Identifying the interacting positions of a protein using Boolean learning and support vector machines

DOI10.1016/J.COMPBIOLCHEM.2006.04.001zbMath1098.92025OpenAlexW2088841047WikidataQ33251421 ScholiaQ33251421MaRDI QIDQ849527

Matthew J. Realff, Andreas S. Bommarius, Anshul Dubey, Lee, Jay H.

Publication date: 31 October 2006

Published in: Computational Biology and Chemistry (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.compbiolchem.2006.04.001

zbMATH Keywords

support vector machinesBoolean functionrecombinationsdirected evolutioninteracting positionOCAT

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Biochemistry, molecular biology (92C40) Biophysics (92C05)

Uses Software

• AdaBoost.MH
• bootstrap

Cites Work

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• A greedy randomized adaptive search procedure (GRASP) for inferring logical clauses from examples in polynomial time and some extensions
• Inference of a minimum size Boolean function from examples by using a new efficient branch-and-bound approach
• A decision-theoretic generalization of on-line learning and an application to boosting
• Boosting the margin: a new explanation for the effectiveness of voting methods
• An approach to guided learning of Boolean functions
• An incremental learning algorithm for constructing Boolean functions from positive and negative examples
• Support vector machines for learning to identify the critical positions of a protein
• Better Bootstrap Confidence Intervals

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