文档介绍：A Novel ncRNA Gene Prediction Approach Based on
Fuzzy works with Structure Learning
Dandan Song*, Zhidong Deng
State Key Laboratory of Intelligent Technology and Systems,
Tsinghua National Laboratory for Information Science and Technology,
Department puter Science, Tsinghua University
Beijing 100084, China
******@., ******@tsinghua.
*current address: School puter Science, Beijing Institute of Technology
Abstract—Discovering ncRNA genes is a challenging problem, database. Will et al. [9] inferred ncRNA families by means of
which has attracted much attention recently. The accuracy of genome-scale structure-based clustering in the Ciona
computational ncRNA prediction methods still needs to be intestinalis genome. Rivas et al. [10] developed parative
improved, however, due to the diversity and the lack of consensus genomic approach for ncRNA genes prediction using a
patterns of ncRNA genes. In this paper, we propose an effective generalized stochastic context-free grammar (SCFG). Their
computational approach based on fuzzy works with ncRNA genefinder, named QRNA, achieved a high sensitivity
structure learning (FNNSL) for novel ncRNA gene prediction. It for detecting novel ncRNAs. However, putational
has advantages such as explicit physical meanings of nodes plexity is high and is constrained to predict conserved
parameters