Application of sequential pattern mining to assessment of psychological crisis intervention skills / 上海交通大学学报（医学版）

Objective: To explore the application of sequential pattern mining to the field of mental health, and analyze the role of the courses in improving the crisis intervention skills of mental health service personnel. Methods: The learning log data recorded by the online learning platform was used for sequential pattern mining, and the activity map was used to visually analyze the learning paths of learners with different learning performances. Results: In each case study, the number of high-frequency sequences of learners with different learning performance had significant differences, which were high-performance group > medium-performance group > low-performance group. In the crisis intervention consultation learning process, the high-performance group learners had the most diverse learning paths; the medium-performance group lacked the mastery of the several specific steps of the six-step model of crisis intervention, and the learning path integrity was poor; the low-performance group did not have a complete learning path. Conclusion: Diversified crisis intervention strategies are conducive to the smooth progress of the consultation process. Learners with different learning performance should develop corresponding skills development strategies according to their learning characteristics.

Efficient Mining of Interesting Patterns in Large Biological Sequences

Pattern discovery in biological sequences (e.g., DNA sequences) is one of the most challenging tasks in computational biology and bioinformatics. So far, in most approaches, the number of occurrences is a major measure of determining whether a pattern is interesting or not. In computational biology, however, a pattern that is not frequent may still be considered very informative if its actual support frequency exceeds the prior expectation by a large margin. In this paper, we propose a new interesting measure that can provide meaningful biological information. We also propose an efficient index-based method for mining such interesting patterns. Experimental results show that our approach can find interesting patterns within an acceptable computation time.

An Efficient Approach to Mining Maximal Contiguous Frequent Patterns from Large DNA Sequence Databases

Mining interesting patterns from DNA sequences is one of the most challenging tasks in bioinformatics and computational biology. Maximal contiguous frequent patterns are preferable for expressing the function and structure of DNA sequences and hence can capture the common data characteristics among related sequences. Biologists are interested in finding frequent orderly arrangements of motifs that are responsible for similar expression of a group of genes. In order to reduce mining time and complexity, however, most existing sequence mining algorithms either focus on finding short DNA sequences or require explicit specification of sequence lengths in advance. The challenge is to find longer sequences without specifying sequence lengths in advance. In this paper, we propose an efficient approach to mining maximal contiguous frequent patterns from large DNA sequence datasets. The experimental results show that our proposed approach is memory-efficient and mines maximal contiguous frequent patterns within a reasonable time.