Application of Competency-based Education in Traditional Chinese Rehabilitation Therapy / 中国康复理论与实践

@#Objective To explore the effect of competency-based education on course of Traditional Chinese Rehabilitation Therapy.Methods 2 sophomore classes of rehabilitation technology were as the control group and the reform group. The control group was taught with traditional model, while the reform group were taught with the competency-based education. The achievement of these groups were compared. Results There was not significant difference in the theoretical test, but was in the skill test and questionnaire score between the groups (P<0.05). Conclusion The competency-based education can improve the performance of students studying Traditional Chinese Rehabilitation Therapy.

Support vector data description for finding non-coding RNA gene / 生物医学工程学杂志

In the field of computational molecule biology, there is still a challenging question of how to detect non-coding RNA gene in lots of unlabeled sequences. Generally, the methods of machine learning and classification are employed to answer this question. However, only a limited number of positive training samples and unlabeled samples are available. The negative samples are difficult to define appropriately, yet they are necessary for usual learning-then-classification method. The common way for most of the existing non-coding RNA gene finding methods is to produce a number of random sequences as negative samples, which may hold some characteristic of positive sample sequences. Consequently, the contrived uncertain factor was introduced and the performance of methods was not good enough. In this paper, Support Vector Data Description (SVDD) is in use for to learning and classification as well as for detecting non-coding RNA gene in lots of unlabeled sequences, and the k-means clustering algorithm is employed before SVDD training to deal with the high flase positive fault in the result of SVDD. The training samples (target samples) are non-coding RNA genes validated by experiment. Moreover, appropriate features were constructed by Principal Component Analysis (PCA). The effectiveness and performance of the method are demonstrated by testing the cases in NONCODE databases and E. coli genome.

Prediction of outer membrane proteins using support vector machine with combined features / 生物工程学报

Outer membrane proteins (OMPs) are embedded in the outer membrane of Gram-negative bacteria, mitochondria, and chloroplasts. The cellular location and functional diversity of OMPs makes them an important protein class. Researches on prediction of OMPs by bioinformatics methods can bring helpful methodologies for identifying OMPs from genomic sequences and for the successful prediction of their secondary and tertiary structures. In this paper, three feature classes were calculated from protein sequences: amino acid compositions, dipeptide compositions and weighted amino acid index correlation coefficients. Then, three feature classes were combined and inputted into a support vector machine (SVM) based predictor to identify OMPs from other folding types of proteins. The results of discrimination using several combined features including four amino acid index categories were calculated, and the influence on discrimination accuracy using different correlation coefficients with different orders and weights was discussed. In cross-validated tests and independent tests for identifying OMPs from a dataset of 1087 proteins belonging to all different types of globular and membrane proteins, the method using combined features obtains an overall accuracy of 96.96% and 97.33% respectively. And these results outperform that of other methods in the literature. Using this method, high specificities are shown from the results of identifying OMPs in five bacterial genomes, and over 99% OMPs with known three-dimensional structures in the PDB database are correctly discriminated. These results indicate that the method is a powerful tool for OMPs discrimination in genomes.

RNA secondary structure prediction based on support vector machine classification / 生物工程学报

The comparative sequence analysis is the most reliable method for RNA secondary structure prediction, and many algorithms based on it have been developed in last several decades. This paper considers RNA structure prediction as a 2-classes classification problem: given a sequence alignment, to decide whether or not two columns of alignment form a base pair. We employed Support Vector Machine (SVM) to predict potential paired sites, and selected co-variation information, thermodynamic information and the fraction of complementary bases as feature vectors. Considering the effect of sequence similarity upon co-variation score, we introduced a similarity weight factor, which could adjust the contribution of co-variation and thermodynamic information toward prediction according to sequence similarity. The test on 49 Rfam-seed alignments showed the effectiveness of our method, and the accuracy was better than many similar algorithms. Furthermore, this method could predict simple pseudoknot.

RNA Sequence-structural Alignment Based on Quantum Evolutionary Algorithm / 生物化学与生物物理进展

As a classical problem of computational molecular biology, the multiple sequences 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 require 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 evolutionary algorithm(QEA) to align RNA sequences, is proposed. The program introduce a full crossover operator and a fitness function which considering the information of RNA premary sequence and secondary structure, and improving on prematurity controling and the convergent speed. The effectiveness and performance of QEA-MRNA are demonstrated by testing cases in BRAliBase.