World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
43
Citations
12275
World Ranking
7793
National Ranking
3372

Overview

Mark Craven is affiliated with the University of Wisconsin-Madison in the United States and has contributed to research primarily within the fields of Biochemistry, Genetics and Molecular Biology, and Medicine. Their work spans a variety of interdisciplinary subfields including Molecular Biology, Genetics, Artificial Intelligence, Biomedical Engineering, and Physiology.

Craven's research addresses topics such as genetic and phenotypic traits in livestock, asthma and respiratory diseases, IL-33, ST2, and ILC pathways, explainable artificial intelligence (XAI), machine learning and data classification, SARS-CoV-2 detection and testing, as well as biosensors and analytical detection.

Several recent papers illustrate the scope of their scholarly contributions:

  • Defining mitochondrial protein functions through deep multiomic profiling (2022, Nature)
  • Expression quantitative trait locus fine mapping of the 17q12-21 asthma locus in African American children: a genetic association and gene expression study (2020, The Lancet Respiratory Medicine)
  • Deciphering the impact of genomic variation on function (2024, Nature)
  • Machine learning for syndromic surveillance using veterinary necropsy reports (2020, PLoS ONE)
  • New Insights Relating Gasdermin B to the Onset of Childhood Asthma (2022, American Journal of Respiratory Cell and Molecular Biology)

Frequent collaborators of Mark Craven include Leonard B. Bacharier, Tiago Luciano Passafaro, Fernando Brito Lopes, J.R.R. Dórea, and Vivian Breen, each having coauthored multiple publications with Craven.

Mark Craven's research has been published in journals with repeated appearances in Nature and Research Square, as well as in The Lancet Respiratory Medicine, PLoS ONE, and the American Journal of Respiratory Cell and Molecular Biology.

Best Publications

  • An Analysis of Active Learning Strategies for Sequence Labeling Tasks

    Burr Settles;Mark Craven

  • Learning to extract symbolic knowledge from the World Wide Web

    Mark Craven;Dan DiPasquo;Dayne Freitag;Andrew McCallum

  • Extracting Tree-Structured Representations of Trained Networks

    Mark Craven;Jude W. Shavlik

  • Constructing Biological Knowledge Bases by Extracting Information from Text Sources

    Mark Craven;Johan Kumlien

  • Learning to construct knowledge bases from the World Wide Web

    Mark Craven;Dan DiPasquo;Dayne Freitag;Andrew McCallum

  • Multiple-Instance Active Learning

    Burr Settles;Mark Craven;Soumya Ray

  • Incorporating domain knowledge into topic modeling via Dirichlet Forest priors

    David Andrzejewski;Xiaojin Zhu;Mark Craven

  • Using sampling and queries to extract rules from trained neural networks

    Mark Craven;Jude W. Shavlik

  • Using neural networks for data mining

    Mark W. Craven;Jude W. Shavlik

  • Extracting comprehensible models from trained neural networks

    Mark William Craven;Jude W. Shavlik

  • Supervised versus multiple instance learning: an empirical comparison

    Soumya Ray;Mark Craven

  • Identification of toxicologically predictive gene sets using cDNA microarrays

    Russell S. Thomas;David R. Rank;Sharron G. Penn;Gina M. Zastrow

  • Active Learning with Real Annotation Costs

    Burr Settles;Mark Craven;Lewis Friedland

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

    Tina Eliassi-Rad;Lyle Ungar;Mark Craven;Dimitrios Gunopulos

  • Hierarchical hidden Markov models for information extraction

    Marios Skounakis;Mark Craven;Soumya Ray

  • Representing sentence structure in hidden Markov models for information extraction

    Soumya Ray;Mark Craven

  • Relational learning with statistical predicate invention: better models for hypertext

    Mark Craven;Seán Slattery

  • A framework for incorporating general domain knowledge into latent Dirichlet allocation using first-order logic

    David Andrzejewski;Xiaojin Zhu;Mark Craven;Benjamin Recht

  • Learning symbolic rules using artificial neural networks

    Mark Craven;Jude W. Shavlik

  • A Bayesian network approach to operon prediction.

    Joseph Bockhorst;Mark W. Craven;David Page;Jude W. Shavlik

  • Combining Statistical and Relational Methods for Learning in Hypertext Domains

    Seán Slattery;Mark Craven

Frequent Co-Authors

Jude W. Shavlik
Jude W. Shavlik University of Wisconsin–Madison
Christopher A. Bradfield
Christopher A. Bradfield University of Wisconsin–Madison
Paul Ahlquist
Paul Ahlquist University of Wisconsin–Madison
Audrey P. Gasch
Audrey P. Gasch University of Wisconsin–Madison
Xiaojin Zhu
Xiaojin Zhu University of Wisconsin–Madison
Tom M. Mitchell
Tom M. Mitchell Carnegie Mellon University
Andreas Vlachos
Andreas Vlachos University of Cambridge
Diane R. Gold
Diane R. Gold Harvard University
Carole Ober
Carole Ober University of Chicago

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