Mark Craven

发表

Fast multisegment alignments for temporal expression profiles.

Adam A Smith, Mark Craven, 2008, Computational systems bioinformatics. Computational Systems Bioinformatics Conference.

Inferring Host Gene Subnetworks Involved in Viral Replication

Mark Craven, Deborah Chasman, Brandi Gancarz, 2014, PLoS Comput. Biol..

Learning to Extract Symbolic Knowledge from the World Wide Web

Tom M. Mitchell, Andrew McCallum, Dayne Freitag, 1998, AAAI/IAAI.

Proceedings of the 3rd Brazilian symposium on Bioinformatics: Advances in Bioinformatics and Computational Biology

Ana L. C. Bazzan, Mark Craven, Natália F. Martins, 2008 .

Machine learning methods for the discovery of regulatory elements in bacteria

Mark Craven, Joseph Bockhorst, Joseph Bockhorst, 2005 .

Learning to Extract Relations from MEDLINE

Mark Craven, Mark W. Craven, 1999 .

Detecting Speculative Language Using Syntactic Dependencies and Logistic Regression

Andreas Vlachos, Mark Craven, 2010, CoNLL Shared Task.

Extracting comprehensible models from trained neural networks

Mark Craven, Jude Shavlik, J. Shavlik, 1996 .

Fundamental Concepts in Biomedical Text Analysis

Hagit Shatkay, Mark Craven, 2012 .

CONALD Report on the Workshop on Learning from Text and the Web

Yiming Yang, Jaime G. Carbonell, Tom M. Mitchell, 1998 .

Mining the Biomedical Literature

Hagit Shatkay, Mark Craven, 2012 .

Predicting Protein Folding Classes without Overly Relying on Homology

Edward C. Uberbacher, Mark Craven, Richard J. Mural, 1995, ISMB.

Connecting quantitative regulatory-network models to the genome

Mark Craven, Joseph Bockhorst, Yue Pan, 2007, ISMB/ECCB.

Biomedical event extraction from abstracts and full papers using search-based structured prediction

Andreas Vlachos, Mark Craven, M. Craven, 2011, BMC Bioinformatics.

Learning Hidden Markov Models for Regression using Path Aggregation

Mark Craven, Keith Noto, M. Craven, 2008, UAI.

Predicting bacterial transcription units using sequence and expression data

Yu Qiu, Mark Craven, Mingzhu Liu, 2003, ISMB.

Supervised versus multiple instance learning: an empirical comparison

Mark Craven, Soumya Ray, M. Craven, 2005, ICML.

Relational Learning with Statistical Predicate Invention: Better Models for Hypertext

Mark Craven, Seán Slattery, M. Craven, 2001, Machine Learning.

Advances in Bioinformatics and Computational Biology, Third Brazilian Symposium on Bioinformatics, BSB 2008, Santo André, Brazil, August 28-30, 2008. Proceedings

Ana L. C. Bazzan, Mark Craven, Natália F. Martins, 2008, BSB.

Report on the CONALD Workshop on Learning from Text and the Web

Jaime G. Carbonell, Mark Craven, Steve Fienberg, 1998 .

Exploiting Zone Information, Syntactic Rules, and Informative Terms in Gene Ontology Annotation of Biomedical Documents

Mark Craven, Burr Settles, 2004, TREC.

Learning from data with complex interactions and ambiguous labels

Mark Craven, Soumya Ray, David C. Page, 2005 .

Search-based Structured Prediction applied to Biomedical Event Extraction

Andreas Vlachos, Mark Craven, M. Craven, 2011, CoNLL.

Tab: Street 2020. 2º Premio The urban Lobby

Mark Craven, Kenneth Li, Fraser Moor, 2012 .

The genomics of a signaling pathway: a KDD Cup challenge task

Mark Craven, 2002, SKDD.

Evidence combination in biomedical natural-language processing

Mark Craven, Marios Skounakis, M. Craven, 2003, BIOKDD.

Biological applications of multi-relational data mining

David Page, Mark Craven, M. Craven, 2003, SKDD.

Learning Sparse Perceptrons

Mark Craven, Jeffrey C. Jackson, M. Craven, 1995, NIPS.

First-Order Learning for Web Mining

Mark Craven, Kamal Nigam, Seán Slattery, 1998, ECML.

EDGE: A Centralized Resource for the Comparison, Analysis, and Distribution of Toxicogenomic Information

Mark Craven, Russell S Thomas, Kevin R Hayes, 2005, Molecular Pharmacology.

Limited Agreement of Independent RNAi Screens for Virus-Required Host Genes Owes More to False-Negative than False-Positive Factors

Mark Craven, Linhui Hao, Paul Ahlquist, 2013, PLoS Comput. Biol..

Multiple-Instance Active Learning

Mark Craven, Burr Settles, Soumya Ray, 2007, NIPS.

Exploiting Relations Among Concepts to Acquire Weakly Labeled Training Data

Mark Craven, Joseph Bockhorst, Joseph Bockhorst, 2002, International Conference on Machine Learning.

Classification and alignment of gene-expression time-series data

Mark Craven, Adam A. Smith, M. Craven, 2009 .

atSNP Search: a web resource for statistically evaluating influence of human genetic variation on transcription factor binding

Mark Craven, Sunduz Keles, Christopher Harrison, 2018, Bioinform..

Information extraction from Medline

Mark Craven, 1998, SIGB.

Understanding Learned Models by Identifying Important Features at the Right Resolution pdf

Mark Craven, Kyubin Lee, Akshay Sood, 2018, AAAI.

Incorporating domain knowledge into topic modeling via Dirichlet Forest priors

Xiaojin Zhu, Mark Craven, David Andrzejewski, 2009, ICML '09.

Representing Sentence Structure in Hidden Markov Models for Information Extraction

Mark Craven, Soumya Ray, M. Craven, 2001, IJCAI.

Mark Craven, Adam A. Smith, Aaron Vollrath, 2008, PLoS Computational Biology.

Learning probabilistic models of cis-regulatory modules that represent logical and spatial aspects

Mark Craven, Keith Noto, M. Craven, 2007, Bioinform..

Clustered alignments of gene-expression time series data

Mark Craven, Adam A. Smith, Christopher A. Bradfield, 2009, Bioinform..

Hierarchical Hidden Markov Models for Information Extraction

Mark Craven, Soumya Ray, Marios Skounakis, 2003, IJCAI.

Markov Networks for Detecting Overalpping Elements in Sequence Data

Mark Craven, Joseph Bockhorst, Joseph Bockhorst, 2004, NIPS.

Refining the Structure of a Stochastic Context-Free Grammar

Mark Craven, Joseph Bockhorst, Joseph Bockhorst, 2001, IJCAI.

Putting It All Together: Current Applications and Future Directions

Hagit Shatkay, Mark Craven, 2012 .

Learning Expressive Models of Gene Regulation

Mark Craven, 2008, ILP.

Relational Learning

Mark Craven, Jude Shavlik, 2014, Encyclopedia of Social Network Analysis and Mining.

Learning Regulatory Network Models that Represent Regulator States and Roles

Mark Craven, Keith Noto, M. Craven, 2004, Regulatory Genomics.

Classifying Biomedical Articles by Making Localized Decisions

Mark Craven, Burr Settles, Thomas Brow, 2005, TREC.

Learning expressive computational models of gene regulatory sequences and responses

Mark Craven, Keith Noto, M. Craven, 2007 .

Multiple alignment of rearranged genomes

Mark Craven, Aaron E. Darling, Nicole T. Perna, 2004, Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference, 2004. CSB 2004..

Learning Statistical Models for Annotating Proteins with Function Information using Biomedical Text

Mark Craven, Soumya Ray, M. Craven, 2005, BMC Bioinformatics.

A specialized learner for inferring structured cis-regulatory modules

Mark Craven, Keith Noto, Keith Noto, 2006, BMC Bioinformatics.

Using Statistical and Relational Methods to Characterize Hyperlink Paths

Mark Craven, 1998 .

Curious machines: active learning with structured instances

Mark Craven, Burr Settles, M. Craven, 2008 .

Context-Specific Nested Effects Models

Mark Craven, Yuriy Sverchkov, Audrey P. Gasch, 2018, RECOMB.

Active Learning with Real Annotation Costs

Mark Craven, Burr Settles, Lewis A. Friedland, 2008 .

Ranking Biomedical Passages for Relevance and Diversity: University of Wisconsin, Madison at TREC Genomics 2006

Xiaojin Zhu, Mark Craven, Burr Settles, 2006, TREC.

Combining Statistical and Relational Methods for Learning in Hypertext Domains

Mark Craven, Seán Slattery, M. Craven, 1998, ILP.

Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence A Framework for Incorporating General Domain Knowledge into Latent Dirichlet Allocation Using First-Order Logic

Xiaojin Zhu, Mark Craven, Benjamin Recht, 2022 .

Learning to construct knowledge bases from the World Wide Web

Tom M. Mitchell, Andrew McCallum, Dayne Freitag, 2000, Artif. Intell..

Learning to predict reading frames in E. coli DNA sequences

Mark Craven, Mark W. Craven, Jude Shavlik, 1993, [1993] Proceedings of the Twenty-sixth Hawaii International Conference on System Sciences.

An Analysis of Active Learning Strategies for Sequence Labeling Tasks

Mark Craven, Burr Settles, M. Craven, 2008, EMNLP.

Incorporating domain knowledge in latent topic models

Xiaojin Zhu, Mark Craven, David Andrzejewski, 2010 .

A review of active learning approaches to experimental design for uncovering biological networks

Mark Craven, Yuriy Sverchkov, M. Craven, 2017, PLoS Comput. Biol..

Constructing Biological Knowledge Bases by Extracting Information from Text Sources

Mark Craven, Johan Kumlien, M. Craven, 1999, ISMB.

Mark Craven, Adam A. Smith, Christopher A. Bradfield, 2008, PLoS Comput. Biol..

EDGE3: A web-based solution for management and analysis of Agilent two color microarray experiments

Mark Craven, Adam A. Smith, Christopher A. Bradfield, 2009, BMC Bioinformatics.

Rule Extraction: Where Do We Go from Here?

Mark Craven, Jude Shavlik, J. Shavlik, 1999 .

Feature Importance Explanations for Temporal Black-Box Models pdf

Mark Craven, Akshay Sood, 2021, ArXiv.

Interaction Networks in Yeast Define and Enumerate the Signaling Steps of the Vertebrate Aryl Hydrocarbon Receptor

Mark Craven, Christopher A Bradfield, M. Craven, 2004, PLoS biology.

Exploiting Zone Information , Syntactic Features , and Informative Terms in Gene Ontology Annotation from Biomedical Documents

Mark Craven, Burr Settles, M. Craven, 2022 .

Explaining Effects of Host Gene Knockouts on Brome Mosaic Virus Replication

Mark Craven, Deborah Chasman, Brandi Gancarz, 2009 .

Machine learning is superior to traditional modeling: a parathyroid hormone calculator example

Mark Craven, Caprice C. Greenberg, David F. Schneider, 2014 .