Skip to content Skip to navigation

Connexions

Sections
You are here: Home » Content » Statistical machine learning for computational biology

Navigation

Content Actions
  • Download PDF/ZIP ...
    Download PDF/ZIP:
    • The PDF of the collection
    • A ZIP file containing all the multimedia files in the collection
    • Collection PDF
    • Collection multimedia ZIP (?)

      The multimedia ZIP file provides offline access to all the multimedia files that are not available in the printed version (PDF) of this collection.

      To access the files:

      1. download the ZIP
      2. extract all of its files to a location on your hard drive
      3. open the README file for instructions or go directly to mediafiles.html file in your Web browser
  • Add to ...
    Add the collection to:
    • My Favorites
    • A lens
    • An external social bookmarking service
    • My Favorites (What is 'My Favorites'?)
      'My Favorites' is a special kind of lens which you can use to bookmark modules and collections directly in Connexions. 'My Favorites' can only be seen by you, and collections saved in 'My Favorites' can remember the last module you were on. You need a Connexions account to use 'My Favorites'.
    • A lens (What is a lens?)

      Definition of a lens

      Lenses

      A lens is a custom view of Connexions content. You can think of it as a fancy kind of list that will let you see Connexions through the eyes of organizations and people you trust.

      What is in a lens?

      Lens makers point to Connexions materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

      Who can create a lens?

      Any individual Connexions member, a community, or a respected organization.

    • External bookmarks
  • E-mail the author

Table of Contents

Recently Viewed

This feature requires Javascript to be enabled.

Statistical machine learning for computational biology

Collection type: Course

Course by: Devika Subramanian

Start »

Collection Properties

Summary: The course is the second module of a three module course entitled "Bioinformatics: from sequence to structure". This course focuses on learning statistical models from biological data. Three problems are covered: gene finding, classification of gene expression data, and inferring regulatory networks from mRNA and proteomic data. The computational techniques covered include: HMMs, support vector machines, and structure learning with Bayesian networks. This course is made possible by a curriculum development grant from the NSF.

Instructor: Devika Subramanian

Institution: Rice University

Course Number: COMP 470

Course Web page

This collection contains: Modules by: Andrew Hughes, Devika Subramanian

Comments, questions, feedback, criticisms?

Send feedback