Presentations text content in Biomedical articles per year
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Biomedical articles per year
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Slide3Questions of biomedical experts
“Are there any DNMT3 proteins present in plants?”
“Yes”
“Yes. The plant DOMAINS REARRANGED METHYLTRANSFERASE2 (DRM2) is a homolog of the mammalian de novo methyltransferase DNMT3. DRM2 contains a novel arrangement of the motifs required for DNA methyltransferase catalytic activity.”
Yes/No question
Exact Answer
Ideal Answer
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Slide4Questions of biomedical experts
“What is the methyl donor of DNA (cytosine-5)-methyltransferases?”
“S-adenosyl-L-methionine”
“S-adenosyl-L-methionine (AdoMet, SAM) is the methyl donor of DNA (cytosine-5)-methyltransferases. DNA (cytosine-5)-methyltransferases catalyze the transfer of a methyl group from S-adenosyl-L-methionine to the C-5 position of cytosine residues in DNA.”
Factoid question
Exact Answer
Ideal Answer
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Slide5Questions of biomedical experts (III)
List question
“In 1955, the production of itaconic acid was firstly described for Ustilago maydis. Some Aspergillus species, like A. itaconicus and A. terreus, show the ability to synthesize this organic acid and A. terreus can secrete significant amounts to the media. Itaconic acid is mainly supplied by biotechnological processes with the fungus Aspergillus terreus. Cloning of the cadA gene into the citric acid producing fungus A. niger showed that it is possible to produce itaconic acid also in a different host organism.”
“Aspergillus terreus”, “Aspergillus niger”, “Ustilago maydis”
Exact Answer
Ideal Answer
“Which species may be used for the biotechnological production of itaconic acid?”
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Slide6Questions of biomedical experts (III)
Summary question
“Histone methyltransferases (HMTs) are responsible for the site-specific addition of covalent modifications on the histone tails, which serve as markers for the recruitment of chromatin organization complexes. There are two major types of HMTs: histone-lysine N-Methyltransferases and histone-arginine N-methyltransferases. The former methylate specific lysine (K) residues such as 4, 9, 27, 36, and 79 on histone H3 and residue 20 on histone H4. The latter methylate arginine (R) residues such as 2, 8, 17, and 26 on histone H3 and residue 3 on histone H4. Depending on what residue is modified and the degree of methylation (mono-, di- and tri-methylation), lysine methylation of histones is linked to either transcriptionally active or silent chromatin.”
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Exact Answer
Ideal Answer
“How do histone methyltransferases cause histone modification?”
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Slide8Finding relevant snippets
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Slide9Not only texts: ontologies, linked data, …
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Slide11Information from structured data
List question
http://www.disease-ontology.org/api/metadata/DOID:162 (cancer) http://www.uniprot.org/uniprot/M3K8_RAT (TPL2 synonym)
Subject: http://www4.wiwiss.fu-berlin.de/diseasome/resource/diseases/3003 (lung cancer)Predicate: http://www4.wiwiss.fu-berlin.de/diseasome/resource/diseasome/associatedGeneObject: http://www4.wiwiss.fu-berlin.de/diseasome/resource/genes/TPL2"
Related RDF triple
Related concepts
“Which forms of cancer is the Tpl2 gene associated with?”
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Slide13BioASQ Vision
Make sure this knowledge is used to the benefit of patientsNeed to make it accessible to biomedical expertsSearch is not effective enoughPush research in automated answering of questionsA challenge for such systems can achieve a multiplying effect
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Slide14What is BioASQ?
A challenge funded by the European Union (FP7).
Task a: Hierarchical text classificationOrganizers distribute new unclassified PubMed articles.Participants assign MeSH terms to the articles.Evaluation based on annotations of PubMed curators.
Task b: IR, QA, summarization, …Organizers distribute English biomedical questions.Participants provide: relevant articles, snippets, concepts, triples, “exact” answers, “ideal” answers. Evaluation: both automatic (GMAP, MRR, ROUGE etc.) and manual (by biomedical experts).
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Slide15Task
b
The challenge
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Task
a
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Slide17Behind the scenes
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Slide18BioASQ Platform
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Slide19Datasets
Task b data contain gold articles, snippets, concepts, triples, “exact” and “ideal” answers prepared by biomedical experts from around Europe.
Task a1st challenge2nd challengeTraining10,876,00412,628,968Test8349071950
Task b1st challenge2nd challengeTraining29310Test281500
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Slide20Data sources
They
include both text and structured info.PubMed abstracts, PubMed Central articles, MeSH.Gene Ontology, UniProt, Jochem, Disease Ontology.
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Slide21Annotation: questions and queries
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Slide22Annotation: snippets
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Slide23Annotation: answers
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Slide24Assessment: relevance of material
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Slide25Assessment: information in answers
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Slide26BioASQ social network
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Slide27Oracle
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Slide28Oracle
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Slide29Two cycles
March 2013 June 2013 August 2013 September 2013
2013 Schedule
February 2014 March 2014 May 2014 September 2014
2014 Schedule
The official challenge is
over, but…Task a continues to run each week .An oracle for task b will be available soon.Oracles will remain available.Third cycle is being designed …
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Slide30Challenge participants so far
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Slide31Challenge participants in each cycle
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Slide32Evaluation measures
Task a: Hierarchical text classificationFlat measures for multi-label classification: Accuracy, MiF, MaF, EBFHierarchical measures: LCA-F (new), HF
Task b: IR, QA, summarization, …Phase A: standard IR measures, mean precision, mean recall, mean F-measure, MAP (used for winners selection), G-MAPPhase B:‘Exact answers’ (based on type): accuracy (yes/no), strict/lenient accuracy, MRR (factoid), mean F-measure (list)‘Ideal answers’: manual scores from the experts {Readability, Repetition, Information Precision and Recall}, plus ROUGE
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Slide33First year technology/results overview
Task 1aMainly SVMs and learning-to-rank.Mostly flat classification, ignoring class taxonomy.Mediocre results by hierarchical methods.One of the systems outperformed NLM’s system.Task 1bPhase A (retrieve relevant documents, concepts, snippets, triples): low performance (compared to baselines).Phase B (formulate ‘exact’ and ‘ideal’ answers): poor performance for ‘exact’ answers (except for yes/no questions); high performance for ‘ideal’ answers (paragraph-sized summaries), but starting with gold documents, snippets etc.Large scope for improvements, esp. in Task 1b.
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Slide34“Exact” answer results (batch 2/3)
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Slide35“Ideal” answer results (batch 2/3)
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Slide36Results – task a – flat measures
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Slide37Results – task a – hierarchical
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Slide38First challenge prizes
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Slide39Sustainability
BioASQ OracleSoftware release and installation instructionsBenchmark datasets BioASQ social networkInvolvement of the biomedical community in the processAttracting sponsors for prizes
Making the challenge viable, at very low cost, after the end of the project
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Slide40Project Consortium
National
Centre for Scientific Research “Demokritos” -NSCR “D” (EL)Transinsight GmbH – TI (D)Universite Joseph Fourier- UJF (F)University Leipzig - ULEI (D)Universite Pierre et Marie Curie Paris 6 – UPMC (F)Athens University of Economics and Business – Research Centre – AUEB-RC (EL)
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Slide41Project Consortium
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Slide42Get in touch!
BioASQ workshop @CLEF (Sheffield, Sept 14)Visit www.bioasq.orgFollow @BioASQ
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Slide43Useful Links
BioASQ Annotation & assessment tools:http://at.bioasq.org/http://assess.bioasq.org/https://github.com/AKSW/BioASQ-ATBioASQ social network: http://sn.bioasq.org/https://github.com/AKSW/BioASQ-SNBioASQ platform: http://bioasq.lip6.fr/BioASQ Oracles: http://bioasq.lip6.fr/oracle/
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A. Kosmopoulos, I. Partalas, E. Gaussier, G. Paliouras, I. Androutsopoulos
,
Evaluation
Measures for Hierarchical Classification: a unified view and novel
approaches.
Data Mining and Knowledge
Discovery (To appear)
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Biomedical articles per year
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