Wang Zheng-zhi

Consensus RNA Secondary Structure Prediction Based on SVMs

Although many endeavors have been done in the field of RNA secondary structure prediction, it is still an open problem in the computational molecular biology. The comparative sequence analysis is the golden standard method when given homologous sequence alignment. The essential of this method can be regarded as classifier problem: to judge whether any two columns of an alignment correspond to a base pair using provided information by alignment. Here, we employ SVMs to resolve this classifier problem, and select the covaration score, the fraction of complementary nucleotides and the consensus probability matrix as the feature vectors. Test on the Rfam shows that average MCC of our method is higher (0.841) than KnetFold (0.831), Pfold (0.741) and RNAalifold(0.623).

Geometric invariants using geometry algebra

In this paper, we interpret the fundamental geometric invariants in a new framework: geometric algebra (GA). The study of such geometric invariance is a field of active research. The homogeneous model (Grassmann model) is selected for different kinds of geometric invariants, including Euclidean invariants, Projective invariants etc. GA focuses on the subspace of a vector space as elements of computation. Linear transformation can be extended to the subspace structure. The paper compares the meaning of invariants using the new model with that using the traditional one. This work shows that geometric algebra is a very elegant language for expressing geometric objects.

Quantum-Inspired Evolutionary Algorithm for RNA Multiple Sequence-Structure Alignment

As a classical problem of computational molecular biology, the multiple sequence-structure alignment is also important foundational process. RNA is one of biological polymer, and is different from protein and DNA that the secondary structure of RNA is more conservative than its primary sequence. Therefore, RNA multiple sequences alignment requires not only information of sequences, but also information of secondary structures which those sequences will form. Here, a program—QEA-MRNA, which based on Quantum-inspired Evolutionary Algorithm to align RNA sequences, is proposed. The program introduce a full crossover operator and a fitness function which considering the information of RNA primary sequence and secondary structure, and improving on premature controlling and the convergent speed. The effectiveness and performance of QEA-MRNA are demonstrated by testing cases in BRAliBase.