Presentation is loading. Please wait.

Presentation is loading. Please wait.

Concept Recognition, Information Retrieval, and Machine Learning in Genomics Question Answering J. Gregory Caporaso Center for Computational Pharmacology.

Published byRoland Marshall Modified over 9 years ago

Similar presentations

Presentation on theme: "Concept Recognition, Information Retrieval, and Machine Learning in Genomics Question Answering J. Gregory Caporaso Center for Computational Pharmacology."— Presentation transcript:

1 Concept Recognition, Information Retrieval, and Machine Learning in Genomics Question Answering J. Gregory Caporaso Center for Computational Pharmacology Department of Biochemistry and Molecular Genetics University of Colorado Health Sciences Center gregcaporaso@gmail.com http://hsc-turing.uchsc.edu

2 TREC Genomics 2006 Q: What is the role of DRD4 in alcoholism? Q: What is the role of DRD4 in alcoholism? Q: What is the role of DRD4 in alcoholism? Q: What is the role of huntingtin in Huntington's Disease? 28 topic questions 162,259 full-text journal articles Q: What is the role of DRD4 in alcoholism? Q: What is the role of DRD4 in alcoholism? Q: What is the role of DRD4 in alcoholism? A: Repeats in the huntingtin gene cause protein aggregation and likely cause HD. Up to 1000 passages per topic questions Mean average precision at document, passage, and aspect levels Relevance judged by human domain experts dfsfdsfjlgdfsdfslkdfsdfsdfsdfsdfsdfsdf DfsfdsTYYfjlgdf Sdfsl Pkdf sdf Asdfs Ldfsdf Fsdf Psdf by Kilgore Trout dfsfdsfjlgdfsdfslkdfsdfsdfsdfsdfsdfsdf

3 1. Document processing: HTML parsing, paragraph splitting, document zoning 2. Section filtering 3. Indri indexing 4. Query expansion i.Strict ii.Loose 5. Indri query, pseudo-relevance feedback 6. Result expansion with Latent Semantic Analysis 7. Naïve Bayes-based result filtering uchsc3uchsc2uchsc1 Plasmids and cell lines Experimental Methods Materials and Methods introduction Concluding remarks Discussion Results res-disc-conc Acknowlegements Aknowledgements Acknowledgments acknowledgments Abstract Introduction Methods Introduction Conclusions References UCHSC approach

4 UCHSC results and conclusions Above mean on each performance metric Performed well where median was low

5 Genomics track results and conclusions Best systems: indexed paragraphs, expanded queries, selected sentences Graph modified from: TREC 2006 Genomics Track Overview, W. Hersh, A.M. Cohen, P. Roberts, H.K. Rekapalli, TREC 2006 Notebook (68), November 2006. Document mean average precision Mean average precision Passage Aspect uchsc2 Successful techniques included concept-based query expansion, acronym expansion Unsuccessful techniques included cluster-based reranking

6 TREC 2006 co-authors Future Directions TREC Genomics 2007: same corpus, very similar or identical task Gold-standard will be released allowing for in-depth comparison of methods Concept Recognition, Information Retrieval, and Machine Learning in Genomics Question-Answering, J. G. Caporaso, W.A. Baumgartner, Jr., H. Kim, Z. Lu, H.L. Johnson, O. Medvedeva, A. Lindemann, L. Fox, E.K. White, K. B. Cohen, L. Hunter. TREC 2006 Proceedings (723), November, 2006. Larry Hunter Bill Baumgartner Kevin Cohen Lynne Fox Helen Johnson Hyunmin Kim Anna Lindemann Zhiyong Lu Olga Medvedeva Elizabeth White gregcaporaso@gmail.com http://hsc-turing.uchsc.edu

Download ppt "Concept Recognition, Information Retrieval, and Machine Learning in Genomics Question Answering J. Gregory Caporaso Center for Computational Pharmacology."

Similar presentations

1 Evaluation Rong Jin. 2 Evaluation  Evaluation is key to building effective and efficient search engines usually carried out in controlled experiments.

1 Evaluation Rong Jin. 2 Evaluation  Evaluation is key to building effective and efficient search engines usually carried out in controlled experiments.
DY 20061 An Information Retrieval Approach based on Discourse Type D. Y. Wang, R. W. P. Luk, K.F. Wong 1 and K.L. Kwok 2 NLDB 2006 Department of.

DY An Information Retrieval Approach based on Discourse Type D. Y. Wang, R. W. P. Luk, K.F. Wong 1 and K.L. Kwok 2 NLDB 2006 Department of.
NYU ANLP-00 1 Automatic Discovery of Scenario-Level Patterns for Information Extraction Roman Yangarber Ralph Grishman Pasi Tapanainen Silja Huttunen.

NYU ANLP-00 1 Automatic Discovery of Scenario-Level Patterns for Information Extraction Roman Yangarber Ralph Grishman Pasi Tapanainen Silja Huttunen.
Query Dependent Pseudo-Relevance Feedback based on Wikipedia SIGIR ‘09 Advisor: Dr. Koh Jia-Ling Speaker: Lin, Yi-Jhen Date: 2010/01/24 1.

Query Dependent Pseudo-Relevance Feedback based on Wikipedia SIGIR ‘09 Advisor: Dr. Koh Jia-Ling Speaker: Lin, Yi-Jhen Date: 2010/01/24 1.
Language Model based Information Retrieval: University of Saarland 1 A Hidden Markov Model Information Retrieval System Mahboob Alam Khalid.

Language Model based Information Retrieval: University of Saarland 1 A Hidden Markov Model Information Retrieval System Mahboob Alam Khalid.
1 Entity Ranking Using Wikipedia as a Pivot (CIKM 10’) Rianne Kaptein, Pavel Serdyukov, Arjen de Vries, Jaap Kamps 2010/12/14 Yu-wen,Hsu.

1 Entity Ranking Using Wikipedia as a Pivot (CIKM 10’) Rianne Kaptein, Pavel Serdyukov, Arjen de Vries, Jaap Kamps 2010/12/14 Yu-wen,Hsu.
Introduction Information Management systems are designed to retrieve information efficiently. Such systems typically provide an interface in which users.

Introduction Information Management systems are designed to retrieve information efficiently. Such systems typically provide an interface in which users.
Predicting Text Quality for Scientific Articles AAAI/SIGART-11 Doctoral Consortium Annie Louis : Louis A. and Nenkova A. 2009. Automatically.

Predicting Text Quality for Scientific Articles AAAI/SIGART-11 Doctoral Consortium Annie Louis : Louis A. and Nenkova A Automatically.
Reference Collections: Task Characteristics. TREC Collection Text REtrieval Conference (TREC) –sponsored by NIST and DARPA (1992-?) Comparing approaches.

Reference Collections: Task Characteristics. TREC Collection Text REtrieval Conference (TREC) –sponsored by NIST and DARPA (1992-?) Comparing approaches.
Retrieval Evaluation: Precision and Recall. Introduction Evaluation of implementations in computer science often is in terms of time and space complexity.

Retrieval Evaluation: Precision and Recall. Introduction Evaluation of implementations in computer science often is in terms of time and space complexity.
QuASI: Question Answering using Statistics, Semantics, and Inference Marti Hearst, Jerry Feldman, Chris Manning, Srini Narayanan Univ. of California-Berkeley.

QuASI: Question Answering using Statistics, Semantics, and Inference Marti Hearst, Jerry Feldman, Chris Manning, Srini Narayanan Univ. of California-Berkeley.
Abstract and keywords Sadeghi Ramin, MD Nuclear Medicine Research Center, Mashhad University of Medical Sciences.

Abstract and keywords Sadeghi Ramin, MD Nuclear Medicine Research Center, Mashhad University of Medical Sciences.
UCB BioText TREC 2003 Participation Participants: Marti Hearst Gaurav Bhalotia, Presley Nakov, Ariel Schwartz Track: Genomics, tasks 1 and 2.

UCB BioText TREC 2003 Participation Participants: Marti Hearst Gaurav Bhalotia, Presley Nakov, Ariel Schwartz Track: Genomics, tasks 1 and 2.
Information retrieval Finding relevant data using irrelevant keys Example: database of photographic images sorted by number, date. DBMS: Well structured.

Information retrieval Finding relevant data using irrelevant keys Example: database of photographic images sorted by number, date. DBMS: Well structured.
Retrieval Evaluation. Introduction Evaluation of implementations in computer science often is in terms of time and space complexity. With large document.

Retrieval Evaluation. Introduction Evaluation of implementations in computer science often is in terms of time and space complexity. With large document.
An investigation of query expansion terms Gheorghe Muresan Rutgers University, School of Communication, Information and Library Science 4 Huntington St.,

An investigation of query expansion terms Gheorghe Muresan Rutgers University, School of Communication, Information and Library Science 4 Huntington St.,
Latent Semantic Analysis (LSA). Introduction to LSA Learning Model Uses Singular Value Decomposition (SVD) to simulate human learning of word and passage.

Latent Semantic Analysis (LSA). Introduction to LSA Learning Model Uses Singular Value Decomposition (SVD) to simulate human learning of word and passage.
Team 8 Mowry, Srinivasan and Wong Ling 573, Spring University of Washington.

Team 8 Mowry, Srinivasan and Wong Ling 573, Spring University of Washington.
Cis-Regulatory/ Text Mining Interface Discussion.

Cis-Regulatory/ Text Mining Interface Discussion.
Srihari-CSE730-Spring 2003 CSE 730 Information Retrieval of Biomedical Text and Data Inroduction.

Srihari-CSE730-Spring 2003 CSE 730 Information Retrieval of Biomedical Text and Data Inroduction.

Similar presentations

Ads by Google