ASPECT, an LDA-Based Predictive Algorithm for In Vitro Selection

6 pages•Published: March 11, 2020

Abstract

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.

Keyphrases: in vitro selection, lda, topic modeling

In: Qin Ding, Oliver Eulenstein and Hisham Al-Mubaid (editors). Proceedings of the 12th International Conference on Bioinformatics and Computational Biology, vol 70, pages 192-197.

Links:	https://easychair.org/publications/paper/RxnT
	https://doi.org/10.29007/vkrq

BibTeX entry

@inproceedings{BICOB2020:ASPECT_LDA_Based_Predictive,
  author    = {Puzhou Wang},
  title     = {ASPECT, an LDA-Based Predictive Algorithm for In Vitro Selection},
  booktitle = {Proceedings of the 12th International Conference on Bioinformatics and Computational Biology},
  editor    = {Qin Ding and Oliver Eulenstein and Hisham Al-Mubaid},
  series    = {EPiC Series in Computing},
  volume    = {70},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/RxnT},
  doi       = {10.29007/vkrq},
  pages     = {192-197},
  year      = {2020}}

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