Applying machine learning techniques to DNA sequence analysis

PDF Version Also Available for Download.

Description

We are developing a machine learning system that modifies existing knowledge about specific types of biological sequences. It does this by considering sample members and nonmembers of the sequence motif being learned. Using this information (which we call a domain theory''), our learning algorithm produces a more accurate representation of the knowledge needed to categorize future sequences. Specifically, the KBANN algorithm maps inference rules, such as consensus sequences, into a neural (connectionist) network. Neural network training techniques then use the training examples of refine these inference rules. We have been applying this approach to several problems in DNA sequence analysis ... continued below

Physical Description

Pages: (3 p)

Creation Information

Shavlik, J.W. January 1, 1992.

Context

This report is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. More information about this report can be viewed below.

Who

People and organizations associated with either the creation of this report or its content.

Author

Sponsor

Publisher

Provided By

UNT Libraries Government Documents Department

Serving as both a federal and a state depository library, the UNT Libraries Government Documents Department maintains millions of items in a variety of formats. The department is a member of the FDLP Content Partnerships Program and an Affiliated Archive of the National Archives.

Contact Us

What

Descriptive information to help identify this report. Follow the links below to find similar items on the Digital Library.

Description

We are developing a machine learning system that modifies existing knowledge about specific types of biological sequences. It does this by considering sample members and nonmembers of the sequence motif being learned. Using this information (which we call a domain theory''), our learning algorithm produces a more accurate representation of the knowledge needed to categorize future sequences. Specifically, the KBANN algorithm maps inference rules, such as consensus sequences, into a neural (connectionist) network. Neural network training techniques then use the training examples of refine these inference rules. We have been applying this approach to several problems in DNA sequence analysis and have also been extending the capabilities of our learning system along several dimensions.

Physical Description

Pages: (3 p)

Notes

OSTI; NTIS; GPO Dep.

Language

Item Type

Identifier

Unique identifying numbers for this report in the Digital Library or other systems.

  • Other: DE92010626
  • Report No.: DOE/ER/61129-T1
  • Grant Number: FG02-91ER61129
  • DOI: 10.2172/5688406 | External Link
  • Office of Scientific & Technical Information Report Number: 5688406
  • Archival Resource Key: ark:/67531/metadc1092643

Collections

This report is part of the following collection of related materials.

Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

What responsibilities do I have when using this report?

When

Dates and time periods associated with this report.

Creation Date

  • January 1, 1992

Added to The UNT Digital Library

  • Feb. 10, 2018, 10:06 p.m.

Description Last Updated

  • Feb. 23, 2018, 11:57 a.m.

Usage Statistics

When was this report last used?

Yesterday: 0
Past 30 days: 2
Total Uses: 3

Interact With This Report

Here are some suggestions for what to do next.

Start Reading

PDF Version Also Available for Download.

International Image Interoperability Framework

IIF Logo

We support the IIIF Presentation API

Shavlik, J.W. Applying machine learning techniques to DNA sequence analysis, report, January 1, 1992; United States. (digital.library.unt.edu/ark:/67531/metadc1092643/: accessed June 19, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.