Mark W. Craven

发表

Learning to Represent Codons: A Challenge Problem for Constructive Induction

Jude W. Shavlik, Mark W. Craven, 1993, IJCAI.

A Probabilistic Learning Approach to Whole-Genome Operon Prediction

David Page, Jude W. Shavlik, Mark W. Craven, 2000, ISMB.

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

David Page, Jude W. Shavlik, Mark W. Craven, 2000, ICML.

Learning Symbolic Rules Using Artificial Neural Networks

Jude W. Shavlik, Mark W. Craven, J. Shavlik, 1993, ICML.

A Bayesian Network Approach to Operon Prediction

David Page, Jude W. Shavlik, Mark W. Craven, 2003, Bioinform..

Constructive Induction in Knowledge-Based Neural Networks

Jude W. Shavlik, Mark W. Craven, Geoffrey G. Towell, 1991, ML.

Using Sampling and Queries to Extract Rules from Trained Neural Networks

Jude W. Shavlik, Mark W. Craven, J. Shavlik, 1994, ICML.

in Advances in Neural Information Processing

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

Computational Phenotyping on Diverse Data Sources

Jimeng Sun, Joydeep Ghosh, Bradley Malin, 2017, American Medical Informatics Association Annual Symposium.

Machine learning approaches to gene recognition

Jude W. Shavlik, Mark W. Craven, J. Shavlik, 1994, IEEE Expert.

Visualizing Learning and Computation in Artificial Neural Networks

Jude W. Shavlik, Mark W. Craven, J. Shavlik, 1992, Int. J. Artif. Intell. Tools.

Using neural networks for data mining

Jude W. Shavlik, Mark W. Craven, J. Shavlik, 1997, Future Gener. Comput. Syst..

Understanding Time-Series Networks: A Case Study in Rule Extraction

Jude W. Shavlik, Mark W. Craven, J. Shavlik, 1997, Int. J. Neural Syst..

Learning to Extract Relations from MEDLINE

Mark Craven, Mark W. Craven, 1999 .

Sirt3 Substrate Specificity Determined by Peptide Arrays and Machine Learning

Mark W. Craven, Brian C. Smith, B. Settles, 2022 .

Identification of toxicologically predictive gene sets using cDNA microarrays.

Mark W. Craven, R. S. Thomas, D. Rank, 2001, Molecular pharmacology.

Extracting Thee-Structured Representations of Thained Networks

Jude W. Shavlik, Mark W. Craven, J. Shavlik, 1995 .

Defining mitochondrial protein functions through deep multiomic profiling

Mark W. Craven, Dain R. Brademan, Nicholas W. Kwiecien, 2022, Nature.

Multiple postoperative complications: Making sense of the trajectories.

Mark W. Craven, Alexander G. Cobian, Shara I. Feld, 2016, Surgery.

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

Mark W. Craven, G. Rosa, T. Passafaro, 2020 .

Learning to Extract Relations from MEDLINE

Mark W. Craven, 1999 .

New Insights Relating Gasdermin B to the Onset of Childhood Asthma.

Mark W. Craven, C. Ober, J. Gern, 2022, American journal of respiratory cell and molecular biology.

Mapping Murine Corneal Neovascularization and Weight Loss Virulence Determinants in the Herpes Simplex Virus 1 Genome and the Detection of an Epistatic Interaction between the UL and IRS/US Regions

Mark W. Craven, Kyubin Lee, Mark Craven, 2016, Journal of Virology.

Quantitative Trait Locus Based Virulence Determinant Mapping of the HSV-1 Genome in Murine Ocular Infection: Genes Involved in Viral Regulatory and Innate Immune Networks Contribute to Virulence

Mark W. Craven, Kyubin Lee, Mark Craven, 2016, PLoS pathogens.

Using neural networks to automatically refine expert system knowledge bases: experiments in the NYNEX MAX domain

Mark W. Craven, Jude Shavlik, D. W. Opitz, 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

Augmenting subnetwork inference with information extracted from the scientific literature

Mark W. Craven, Deborah Chasman, Hoifung Poon, 2019, PLoS Comput. Biol..

Implications of Multiple Complications on the Postoperative Recovery of General Surgery Patients

Mark W. Craven, G. Kennedy, S. Tevis, 2016, Annals of surgery.

Quantifying technical skills during open operations using video-based motion analysis.

Mark W. Craven, Carla M Pugh, Robert G Radwin, 2014, Surgery.

Recombination Analysis of Herpes Simplex Virus 1 Reveals a Bias toward GC Content and the Inverted Repeat Regions

Mark W. Craven, C. Brandt, Yuriy Sverchkov, 2015, Journal of Virology.

Big Data in Healthcare: Opportunities and Challenges

Mark W. Craven, C. D. Page, Mark Craven, 2015, Big Data.

Extracting Tree-Structured Representations of Trained Networks

Mark W. Craven, J. Shavlik, Jude W. Shavlik, 1995, NIPS.

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.