DNA sequence analysis using hierarchical ART-based classification networks

Technical Report · Sat Dec 31 04:00:00 EST 1994

OSTI ID:377151

LeBlanc, C; Hruska, S I ^[1]; Katholi, C R; Unnasch, T R ^[2]

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1. Florida State Univ., Tallahassee, FL (United States)
2. Univ. of Alabama, Birmingham, AL (United States)

Adaptive resonance theory (ART) describes a class of artificial neural network architectures that act as classification tools which self-organize, work in real-time, and require no retraining to classify novel sequences. We have adapted ART networks to provide support to scientists attempting to categorize tandem repeat DNA fragments from Onchocerca volvulus. In this approach, sequences of DNA fragments are presented to multiple ART-based networks which are linked together into two (or more) tiers; the first provides coarse sequence classification while the sub- sequent tiers refine the classifications as needed. The overall rating of the resulting classification of fragments is measured using statistical techniques based on those introduced to validate results from traditional phylogenetic analysis. Tests of the Hierarchical ART-based Classification Network, or HABclass network, indicate its value as a fast, easy-to-use classification tool which adapts to new data without retraining on previously classified data.

• Research Organization: Stanford Univ., CA (United States)
• OSTI ID: 377151
• Report Number(s): CONF-9408117--
• Country of Publication: United States
• Language: English

Related Subjects

55 BIOLOGY AND MEDICINE
BASIC STUDIES
99 GENERAL AND MISCELLANEOUS
ALGORITHMS
ARTIFICIAL INTELLIGENCE
DNA SEQUENCING
NEURAL NETWORKS