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    2010
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    Machine Learning Models for Phenotype Classification and Biomarker Discovery
    Local Causal Discovery, Markov Blanket Induction, and Feature Selection
    Publicly Available Tools and Software
    Global Causal Discovery and Reverse Engineering of Regulatory Networks
    Text Categorization and Information Retrieval
    Planning and Temporal Reasoning
    Active Learning
    Other


Selected publications on All topics, All years:

2010

Chen Y, Mani S. Study of Active Learning in the Challenge. accepted in the conference of World Congress on Computational Intelligence, July 19-23, 2010, Barcelona, Spain.

Chen Y, Mani S. Active Learning for Unbalanced Data in the Challenge with Multiple Models and Biasing. will appear in Journal of Machine Learning Research: Workshop and Conference Proceedings, 2010.

Aliferis C, Statnikov A, Tsamardinos I, Mani S, Koutsoukos X. Local causal and Markov blanket induction for causal discovery and feature selection for classification. Part II: Analysis and extensions. Journal of Machine Learning Research 11:235-284, 2010.

Aliferis C, Statnikov A, Tsamardinos I, Mani S, Koutsoukos X. Local causal and Markov blanket induction for causal discovery and feature selection for classification. Part I: Algorithms and empirical evaluation. Journal of Machine Learning Research 11:171-234, 2010.

Mani S, Statnikov A and Aliferis C. Bayesian Algorithms for Causal Data Mining. Proceedings of the NIPS Workshop on Causality 6:121-136, 2010.

Xu H, Stenner SP, Doan S, Johnson KB, Waitman LR, Denny JC. MedEx – A Medication Information Extraction System for Clinical Narratives. JAMIA 2010; 171:19-24.

Xu H, Doan S, Birdwell KA, Cowan JD, Vincz AJ, Haas DW, Basford MA, Denny JC. An automated approach to calculating the daily dose of tacrolimus in electronic health records. AMIA 2010 Clinical Research Informatics Summit.

2009

Varol A, Mani S, Compton D, Fuchs L, Fuchs D. Early Prediction of Reading Disability using Machine Learning. Proceedings of the AMIA fall symposium, 467-471, 2009.

Xu H, Stetson P, Friedman C. Methods for Building Sense Inventories of Abbreviations in Clinical Notes. JAMIA, 2009 161:103-108.

Denny JC, Peterson JF, Choma NN, Xu H, Miller RA, Bastarache L, Peterson NB. Development of a Natural Language Processing System to Identify Timing and Status of Colonoscopy Testing in Electronic Medical Records. AMIA 2009 accepted, pending JAMIA review.

2008

Tsamardinos I, Brown LE. Bounding the False Discovery Rate in Local Bayesian Network Learning. Proceedings of the Twenty-Third National Conference on Artificial Intelligence AAAI 2008. [Article] [Supplement]

Brown LE, Tsamardinos I. Markov Blanket-Based Variable Selection in Feature Space. Technical Report DSL TR-08-01, 2008.

Statnikov A, Wang L, Aliferis CF. A Comprehensive Comparison of Random Forests and Support Vector Machines for Microarray-Based Cancer Classification. BMC Bioinformatics, 2008; 9:319. [Pubmed]

Statnikov A, Li C, Aliferis CF. A Statistical Reappraisal of the Findings of an Esophageal Cancer Genome-Wide Association Study. Cancer Research, 2008; 68: 3074-3075. [Pubmed]

2007

Guyon I, Aliferis CF, Elisseeff A. Causal Feature Selection. In: Computational Methods of Feature Selection, H. Liu and H. Motoda Eds. Chapman and Hall, 2007. [Article]

Fu L, Wang L, Aphinyanaphongs Y, Aliferis CF. A Comparison of Impact Factor, Clinical Query Filters, and Pattern Recognition Query Filters in Terms of Sensitivity to Topic. Medinfo 2007.

Aphinyanaphongs, Y, Aliferis CF. Categorization Models for Identifying Unproven Cancer Treatments on the Web. Medinfo 2007. [Article]

Dexheimer JW, Brown LE, Leegon J, Aronsky D. Comparing Decision Support Methodologies for Identifying Asthma Exacerbations. Medinfo 2007.

Mani S, Aliferis CF, Krishnaswami S, Kotchen T. Learning Causal and Predictive Clinical Practice Guidelines from Data. Medinfo 2007.

Shifrin M, Belousova O, Kasparova E. Diagnostic Games, Tool for Clinical Experience Formalization in Interactive Physician-IT-specialist Framework. Proceedings of the 20th IEEE International Symposium on Computer-Based Medical Systems, 2007. [Article]

Aliferis CF, Statnikov A, Tsamardinos I, Mani S, Koutsoukos X. Local Causal and Markov Blanket Induction Algorithms for Causal Discovery and Feature Selection for Classification. Technical Report DSL TR-07-02, 2007.

Mani S, Aliferis CF, Krishnaswami S, Kotchen T. Learning causal and predictive clinical practice guidelines from data. Medinfo 2007;850-4. [Article]

Mani S, Aliferis CF. A Causal Modeling Framework for Generating Clinical Practice Guidelines from Data. AIME 2007:446-450. [Article]

Statnikov A, Li C, Aliferis CF. Effects of Environment, Genetics and Data Analysis Pitfalls in an Esophageal Cancer Genome-Wide Association Study. PLoS ONE, 2007; 29: e958. [Pubmed]

Statnikov A, Aliferis CF. Are Random Forests Better than Support Vector Machines for Microarray-Based Cancer Classification?. AMIA Annual Symposium Proceedings, 2007. [Article]

2006

Aliferis CF, Statnikov A, Tsamardinos I. Challenges in the Analysis of Mass-Throughput Data: A Technical Commentary from the Statistical Machine Learning Perspective. Cancer Informatics 2006;2:133-62. [Article]

Aliferis CF, Statnikov A, Tsamardinos I, Kokkotou E, Massion PP. Application and Comparative Evaluation of Causal and Non-Causal Feature Selection Algorithms for Biomarker Discovery in High-Throughput Biomedical Datasets. Proceedings of the NIPS Workshop on Causality and Feature Selection, 2006.

Aliferis CF, Statnikov A, Massion PP. Pathway Induction and High-Fidelity Simulation for Molecular Signature and Biomarker Discovery in Lung Cancer Using Microarray Gene Expression Data. Proceedings of the APS Conference: Physiological Genomics and Proteomics of Lung Disease, 2006.

Statnikov A, Hardin D, Aliferis CF. Using SVM weight-based methods to identify causally relevant and non-causally relevant variables. Proceedings of the NIPS 2006 Workshop on Causality and Feature Selection, 2006.

Statnikov A, Tsamardinos I, Aliferis CF. New Efficient and Correct Algorithms for Identification of Direct Causal Relationships and Markov Blankets from Data.. Technical Report DSL TR-06-01, 2006.

Mani S, Spirtes P, Cooper GF. A theoretical study of Y structures for causal discovery. Proceedings of the Conference on Uncertainty in Artificial Intelligence UAI, 2006. [Article]

Mani S, Cooper GF. Causal Discovery Algorithms based on Y Structures. Proceedings of the NIPS 2006 Workshop on Causality and Feature Selection, 2006.

Tsamardinos I, Brown LE, Aliferis CF. The Max-Min Hill-Climbing Bayesian Network Structure Learning Algorithm. Machine Learning 2006;651:31-78. [Article] [Supplement]

Aphinyanaphongs Y, Statnikov A, Aliferis CF. A comparison of citation metrics to machine learning filters for the identification of high quality MEDLINE documents. J Am Med Inform Assoc 2006 Jul;134:446-55. [Pubmed]

Aphinyanaphongs Y, Aliferis CF. Prospective validation of text categorization models for indentifying high-quality content-specific articles in PubMed. AMIA 2006 Annual Symposium Proceedings. [Article]

Bernstam EV, Herskovic JR, Aphinyanaphongs Y, Aliferis CF, Sriram MG, Hersh WR. Using citation data to improve retrieval from MEDLINE. J Am Med Inform Assoc 2006 Jan;131:96-105. [Pubmed]

Tsamardinos I, Statnikov A, Brown LE, Aliferis CF. Generating Realistic Large Bayesian Networks by Tiling. Proceedings of the Nineteenth International Florida Artificial Intelligence Research Society FLAIRS Conference, 2006. [Article]

Statnikov A, Kasparova E, Aliferis CF. Applying Decision Support Models in the Presence of Incomplete Evidence. Technical Report DSL TR-06-02, 2006.

Shultz E, Aliferis CF, Aronsky D. Selection and Interpretation of Laboratory Procedures. In: “Tietz Textbook of Clinical Chemistry and Molecular Diagnostics”, Carl A. Burtis, Edward R. Ashwood, David E. Bruns eds.. Elisvier Saunders, St Louis, MO, 2006. [Publisher]

2005

Sboner A, Aliferis CF. Modeling clinical judgment and implicit guideline compliance in the diagnosis of melanomas using machine learning. AMIA 2005 Annual Symposium Proceedings 2005;664-8. [Article]

Levy S, Statnikov A, Aliferis CF. Biomarker Selection from High-Dimensionality Data. Pharmaceutical Discovery. 2005 Microarray Supplement, September 2005: 37-44.

Hoot N, Feurer I, Pinson CW, Aliferis CF. Modelling liver transplant survival: Comparing techniques of deriving predictor sets. Journal of Gastrointestinal Surgery 2005;94:563.

Brown LE, Tsamardinos I, Aliferis CF. A comparison of novel and state-of-the-art polynomial Bayesian network learning algorithms. Proceedings of the Twentieth National Conference on Artificial Intelligence AAAI 2005. [Article]

Fu LD, Tsamardinos I. A Comparison of Bayesian Network Learning Algorithms from Continuous Data. AMIA 2005 Annual Symposium Proceedings, 2005. [Article]

Aphinyanaphongs Y, Tsamardinos I, Statnikov A, Hardin D, Aliferis CF. Text categorization models for high-quality article retrieval in internal medicine. J Am Med Inform Assoc 2005 Mar;122:207-16. [Pubmed] [Supplement]

Duda S, Aliferis CF, Miller R, Statnikov A, Johnson K. Extracting drug-drug interaction articles from MEDLINE to improve the content of drug databases. AMIA 2005 Annual Symposium Proceedings 2005;216-20. [Article]

Cooper GF, Abraham V, Aliferis CF, Aronis JM, Buchanan BG, Caruana R, Fine MJ, Janosky JE, Livingston G, Monti S, Mitchell T, Spirtes P. Predicting dire outcomes of patients with community acquired pneumonia. J Biomed Inform 2005 Oct;385:347-66. [Pubmed]

Statnikov A, Tsamardinos I, Aliferis CF. Using GEMS for Cancer Diagnosis and Biomarker Discovery from Microarray Gene Expression Data.. Proceedings of the 13th Annual International Conference on Intelligent Systems for Molecular Biology ISMB, 2005. [Software Demo Abstract]

Statnikov A, Tsamardinos I, Aliferis CF. Using the GEMS System for Supervised Analysis of Cancer Microarray Gene Expression Data. AMIA 2005 Annual Symposium Proceedings, 2005. [Software Demo Abstract]

Statnikov A, Tsamardinos I, Aliferis CF. Using the GEMS System for Cancer Diagnosis and Biomarker Discovery from Microarray Gene Expression Data. Proceedings of the 12th National Conference on Artificial Intelligence AAAI, 2005. [Software Demo Article]

Statnikov A, Tsamardinos I, Aliferis CF. Using GEMS for Cancer Diagnosis and Biomarker Discovery from Microarray Gene Expression Data.. Proceedings of the 13th Annual International Conference on Intelligent Systems for Molecular Biology ISMB, 2005. [Poster Abstract]

Statnikov A, Aliferis CF, Tsamardinos I, Hardin D, Levy S. A Comprehensive Evaluation of Multicategory Classification Methods for Microarray Gene Expression Cancer Diagnosis. Bioinformatics, 2005 Mar 1;215:631-43 . [Pubmed] [Appendix] [Supplement]

Statnikov A, Tsamardinos I, Dosbayev Y, Aliferis CF. GEMS: a system for automated cancer diagnosis and biomarker discovery from microarray gene expression data. Int J Med Inform 2005 Aug;747-8:491-503. [Pubmed] [Supplement]

Fananapazir N, Li M, Spentzos D, Aliferis CF. Formative evaluation of a prototype system for automated analysis of mass spectrometry data. AMIA 2005 Annual Symposium Proceedings 2005;241-5. [Article] [Supplement]

2004

Hardin D, Tsamardinos I, Aliferis CF. A Theoretical Characterization of Linear SVM-Based Feature Selection. Proceedings of the Twenty First International Conference on Machine Learning ICML, 2004. [Article] [Supplement]

Brown LE, Tsamardinos I, Aliferis CF. A novel algorithm for scalable and accurate Bayesian network learning. Medinfo 2004;11Pt 1:711-5. [Article] [Supplement]

Aphinyanaphongs Y, Aliferis CF. Learning Boolean queries for article quality filtering. Medinfo 2004;11Pt 1:263-7. [Article]

Pollack ME, Tsamardinos I. Efficiently Dispatching Plans Encoded as Simple Temporal Problems. In Intelligent Techniques for Planning, Idea Group Publishing, Editors: Ioannis Vlahavas and Dimitris Vrakas, 2004 . [Publisher]

Fu LD. Development of the Scientific Computing Center at Vanderbilt University. In Transforming Health Care Through Information Lorenzi NM, Ash JS, Einbinder J, McPhee W, Einbinder L eds., Springer, 2004. [Publisher]

Statnikov A, Aliferis CF, Tsamardinos I. Methods for multi-category cancer diagnosis from gene expression data: a comprehensive evaluation to inform decision support system development. Medinfo 2004;11Pt 2:813-7. [Article]

2003

Aliferis CF, Tsamardinos I, Massion PP, Statnikov A, Hardin D. Why classification models using array gene expression data perform so well: a preliminary investigation of explanatory factors. Proceedings of the 2003 International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences METMBS, 2003. [Article]

Aliferis CF, Tsamardinos I, Massion PP, Statnikov A, Fananapazir N, Hardin D. Machine learning models for classification of lung cancer and selection of genomic markers using array gene expression data. Proceedings of the Sixteenth International Florida Artificial Intelligence Research Society Conference FLAIRS, 2003. [Article] [Supplement]

Tsamardinos I, Aliferis CF, Statnikov A. Time and sample efficient discovery of Markov blankets and direct causal relations. Proceedings of the Ninth International Conference on Knowledge Discovery and Data Mining KDD 2003;673-8. [Article]

Tsamardinos I, Aliferis CF, Statnikov A. Algorithms for large scale Markov blanket discovery. Proceedings of the Sixteenth International Florida Artificial Intelligence Research Society Conference FLAIRS 2003;376-81. [Article]

Tsamardinos I, Aliferis CF, Statnikov A. Time and sample efficient discovery of Markov blankets and direct causal relations. Technical Report DSL 03-06, 2004. [Article]

Tsamardinos I, Aliferis CF. Towards principled feature selection: relevancy, filters and wrappers. Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics AI & Stats, 2003. [Article]

Frey L, Fisher D, Tsamardinos I, Aliferis CF, Statnikov A. Identifying Markov blankets with decision tree induction. Proceedings of the Third IEEE International Conference on Data Mining ICDM, 2003. [Article]

Aliferis CF, Tsamardinos I, Statnikov A. HITON: a novel Markov blanket algorithm for optimal variable selection. AMIA 2003 Annual Symposium Proceedings 2003;21-5. [Article]

Tsamardinos I, Aliferis CF, Statnikov A, Brown LE. Scaling-up Bayesian network learning to thousands of variables using local learning technique. Technical Report DSL 03-02, 2003. [Article]

Aphinyanaphongs Y, Aliferis CF. Text categorization models for retrieval of high quality articles in internal medicine. AMIA 2003 Annual Symposium Proceedings 2003;31-5. [Article]

Tsamardinos I, Vidal T, Pollack ME. CTP: A New Constraint-Based Formalism for Conditional, Temporal Planning. Special Issue of Constraints Journal on Planning, 84:365-388, 2003 . [Article]

Tsamardinos I, Pollack ME, Ramakrishnan S. Assessing the Probability of Legal Execution of Plans with Temporal Uncertainty. Proceedings of the International Conference on Automated Planning and Scheduling ICAPS, Workshop on Planning under Uncertainty and Incomplete Information, 2003. [Article]

Tsamardinos I, Pollack ME. Efficient Solution Techniques for Disjunctive Temporal Reasoning Problems. Artificial Intelligence, 1511-2:43-90, 2003 . [Article]

Pollack ME, Brown L, Colbry D, McCarthy CE, Orosz C, Peintner B, RamakrishnanS, Tsamardinos I. Autominder: An Intelligent Cognitive Orthotic System for People with Memory Impairment. Robotics and Autonomous Systems, 443-4:273-282, 2003 . [Article]

Statnikov A, Tsamardinos I, Aliferis CF. An Algorithm for Generation of Large Bayesian Networks. Technical Report DSL TR-03-01, 2003. [Article]

Aliferis CF, Tsamardinos I, Statnikov A, Brown LE. Causal Explorer: a causal probabilistic network learning toolkit for biomedical discovery. Proceedings of the 2003 International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences METMBS, 2003. [Article] [Supplement]

2002

Aliferis CF, Hardin D, Massion PP. Machine learning models for lung cancer classification using array comparative genomic hybridization. AMIA 2002 Annual Symposium Proceedings 2002;7-11. [Article]

Aliferis CF, Tsamardinos I, Statnikov A. Large-scale feature selection using Markov blanket induction for the prediction of protein-drug binding. Technical Report DSL 02-06, 2002. [Article]

Aliferis CF, Tsamardinos I. Algorithms for large-scale local causal discovery and feature selection in the presence of small sample or large causal neighborhoods. Technical Report DSL 02-08, 2002. [Article]

Tsamardinos I. A Probabilistic Approach to Robust Execution ofTemporal Plans with Uncertainty. Proceedings of the 2nd Greek National Conference on Artificial Intelligence, Thessaloniki, Greece, April 2002, p. 97-108 . [Article]

Pollack ME, McCarthy CE, Ramakrishnan S, Tsamardinos I, Brown L, Carrion S, Colbry D, Orosz C, Peintner B. Autominder: A Planning, Monitoring, and Reminding Assistive Agent. Proceedings of the 7th International Conference on Intelligent Autonomous Systems IAS, 2002 . [Article]

Pollack ME, McCarthy CE, Ramakrishnan S, Tsamardinos I. Execution Time Plan Management for a Cognitive Orthotic System. eds. M. Beetz and J. Hertzberg, Plan-Based Control of Robotic Agents, 2002. [Article]

Berfield A, Chrysanthis PK, Tsamardinos I, Pollack ME, Banerjee S. A Scheme for Integrating e-Services in Establishing Virtual Enterprises. Proceedings of the 12th International Workshop on Research Issues on Data Engineering RIDE-02, 2002. [Article]

2001

Kukafka R, O’Carrol PW, Gerberding JL, Shortliffe EH, Aliferis CF, Lumpkin JR, Yasnoff WA. Issues and Opportunities in Public Health Informatics: A Panel Discussion. J Public Health Manag Pract. 2001 Nov;76:31-42. [Pubmed]


Copyright © 2002-2007, Discovery Systems Laboratory, Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA