Open AccessASPECT, an LDA-Based Predictive Algorithm for In Vitro Selection
Author(s) -
Puzhou Wang
Publication year - 2020
Publication title -
epic series in computing
Language(s) - English
Resource type - Conference proceedings
ISSN - 2398-7340
DOI - 10.29007/vkrq
Subject(s) - selection (genetic algorithm) , a priori and a posteriori , computer science , sequence (biology) , latent dirichlet allocation , identification (biology) , algorithm , dna sequencing , function (biology) , artificial intelligence , machine learning , computational biology , pattern recognition (psychology) , dna , biology , topic model , genetics , philosophy , botany , epistemology
In vitro selection enables the identification of functional DNA or RNA sequences (i.e., active sequences) out of entirely or partially random pools. Various computational tools have been developed for the analysis of sequencing data from selection experiments. However, most of these tools rely on structure-function relationship that is usually unknown for de novo selection experiments. This largely restricts the applications of these algorithms. In this paper, an active sequence predictor based on Latent Dirichlet allocation (LDA), ASPECT (Active Sequence PrEdiCTor), is proposed. ASPECT is independent of a priori knowledge on the structures of active sequences. Experimental results showed that ASPECT is effective.