Adaptive algorithm of automated annotation
Related Experiment Video
Updated: Aug 18, 2026
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
A M Leontovich1, L I Brodsky, V A Drachev
1Belozersky Institute of Physico-Chemical Biology, Moscow State University, Russia.
Motivation:
It is common knowledge that the avalanche of data arriving from the sequencing projects cannot be annotated either experimentally or manually by experts. The need for a reliable and convenient tool for automated sequence annotation is broadly recognized.
Results:
Here, we describe the Adaptive Algorithm of Automated Annotation (A(4)) based on a statistical approach to this problem. The mathematical model relates a set of homologous sequences and descriptions of their functional properties, and calculates the probabilities of transferring a sequence description onto its homologue. The proposed model is adaptive, its parameters (distribution characteristics, transference probabilities, thresholds, etc.) are dynamic, i.e. are generated individually for the sequences and various functional properties (words of the description). The proposed technique significantly outperforms the widely used test for frequency threshold, which is a special case of our model realized for the simplest set of parameters. The prediction technique has been realized as a computer program and tested on a random sequence sampling from SWISS-PROT.
Availability:
The automated annotation program based on the proposed algorithm is available through the Web browser at http://www.genebee.msu.su/services/annot/basic.html.
