Reflection on teaching of boinformatics course for medical students in the big data era / 中华医学教育探索杂志

Bioinformatics is an intercross field of biology and computer science. With the develop-ment of computer network information technology and"precision medical care", biomedical field ushered in the era of big data. The demand for bioinformatics knowledge to medical students has shifted from single gene level to the analysis on massive medical data. This paper analyzed the characteristics of the big data era, as well as the problems of the teaching idea, curriculum coverage and teaching platform could not meet the requirements of the new era in teaching of bioinformatics course for medical students. Finally, we pro-posed several measures to strengthen the reform of bioinformatics teaching to meet the challenge of the big data era, including optimizing courses, changing educational philosophy, introducing new teaching methods, and setting up teaching platform with times.

Exploration of bioinformatics courses for eight-year medical students / 中华医学教育探索杂志

The need of eight-year clinical students for bioinformatics undergraduate courses is described.In addition,the measures and experiences on textbooks choosing,teaching content assignment,teaching methods designing and test means innovation are also discussed.All these provide a reference implementation for the development of eight-year clinical bioinformatics courses.

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.