Ds-+-Mp-+J+ +J+ | | | | | | | | | | |LEFT-WALL what is.v the role.n of X in Y ?
In the following example, N1= “role” and N2= “X” in the question
keyword(N2) :- noun(N1), noun(N2), Mp(N1,X), J(X,N2).
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
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
Anaphora Resolution
Pronominal 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: 10024886]
Which histones?
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: 8863848]
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: 15920482]
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.
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: 11401490]
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
Extraction Two main directions towards extraction:
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
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”.
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 10232877]: GSTM1 is mentioned to be
related to breast cancer as an example, but article is about GSTM1 and alcoholism.
Future Components
Structural Feedback Answer Presentation Semantics of Words Simple Reasoning using Domain Knowledge
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
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].
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:12161276):
“… 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.
Simple Reasoning using Domain Knowledge (Example 1) Question:
“Does IDE play a role in Alzheimer’s Disease (AD)?” Retrieved Abstract (PMID:12161276):
“… 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.
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
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
Potential Projects
Learning 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
References Literature mining for the biologist: from information retrieval to biological
discovery. Lars Juhl Jensen, Jasmin Saric and Peer Bork. Nature Reviews Genetics 7, 119-129 (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, 316-319 (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.
References Answer Presentation
[LCC02] Dan I. Moldovan, Mihai Surdeanu: On the Role of Information Retrieval and Information Extraction in Question Answering Systems. SCIE 2002: 129-147.
[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 15 -19, 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.