M. Craven

发表

Using Multiple Levels of Learning and Diverse Evidence Sources to Uncover Coordinately Controlled Genes

J. Shavlik, J. Glasner, M. Craven, 2000 .

Learning Sparse Perceptrons

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

Learning to Extract Symbolic Knowledge from the World Wide Web

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

Evidence combination in biomedical natural-language processing

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

Inferring Host Gene Subnetworks Involved in Viral Replication

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

An Analysis of Active Learning Strategies for Sequence Labeling Tasks

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

Multiple-Instance Active Learning

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

Hierarchical Hidden Markov Models for Information Extraction

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

Pairwise learning of multilabel classifications with perceptrons

M. Craven, L. Ungar, 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

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..

Pathway connectivity and signaling coordination in the yeast stress-activated signaling network

David B. Berry, M. Craven, A. Gasch, 2014, Molecular systems biology.

Learning from data with complex interactions and ambiguous labels

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

Statistical Timeline Analysis for Electronic Health Records Table of Contents

Jeremy C. Weiss, M. Craven, C. McCarty, 2022 .

Would large dataset sample size unveil the potential of deep neural networks for improved genome-enabled prediction of complex traits? The case for body weight in broilers

M. Craven, G. Rosa, T. Passafaro, 2020, BMC genomics.

Contributors to the Challenge Task

J. Shavlik, M. Craven, Junichi Tsujii, 2022 .

Understanding Human Distress: Moving beyond the Concept of ‘Psychopathology’

M. Craven, A. Coyle, M. Milton, 2010 .

Explaining Effects of Host Gene Knockouts on Brome Mosaic Virus Replication

Mark Craven, Deborah Chasman, Brandi Gancarz, 2009 .

Machine learning for syndromic surveillance using veterinary necropsy reports

M. Craven, N. Bollig, L. Clarke, 2020, PloS one.

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

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

DOUBLE HELIX

M. Craven, 2020, Faculty Brat.

Noninvasive Detection of Colorectal Carcinomas Using Serum Protein Biomarkers.

M. Craven, P. Pickhardt, M. Sussman, 2020, The Journal of surgical research.

Improving diagnostic recognition of primary hyperparathyroidism with machine learning

David F. Schneider, M. Craven, C. Greenberg, 2017, Surgery.

Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA, USA, August 20-23, 2006

Dimitrios Gunopulos, Tina Eliassi-Rad, Lyle H. Ungar, 2006, KDD.

Would large dataset sample size unveil the potential of deep neural networks for improved genome-enabled prediction of complex traits? The case for body weight in broilers

M. Craven, G. Rosa, T. Passafaro, 2020, BMC genomics.

BIOINFORMATICS Clustered alignments of gene-expression time series data

M. Craven, C. Bradfield, A. Vollrath, 2009 .

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

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

Learning expressive computational models of gene regulatory sequences and responses

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

Curious machines: active learning with structured instances

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

Combining Statistical and Relational Methods for Learning in Hypertext Domains

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

Active Learning with Real Annotation Costs

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

Constructing Biological Knowledge Bases by Extracting Information from Text Sources

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

Context-Specific Nested Effects Models.

Yuriy Sverchkov, Mark Craven, Yi-Hsuan Ho, 2020, Journal of computational biology : a journal of computational molecular cell biology.

Context-Specific Nested Effects Models

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

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

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

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..

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

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

Nitric oxide‐releasing polyacrylonitrile disperses biofilms formed by wound‐relevant pathogenic bacteria

M. Craven, N. Cady, J. Hrabie, 2016, Journal of applied microbiology.

Machine learning methods for the discovery of regulatory elements in bacteria

Mark Craven, Joseph Bockhorst, Joseph Bockhorst, 2005 .

Refining the Structure of a Stochastic Context-Free Grammar

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

Extracting comprehensible models from trained neural networks

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

Markov Networks for Detecting Overalpping Elements in Sequence Data

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

Classification and alignment of gene-expression time-series data

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

Documented Lifestyle Education Among Young Adults with Incident Hypertension

Eneida A. Mendonça, Maureen A. Smith, M. Craven, 2015, Journal of General Internal Medicine.

Learning to Predict Post-Hospitalization VTE Risk from EHR Data

Peggy L. Peissig, Emily Kawaler, Alexander G. Cobian, 2012, AMIA.

Incorporating domain knowledge into topic modeling via Dirichlet Forest priors

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

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

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

Identifying patterns and predictors of lifestyle modification in electronic health record documentation using statistical and machine learning methods.

Eneida A. Mendonça, M. Craven, E. Mendonça, 2020, Preventive medicine.

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

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

Connecting quantitative regulatory-network models to the genome

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

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.

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 .

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

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

ISMB 2010 Organization

Roger L. Chang, S. Batzoglou, L. Cowen, 2010, Bioinformatics.

Relational Learning with Statistical Predicate Invention: Better Models for Hypertext

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

Using Active Learning for Speeding up Calibration in Simulation Models

Oguzhan Alagoz, Mucahit Cevik, Natasha K Stout, 2016, Medical decision making : an international journal of the Society for Medical Decision Making.

Incorporating domain knowledge in latent topic models

Xiaojin Zhu, Mark Craven, David Andrzejewski, 2010 .

Representing Sentence Structure in Hidden Markov Models for Information Extraction

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

Predicting bacterial transcription units using sequence and expression data

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

Search-based Structured Prediction applied to Biomedical Event Extraction

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

Supervised versus multiple instance learning: an empirical comparison

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

Modeling the Temporal Evolution of Postoperative Complications

Alexander G. Cobian, Mark W. Craven, Gregory D. Kennedy, 2016, AMIA.

Feature Importance Explanations for Temporal Black-Box Models

M. Craven, Akshay Sood, 2021, AAAI.

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..

Multiple alignment of rearranged genomes

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

Exploiting Relations Among Concepts to Acquire Weakly Labeled Training Data

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

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

Learning Hidden Markov Models for Regression using Path Aggregation

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

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

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

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

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

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

Dimitrios Gunopulos, Tina Eliassi-Rad, Lyle H. Ungar, 2006, KDD 2006.

Clustered alignments of gene-expression time series data

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

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

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

Learning Regulatory Network Models that Represent Regulator States and Roles

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

Using Active Learning for Speeding up Calibration in Simulation Models

M. Craven, A. Trentham-Dietz, O. Alagoz, 2016, Medical decision making : an international journal of the Society for Medical Decision Making.

Biological applications of multi-relational data mining

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

Breast Cancer Risk Prediction Using Electronic Health Records

Jun Fan, Jie Yin, David Page, 2017, 2017 IEEE International Conference on Healthcare Informatics (ICHI).