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Additional file 3: Text S1. Quality control report of protein extraction, labeling efficiency, and mass spectrometry stability.

Additional file 1: Table S1. Primers sequences designed for RT-qPCR.

With the rapid development of the Internet, for-profit site need to analyze the user's behavior and provide more satisfactory service. Therefore, the classification of network behavior analysis and the further research based on it are more and more urgent on the agenda. In this paper, a method based on similar aggregation user behavior analysis algorithm is proposed. Addressing the recommendation of personalized books problems is solved by this method. Firstly, the user behavior is analyzed by using the RFM model. Secondly, the Apriori algorithm based on weight increment is applied to mining association rules between users in line with the recent habits of users. Similarity is calculated by Apriori algorithm with using VSM model. In this paper, readers’ browsing history of e-library which is provided by Harbin University of Science and Technology is used as experimental data. This method is compared with the method which does not use the weight increment and similar aggregation. Comparison of results showed that the method of our paper can meet the requirements of the Book Recommendation system.

In the computer-aided medical diagnosis, the problem of missing attribute values in many medical data sets brings a great challenge to data mining. To solve the problem, this paper proposes a method based on density clustering and grey relational analysis. It provides an effective solution for missing medical data. The method uses the characteristic and degree of data samples dynamic relation and the existing attribute value information to impute the missing value, in order to alleviate the difficulty brought by missing data for aided medical diagnosis. By comparison with the experiment, the proposed method can effectively solve the classification problem which causes by missing medical attribute value, accurately predict the patient’s health and provide the help to doctor’s diagnosis.

Open-domain Question Answering System is an interesting and challenging subject to research in the current field of natural language processing. The difference between QA system and the traditional text retrieval lies in the answer extraction module, which realizes the accurate answer extraction. The answer extraction on the basis of pattern matching is an efficient strategy, which focuses on displaying answers through formalized pattern. The most significant goal of the formalized answer extraction on basis of pattern matching strategy is to establish a complete pattern knowledge database. The automatic construction of formalized pattern for answer extraction is the future tendency of formalized extraction. Unfortunately, formalized answer extraction is still less effective than the extraction method based on statistical learning. This paper analyzes the following subjects: 1. Low coverage of questions. 2. Unreliability of pattern tag. 3. Difficulty in the assessment of pattern confidence. 4. Low level of pattern generalization. Based on the above four subjects, this thesis attempts to automatically construct pattern knowledge database through pattern learning and question sorting architecture based on answer types, use reliable pattern tag to process the formalization of pattern and dramatically increase coverage and accuracy. Furthermore, assess the pattern confidence in terms of coverage and accuracy. Finally, it will put forward pattern generalization technology based on the principle of unchanged pattern major elements, which observably enhances the pattern generalizing performance. Experimental results show that the average coverage of this paper reaches 57.2%, the average accuracy reaches 46.2%; major question accuracy is 80.8% and generalization technology increases accuracy nearly 6%. In sum, this paper realizes high extraction accuracy with simple methods. Especially in the issue of pattern matching on the case, can achieve high extraction accuracy.