Supervised learning approaches in pathway modeling

S. Tagore, V. S. Gomase, K. V. Kale

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

These are machine learning approaches that are used for creating a function from training data and where both the inputs and outputs can be analyzed and observed. Supervised learner predicts the value of the function for any valid input object after having seen a number of training examples For solving a problem it can perform various steps, such as determining the type of training examples; gathering a training set; determining the input feature representation of the learned function; determining the structure of the learned function and corresponding learning algorithm; completing the design. The output of the function can be a continuous value or can predict a class label of the input object. These techniques have been quite widely used in the filed of bioinformatics. We have made a review for the application of some of the supervised learning strategies in biological pathway modeling.

Original languageEnglish
Title of host publicationPathway Modeling and Algorithm Research
PublisherNova Science Publishers, Inc.
Pages11-44
Number of pages34
ISBN (Print)9781611227574
StatePublished - 2011
Externally publishedYes

Keywords

  • Fasciculation
  • Neural network
  • Neurotrophic
  • Regression
  • Tolerance

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