Keith Noto's Publications

K. Noto, C. E. Brodley, and D. Slonim.
FRaC: A Feature-Modeling Approach for Semi-Supervised and Unsupervised Anomaly Detection.
Data Mining and Knowledge Discovery 25(1):109-133, 2012.
(PDF, bibtex, Source code and Detailed Results)

M. Krallinger et al.
The Protein-Protein Interaction tasks of BioCreative III: Classification/Ranking of Articles and Linking Bio-ontology Concepts to Full Text.
BMC Bioinformatics 12:8, 2011.

K. Noto, C. E. Brodley, and D. Slonim.
Anomaly Detection Using an Ensemble of Feature Models.
Proceedings of the 10th IEEE International Conference on Data Mining (ICDM 2010),
Sydney, Australia, December 14-17, 2010, acceptance rate 19%.
IEEE Computer Society Press.
(PDF, bibtex, Source code and Detailed Results)

M. H. Saier, Jr., M. R. Yen, K. Noto, D. G. Tamang and C. Elkan
The Transporter Classification Database: Recent Advances.
Nucleic Acids Research 2009;37(Database issue):D274-D278.
(PDF, PubMed, bibtex, visit TCDB online)

A. K. Sehgal, S. Das, K. Noto, M. H. Saier, Jr. and C. Elkan.
Identifying Relevant Data for a Biological Database: Handcrafted Rules Versus Machine Learning.
In IEEE/ACM Transactions on Computational Biology and Bioinformatics 8(11), 2009.
(PDF, bibtex, online)

K. Noto, M. H. Saier, Jr. and C. Elkan
Learning to Find Relevant Biological Articles Without Negative Training Examples
Twenty-First Australasian Joint Conference on Artificial Intelligence,
Auckland, New Zealand, December 1-5, 2008, acceptance rate 29%.
In Lecture Notes in Bioinformatics 5360:202-213. Springer-Verlag.
(PDF, bibtex, Data sets)

C. Elkan and K. Noto
Learning Classifiers from Only Positive and Unlabeled Data.
Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2008), 213-220.
Las Vegas, United States of America, August 24-27, 2008, acceptance rate 18.6%.
(PDF, bibtex, data sets, poster)

K. Noto and M. Craven.
Learning Hidden Markov Models for Regression Using Path Aggregation.
Proceedings of the 24th Uncertainty in Artificial Intelligence Conference (UAI 2008), 444-451.
Helsinki, Finland, July 9-12, 2008. Acceptance rate 13%.
(PDF, bibtex, data sets, source code, talk slides)

K. Noto.
Learning Expressive Computational Models of Gene Regulatory Sequences and Responses
PhD thesis, Department of Computer Sciences, University of Wisconsin-Madison.
(PDF, bibtex)

K. Noto and M. Craven.
Learning Probabilistic Models of cis-Regulatory Modules that Represent Logical and Spatial Aspects.
Proceedings of the 2006 European Conference on Computational Biology,
Eilat, Israel, January 21-24, 2007, acceptance rate 18%.
In Bioinformatics 23(2):e156-162.
(PDF, PubMed, bibtex, source code)

K. Noto and M. Craven.
A Specialized Learner for Inferring Structured cis-Regulatory Modules.
BMC Bioinformatics 7:528, 2006
(PDF, PubMed, bibtex, source code)

K. Noto and M. Craven.
Learning Regulatory Network Models that Represent Regulator States and Roles.
RECOMB 2004 Workshop on Regulatory Genomics. San Diego, CA, March 2004.
In Lecture Notes in Bioinformatics 3318, 52-64. Springer-Verlag, 2004
(PDF, bibtex)