Presentation on theme: "BioQA - A question answering system for the biomedical domain"— Presentation transcript:
1BioQA - A question answering system for the biomedical domain Luis Tari
2Question 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
3QA How is QA different from a search engine? QA Search Engine Check outQASearch EngineQueries in Natural Language (Questions)Queries based on keywordsPresent answers to usersUsers find the answers from retrieved resultsSome natural language process is used to determine answersMostly keywords and ranking to retrieve results
4Text 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.
5TREC Genomics TrackTREC 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
6TREC 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.
7BioQA A QA system for the biomedical domain A great deal of genomics information resources are availableEntrez Gene, PubMed, UniProt, Gene Ontology, UMLS, many many more…BioQA utilizes some of the genomics resources, whereas a generic QA does notKeyword search is not enoughConsider the following examples
8Example 1Suppose 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…
9Example 2 Query: “interferon beta” AND “multiple sclerosis” good exampleHmm… this is more like what I am looking for….
10Objectives of BioQA Phase 1 Phase 2 Phase 3 Retrieve relevant articles with respect to the specific needs of user’s questionsPhase 2Extract and present answers to the usersPhase 3Answer questions that require simple reasoning
13Main Components of BioQA Phase 1 Question Processing and Query FormationEntity RecognitionIndexingPronoun ResolutionExtractionRanking
14Question Processing and Query Formation Process questions so that keywords are extracted to form queries for retrievalIncorporate synonyms for the keywordsConsider the question:“What is the role of PRNP in mad cow disease?”First ideaGet all the nouns from the questionBut we do not want a query that includes “role”Second ideaIdentify all the entities from the question and treat them as keywordsBut what if we are unable to identify some of the entities?
15Question 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 questionXp| MVp || Ost | |+---Ws--+Ss*w Ds-+-Mp-+J+ +J+ || | | | | | | | | |LEFT-WALL what is.v the role.n of X in Y ?
16Entity Recognition To recognize gene symbols, disease names Lots of resources ongene 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 tasksRecognize gene names given a biomedical articleGenerate gene symbol synonyms and variants given a gene symbol in a queryNotice that “Cdc28”, “Cdc28p”, “cdc-28” are not synonyms of “CDC28”, so they are not listed in dictionaries such as Entrez Gene
17Entity Recognition Various approaches: Machine learning techniques to recognize names on the basis of their characteristic featuresDictionary-based methods with generation of variantsDictionary-based + Part-of-Speech methodsRule-based methodsSome of the best Entity Taggers:ABNERGAPSCORE
18Anaphora Resolution Pronominal Anaphors Sortal Anaphors Resolving third-person pronouns and reflexive pronounsExample: “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?
19Anaphora 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: ]
20Extraction To extract knowledge from text Knowledge such as protein-protein interactions, gene-disease relations, …Can be used in presenting answersExtracting 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 Clb2Swe1 is phosphorylated by Clb2-Cdc28 complexCdc5 is involved in Swe1 phosphorylation.
21Extraction 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 cellsPACT is involved in the regulation of viral replication
22Extraction Two main directions towards extraction: However, CooccurrenceIdentify entities that co-occur within abstractsFrequency-based scoring scheme to rank the extracted relationshipsNLPCombine the analysis of syntax and semanticsUsing extraction rules that are implemented manually or learned automatically from annotated corpusHowever,Cooccurrences sometimes do not actually mean correct relationsCannot infer directional relationships from cooccurrences
23Hard Lessons learned from TREC Synonyms from gene dictionary is NOT enoughGenerating gene symbol variants is essentialOne query is not enough to do the jobGenerating 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”.
24more…. 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 wellAbstracts can merely mention an entity as an examplee.g. [PMID ]: GSTM1 is mentioned to be related to breast cancer as an example, but article is about GSTM1 and alcoholism.
25Future Components Structural Feedback Answer Presentation Semantics of WordsSimple Reasoning using Domain Knowledge
26Structural 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 expertsidentified to be relevant by users
27Answer PresentationTo present answers to users in a precise and concise mannerCurrent Status: relevant “answers” are presented to the users in the form of abstractsProblem: Not concise enough for usersIdeas:Retrieve small passage of text, based on proximity of keywords [LCC02] and simple cosine similarity between sentences [Singapore05].Extraction using NLPUse text summarization techniques to present answers [PSB06].
28Semantics of WordsWordNet – a resource that provides synonyms of words in different senses; relations between wordsQuestion:“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, useplay: 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.
29Simple 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.
30Simple Reasoning using Domain Knowledge (Example 2) Question: Does MMS2 involve in cancer?Domain Knowledge about MMS2MMS2 is known to be involved in biological processes such as cell proliferation and the ubiquitin cycle, based on the Gene Ontology.Cell Proliferation – cell growthUbiquitin cycle – regulating proteins' half-livesEither way, that is deviating from the normal half- life, and that is not a good thing.
31Simple Reasoning using Domain Knowledge (Example 2 cont.) Domain Knowledge about cancerAbnormal growth of tissuesSometimes 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 knowledgeWhere to get such domain knowledge
32Potential Projects Learning Answer Presentation Extraction Structural FeedbackRules for describing keywords in questionsAnswer PresentationPassage retrieval, extractionExtractiongene-disease, gene-biological process relationsSortal ResolutionSemantics of Words
33ReferencesLiterature 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 ResolutionAnaphora Resolution in Biomedical Literature. Jose Castano, Jason Zhang, James Pustejovsky.Extraction of Gene-Disease RelationsAssociation 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.
34References 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.