@incollection{noto.recombws04
,author = "K. Noto and M. Craven"
,title = "Learning Regulatory Network Models that Represent Regulator States and Roles"
,booktitle = "Regulatory Genomics: {RECOMB} 2004 International Workshop"
,year = 2004
,editors = "E. Eskin and C. Workman"
,publisher = "Springer-Verlag"
,address = "New York, NY"
}
@article{noto.eccb06
,author = "K. Noto and M. Craven"
,title = "Learning Probabilistic Models of cis-Regulatory Modules that Represent Logical and Spatial Aspects"
,journal = "Bioinformatics"
,volume = 23
,number = 2
,pages = "e156--e162"
,year = 2006
}
@article{noto.bmcb06
,author = "K. Noto and M. Craven"
,title = "A Specialized Learner for Inferring Structured cis-Regulatory Modules"
,journal = "{BMC} Bioinformatics"
,volume = "7"
,pages = "528"
,year = "2006"
}
@phdthesis{noto.thesis
,author = "K. Noto"
,title = "Learning Expressive Computational Models of Gene Regulatory Sequences and Responses"
,school = "Department of Computer Sciences, University of Wisconsin"
,year = "2007"
,address = "Madison, WI"
}
@inproceedings{noto.uai08
,title = "Learning Hidden {M}arkov Models for Regression Using Path Aggregation"
,author = "K. Noto and M. Craven"
,booktitle = "Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence"
,editor = "D. McAllester and P. Myllym\"{\a}ki"
,pages = "444--451"
,year = "2008"
}
@inproceedings{elkan.kdd08
,title = "Learning Classifiers from Only Positive and Unlabeled Data"
,author = "C. Elkan and K. Noto"
,booktitle = "Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2008)"
,pages = "213--220"
,year = "2008"
}
@incollection{noto.ajcai08
,title = "Learning to Find Relevant Biological Articles Without Negative Training Examples"
,author = "K. Noto and Saier, Jr., M. H. and C. Elkan"
,booktitle = "AI 2008: Advances in Artificial Intelligence"
,editor = "W. R. Wobcke and M. Zhang"
,pages = "202--213"
,year = "2008"
,publisher = "Springer"
,series = "Lecture Notes in Computer Science"
,volume = "5360"
}
@article{saier.nar09
,title="The Transporter Classification Database: Recent Advances"
,author="Saier, Jr., M. H. and M.R. Yen and K. Noto and D. G. Tamang and C. Elkan"
,journal=nar
,year=2009
,month="January"
,volume="37"
,number="Database issue"
,pages="D274--D278"
,PMID=19022853
,abstract="The Transporter Classification Database ({TCDB}), freely accessible at http://www.tcdb.org, is a relational database containing sequence, structural, functional and evolutionary information about transport systems from a variety of living organisms, based on the International Union of Biochemistry and Molecular Biology-approved transporter classification (TC) system. It is a curated repository for factual information compiled largely from published references. It uses a functional/phylogenetic system of classification, and currently encompasses about 5000 representative transporters and putative transporters in more than 500 families. We here describe novel software designed to support and extend the usefulness of {TCDB}. Our recent efforts render it more user friendly, incorporate machine learning to input novel data in a semiautomatic fashion, and allow analyses that are more accurate and less time consuming. The availability of these tools has resulted in recognition of distant phylogenetic relationships and tremendous expansion of the information available to {TCDB} users."
}
@article{sehgal.tcbb09
,author="A.K. Sehgal and S. Das and K. Noto and M. Saier and C. Elkan"
,title="Identifying Relevant Data for a Biological Database: Handcrafted Rules Versus Machine Learning"
,journal ="{IEEE/ACM} Transactions on Computational Biology and Bioinformatics"
,volume=99
,year=2009
,abstract="With well over one thousand specialized biological databases in use today, the task of automatically identifying novel, relevant data for such databases is increasingly important. In this paper, we describe practical machine learning approaches for identifying MEDLINE documents and Swiss-Prot/TrEMBL protein records, for incorporation into a specialized biological database of transport proteins named TCDB. We show that both learning approaches outperform rules created by hand by a human expert. As one of the first case studies involving two different approaches to updating a deployed database, both the methods compared and the results will be of interest to curators of many specialized databases."
,issn="1545--5963"
,doi = {http://doi.ieeecomputersociety.org/10.1109/TCBB.2009.83}
,publisher ="{IEEE} Computer Society"
,address = {Los Alamitos, CA, USA}
}
@article{noto.icdm10
,author="K. Noto and C. Brodley and D. Slonim"
,title="Anomaly Detection Using an Ensemble of Feature Models"
,journal="Proceedings of the 10th IEEE International Conference on Data Mining (ICDM 2010)"
,publisher="IEEE Computer Society Press"
,year=2010
}
@article{noto.dami11
,author="K. Noto and C. Brodley and D. Slonim"
,title="{FRaC}: A Feature-Modeling Approach for Semi-Supervised and Unsupervised Anomaly Detection"
,journal="Data Mining and Knowledge Discovery"
,year=2011
,volume=25
,issue=1
,pages="109--133"
,url="http://bcb.cs.tufts.edu/frac"
}
%TODO:page numbers forthcoming
@article{krallinger.bmcb11
,author="M. Krallinger and M. Vazquez and F. Leitner and D. Salgado and A. Chatraryamontri and A. Winter and L. Perfetto and L. Briganti and L. Licata and M. Iannuccelli and L. Castagnoli and G. Cesareni4, and M. Tyers and G. Schneider and F. Rinaldi and R. Leaman and G. Gonzalez and S. Matos and S. Kim and W. John Wilbur and L. Rocha and H. Shatkay and A. V Tendulkar and S. Agarwal and F. Liu and X. Wang and R. Rak and K. Noto and C. Elkan and Z. Lu and R. I. Dogan and J.-F. Fontaine and M. A. Andrade-Navarro and A. Valencia"
,title="The Protein-Protein Interaction tasks of {BioCreative} {III}: Classification/Ranking of Articles and Linking Bio-ontology Concepts to Full Text."
,journal="BMC Bioinformatics"
,volume=12
,number=8
,year=2011
}