- J. Rensvold, E. Shishkova, I. Miller, Y. Sverchkov, A. Cetinkaya, A. Pyle, M. Manicki, D. Brademan, Y. Alanay, J. Raiman, A. Jochem, P. Hutchins, S. Peters, V. Linke, K. Overmyer, A. Hebert, C. Vincent, N. Kwiecien, M. Rush, M. Westphall, M. Craven, N. Akarsu, R. Taylor, J. Coon, D. Pagliarini (2022).
Defining Mitochondrial Protein Functions through Deep Multi-omic Profiling.
Nature 606(7913):382-388. - A. Sood, M. Craven (2022).
Feature Importance Explanations for Temporal Black-Box Models.
Proceedings of the 36th AAAI Conference on Artificial Intelligence. - N. Schoettler, E. Dissanayake, M. Craven, J. Yee, J. Eliason, E. Schauberger, R. Lemanske, C. Ober, J. Gern (2022).
New Insights Relating Gasdermin B to the Onset of Childhood Asthma.
American Journal of Respiratory Cell and Molecular Biology,67(4):430-437. - A. Cobian, M. Abbott, A. Sood, Y. Sverchkov, L. Hanrahan, T. Guilbert, and M. Craven (2020).
Modeling Asthma Exacerbations from Electronic Health Records.
Proceedings of the AMIA Joint Summits on Translational Science. - G. Pack, M. Craven and A. Acharya (2020).
A Secondary Analysis of Panoramic Radiographs Reveals Hotspots in the Maxillofacial Region Associated with Diabetes.
Proceedings of the AMIA Joint Summits on Translational Science. - N. Bollig, L. Clarke, E. Elsmo, and M. Craven (2020).
Machine learning for syndromic surveillance using veterinary necropsy reports.
PLoS ONE 15(2):e0228105. - S. Kiblawi, D. Chasman, A. Henning, E. Park, H. Poon, M. Gould, P. Ahlquist and M. Craven (2019).
Augmenting subnetwork inference with information extracted from the scientific literature.
PLoS Computational Biology 15(6):e1006758. - K. Lee, A. Sood and M. Craven (2019).
Understanding Learned Models by Identifying Important Features at the Right Resolution.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence. - J. Gern, D. Jackson, R. Lemanske, C. Seroogy, U. Tachinardi, M. Craven, et al. (2019).
The Children's Respiratory and Environmental Workgroup (CREW) Birth Cohort Consortium: Design, Methods, and Study Population.
Respiratory Research. - S. Shin, R. Hudson, C. Harrison, M. Craven, S. Keles (2018).
atSNP Search: A Web Resource for Statistically Evaluating Influence of Human Genetic Variation on Transcription Factor Binding.
Bioinformatics. - Y. Sverchkov, Y.-H. Ho, A. Gasch and M. Craven (2018).
Context-Specific Nested Effects Models.
Proceedings of the Annual Inernational Conference on Research in Computational Biology (RECOMB). - Y. Sverchkov and M. Craven (2017).
A Review of Active Learning Approaches to Experimental Design for Uncovering Biological Networks.
PloS Computational Biology 13(6):e1005466.Y. Somnay, M. Craven, K. McCoy, S. Carty, T. Wang, C. Greenberg, and D. Schneider (2017).
Improving diagnostic recognition of primary hyperparathyroidism with machine learning.
Surgery 161(4):1113-1121. - S. I. Feld, A. G. Cobian, S. E. Tevis, G. D. Kennedy and M. W. Craven (2016).
Modeling the Temporal Evolution of Postoperative Complications
Proceedings of the American Medical Informatics Association (AMIA) Annual Symposium. - K. Lee, A. Kolb, I. Larsen, M. Craven and C. Brandt (2016).
Mapping Murine Corneal Neovascularization and Weight Loss Virulence Determinants in the HSV-1 Genome and the Detection of an Epistatic Interaction between the UL and IRS/US Regions.
Journal of Virology 90(18):8115-31. - A. Kolb, K. Lee, I. Larsen, M. Craven and C. Brandt (2016).
Quantitative Trait Locus Based Virulence Determinant Mapping of the HSV-1 Genome in Murine Ocular Infection: Genes Involved in Viral Regulatory and Innate Immune Networks Contribute to Virulence.
PLoS Pathogens 12(3):e1005499. - S. E. Tevis, A. G. Cobian, H. P. Truong, M. W. Craven and G. D. Kennedy (2016).
Implications of Multiple Complications on the Postoperative Recovery of General Surgery Patients.
Annals of Surgery 263(6):1213-1218. - M. Cevik, M. A. Ergun, N. K. Stout, A. Trentham-Dietz, M. Craven and O. Alagoz (2016).
Using Active Learning for Speeding up Calibration in Simulation Models.
Medical Decision Making 36(5):581-593. - K. Lee, A. Kolb, Y. Sverchkov, J. Cuellar, M. Craven and C. Brandt (2015).
Recombination Analysis of Herpes Simplex Virus Type 1 Reveals a Bias towards GC Content and the Inverted Repeat Region.
Journal of Virology 89(14):7214-7223. - D. Chasman, Y.-H. Ho, D. Berry, C. Nemec, M. MacGilvray, A. Merrill, J. Hose, M. V. Lee, J. Will, J. Coon, A. Ansari, M. Craven and A. Gasch (2014).
Pathway Connectivity and Signaling Coordination in the Yeast Stress-Activated Signaling Network.
Molecular Systems Biology 10(11):759. - D. Chasman, B. Gancarz, L. Hao, M. Ferris, P. Ahlquist and M. Craven (2014).
Inferring Host Gene Subnetworks Involved in Viral Replication.
PLoS Computational Biology 10(5). - L. Hao, Q. He, Z. Wang, M. Craven, M. Newton and P. Ahlquist (2013).
Limited Agreement of Independent RNAi Screens for Virus-Required Host Genes Owes More to False-Negative than False-Positive Factors.
PLoS Computational Biology 9(9). - H. Shatkay and M. Craven (2012).
Mining the Biomedical Literature.
MIT Press. - E. Kawaler, A. Cobian, P. Peissig, D. Cross, S. Yale and M. Craven (2012).
Learning to Predict Post-Hospitalization VTE Risk from EHR Data.
Proceedings of the American Medical Informatics Association (AMIA) Annual Symposium. - A. Vlachos & M. Craven (2012).
Biomedical Event Extraction from Abstracts and Full Papers using Search-Based Structured Prediction.
BMC Bioinformatics 13(Suppl. 11):S5. - A. Vlachos & M. Craven (2011).
Search-based Structured Prediction Applied to Biomedical Event Extraction.
Proceedings of the 15th Conference on Computational Natural Language Learning (CoNLL-2011). - D. Andrzejewski, X. Zhu, M. Craven & B. Recht (2011).
A Framework for Incorporating General Domain Knowledge into Latent Dirichlet Allocation using First-Order Logic.
Proceedings of the 22nd International Joint Conference on Artificial Intelligence. - A. Kolb, M. Adams, E. Cabot, M. Craven & C. Brandt (2011).
Multiplex Sequencing of Several Ocular Herpes Simplex Virus Type-1 Genomes: Phylogeny, Sequence Variability, and SNP Distribution.
Investigative Ophthalmology and Visual Science 52(12). - B. Smith, B Settles, W. Hallows, M. Craven & J. Denu (2010).
SIRT3 Substrate Specificity Determined by Peptide Arrays and Machine Learning.
ACS Chemical Biology. - A. Vlachos & M. Craven (2010).
Detecting Speculative Language using Syntactic Dependencies and Logistic Regression.
Proceedings of the Fourteenth Conference on Computational Natural Language Learning (CoNLL-2010):Shared Task. - D. Andrzejewski, X. Zhu & M. Craven (2009).
Incorporating Domain Knowledge into Topic Modeling via Dirichlet Forest Priors.
Proceedings of the 26th International Conference on Machine Learning, pp. 25-32. - A. Smith, A. Vollrath, C. Bradfield & M. Craven (2009).
Clustered Alignments of Gene-Expression Time Series Data.
Bioinformatics 25:i119-i127. (special issue: Proceedings of the 17th ISMB and 8th ECCB Conferences) - B. Settles & M. Craven (2008).
An Analysis of Active Learning Strategies for Sequence Labeling Tasks.
Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1069-1078, ACL Press. - B. Settles, M. Craven & L. Friedland (2008).
Active Learning with Real Annotation Costs.
Proceedings of the NIPS Workshop on Cost-Sensitive Learning. - A. Smith, A. Vollrath, C. Bradfield & M. Craven (2008).
Similarity Queries for Temporal Toxicogenomic Expression Profiles.
PLoS Computational Biology 4(7). - A. Smith & M. Craven (2008).
Fast Multisegment Alignments for Temporal Expression Profiles.
Proceedings of the 7th International Conference on Computational Systems Bioinformatics, 315--326. Imperial College Press. - K. Noto & M. Craven (2008).
Learning Hidden Markov Models for Regression using Path Aggregation.
Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence . - B. Settles, S. Ray & M. Craven (2008).
Multiple-Instance Active Learning.
Advances in Neural Information Processing Systems (NIPS-20), MIT Press. - Y. Pan, T. Durfee, J. Bockhorst & M. Craven (2007).
Connecting Quantitative Regulatory-Network Models to the Genome.
Bioinformatics 23(13):i367-i376. (special issue: Proceedings of the 15th ISMB and 6th ECCB Conferences) - K. Noto & M. Craven (2007).
Learning Probabilistic Models of cis-Regulatory Modules that Represent Logical and Spatial Aspects.
Bioinformatics 23(2):e156-e162. (special issue: Proceedings of the 5th European Conference on Computational Biology) - A. Goldberg, D. Andrzejewski, J. Van Gael, B. Settles, X. Zhu & M. Craven (2007).
Ranking Biomedical Passages for Relevance and Diversity.
Proceedings of the Fifteenth Text Retrieval Conference (TREC 2006). - K. Noto & M. Craven (2006).
A Specialized Learner for Inferring Structured cis-Regulatory Modules.
BMC Bioinformatics, 7:528. - T. Brow, B. Settles & M. Craven (2006).
Classifying Biomedical Articles by Making Localized Decisions.
Proceedings of the Fourteenth Text Retrieval Conference (TREC 2005). - S. Ray & M. Craven (2005).
Supervised versus Multiple Instance Learning: An Empirical Comparison.
Proceedings of the 22nd International Conference on Machine Learning, 697-704. ACM Press. - J. Bockhorst & M. Craven (2005).
Markov Networks for Detecting Overlapping Elements in Sequence Data.
Advances in Neural Information Processing Systems (NIPS-17), 193-200. MIT Press. - S. Ray & M. Craven (2005).
Learning Statistical Models for Annotating Proteins with Function Information using Biomedical Text.
BMC Bioinformatics, 6(Suppl. 1):S18 - B. Settles & M. Craven (2005).
Exploiting Zone Information, Syntactic Features, and Informative Terms in Gene Ontology Annotation from Biomedical Documents.
Proceedings of the Thirteenth Text Retrieval Conference (TREC 2004). - K. Hayes, A. Vollrath, G. Zastrow, B. McMillan, M. Craven, S. Jovanovich,
J. Walisser, D. Rank, S. Penn, J. Reddy, R. Thomas & C. Bradfield (2005).
EDGE: A Centralized Resource for the Comparison, Analysis and Distribution of Toxicogenomic Information.
Molecular Pharmacology, 67(4):1360-1368. - K. Noto & M. Craven (2004).
Learning Regulatory Network Models that Represent Regulator States and Roles.
In E. Eskin and C. Workman (Editors) Regulatory Genomics: RECOMB 2004 International Workshop, 52-64. Springer-Verlag. - G. Yao, M. Craven, N. Drinkwater & C. Bradfield (2004).
Interaction Networks in Yeast Define and Enumerate the Signaling Steps of the Vertebrate Aryl Hydrocarbon Receptor.
PLoS Biology, 2(3):356-367. - M. Skounakis, M. Craven & S. Ray (2003).
Hierarchical Hidden Markov Models for Information Extraction.
Proceedings of the 18th International Joint Conference on Artificial Intelligence, 427-433. Morgan Kaufmann. - M. Skounakis & M. Craven (2003).
Evidence Combination in Biomedical Natural-Language Processing.
Proceedings of the 3rd Workshop on Data Mining in Bioinformatics, held in conjunction with KDD 2003. - J. Bockhorst, Y. Qiu, J. Glasner, M. Liu, F. Blattner & M. Craven (2003).
Predicting Bacterial Transcription Units using Sequence and Expression Data.
Bioinformatics, 19(Supplement):34-43.
(special issue: Proceedings of the 11th International Conference on Intelligent Systems for Molecular Biology) - J. Bockhorst, M. Craven, D. Page, J. Shavlik & J. Glasner (2003).
A Bayesian Network Approach to Operon Prediction.
Bioinformatics, 19(10):1227-1235. - D. Page & M. Craven (2003).
Biological Applications of Multi-Relational Data Mining.
SIGKDD Explorations, 5(1):69-79. - M. Craven (2003).
The Genomics of a Signaling Pathway: A KDD Cup Challenge Task.
SIGKDD Explorations, 4(2):97-98. - J. Bockhorst & M. Craven (2002).
Exploiting Relations Among Concepts to Acquire Weakly Labeled Training Data.
Proceedings of the 19th International Conference on Machine Learning, 43-50. Morgan Kaufmann. - R. Thomas, D. Rank, S. Penn, G. Zastrow, K. Hayes, K. Pande,
E. Glover, T. Silander, M. Craven, J. Reddy, S. Jovanovich
& C. Bradfield (2001).
Identification of Toxicologically Predictive Gene Sets Using cDNA Microarrays.
Molecular Pharmacology, 60:1189-1194. - J. Bockhorst & M. Craven (2001).
Refining the Structure of a Stochastic Context-Free Grammar.
Proceedings of the 17th International Joint Conference on Artificial Intelligence, 1315-1320. Morgan Kaufmann. - S. Ray & M. Craven (2001).
Representing Sentence Structure in Hidden Markov Models for Information Extraction.
Proceedings of the 17th International Joint Conference on Artificial Intelligence, 1273-1279. Morgan Kaufmann. - M. Craven & S. Slattery (2001).
Relational Learning with Statistical Predicate Invention: Better Models for Hypertext.
Machine Learning, 43(1-2): 97-119. - M. Craven, D. Page, J. Shavlik, J. Bockhorst &
J. Glasner (2000).
A Probabilistic Learning Approach to Whole-Genome Operon Prediction.
Proceedings of the 8th International Conference on Intelligent Systems for Molecular Biology, 116-127. AAAI Press. - M. Craven, D. Page, J. Shavlik, J. Bockhorst &
J. Glasner (2000).
Using Multiple Levels of Learning and Diverse Evidence Sources to Uncover Coordinately Controlled Genes.
Proceedings of the 17th International Conference on Machine Learning, 199-206. Morgan Kaufmann. - M. Craven, D. DiPasquo, D. Freitag, A. McCallum,
T. Mitchell, K. Nigam & S. Slattery (2000).
Learning to Construct Knowledge Bases from the World Wide Web.
Artificial Intelligence, 118(1-2): 69-113. - M. Craven & J. Kumlien (1999).
Constructing Biological Knowledge Bases by Extracting Information from Text Sources.
Proceedings of the 7th International Conference on Intelligent Systems for Molecular Biology, 77-86, AAAI Press. - M. Craven & J. Shavlik (1999).
Rule Extraction: Where Do We Go from Here?
University of Wisconsin Machine Learning Research Group Working Paper 99-1. - S. Slattery & M. Craven (1998).
Combining Statistical and Relational Methods for Learning in Hypertext Domains.
Proceedings of the 8th International Conference on Inductive Logic Programming, pp. 38-52. Springer Verlag. - M. Craven, D. DiPasquo, D. Freitag, A. McCallum,
T. Mitchell, K. Nigam & S. Slattery (1998).
Learning to Extract Symbolic Knowledge from the World Wide Web.
Proceedings of the 15th National Conference on Artificial Intelligence, pp. 509-516. AAAI Press. - M. Craven, S. Slattery & K. Nigam (1998).
First-Order Learning for Web Mining.
Proceedings of the 10th European Machine Learning Conference, 250-255. Springer Verlag. - M. Craven & J. Shavlik (1997).
Understanding Time-Series Networks: A Case Study in Rule Extraction.
International Journal of Neural Systems 8(4): 373-384. - M. Craven & J. Shavlik (1997).
Using Neural Networks for Data Mining.
Future Generation Computer Systems (Special Issue on Data Mining) 13:211-229. - M. Craven (1996).
Extracting Comprehensible Models from Trained Neural Networks.
PhD thesis, Department of Computer Sciences, University of Wisconsin-Madison.
(Also appears as UW Technical Report CS-TR-96-1326) - M. Craven & J. Shavlik (1995).
Extracting Tree-Structured Representations of Trained Networks.
Advances in Neural Information Processing Systems (NIPS-8), pp. 24-30. MIT Press. - J. Jackson & M. Craven (1995).
Learning Sparse Perceptrons.
Advances in Neural Information Processing Systems (NIPS-8), pp. 654-660. MIT Press. - M. Craven, R. Mural, L. Hauser & E. Uberbacher (1995).
Predicting Protein Folding Classes without Overly Relying on Homology.
Proceedings of the 3rd International Conference on Intelligent Systems for Molecular Biology, pp.98-106. AAAI Press. - M. Craven & J. Shavlik (1994).
Using Sampling and Queries to Extract Rules from Trained Neural Networks.
Proceedings of the 11th International Conference on Machine Learning, pp. 37-45. Morgan Kaufmann. - M. Craven & J. Shavlik (1993).
Learning to Represent Codons: A Challenge Problem for Constructive Induction.
Proceedings of the 13th International Joint Conference on Artificial Intelligence, pp. 1319-1324. Morgan Kaufmann. - M. Craven & J. Shavlik (1993).
Learning Symbolic Rules Using Artificial Neural Networks.
Proceedings of the 10th International Conference on Machine Learning, pp. 73-80. Morgan Kaufmann.