**2. Research method of serum peptidomics**

The research of serum peptidomics involves in sample detection and data analysis. Fig.7 is the work flow of serum peptidomics. As shown in Fig.7, blood samples from the

Fig. 7. Technical Process for Serum Peptidomics

The three systems, namely, SELDI system, ClinProt system and ClinTOF system, of the

For the moment, the serum peptidomics is clinically used in complex diseases involved in polygene and featuring multi-cause heterogeneity, like cancer (including colon and rectal cancer, lung cancer, hepatic carcinoma, esophagus cancer, stomach cancer, cervical carcinoma and nasopharyngeal carcinoma), nerve degenerative diseases (including Alzheimer disease, Parkinson's disease, Huntington's Disease), autoimmune diseases (rheumatoid arthritis, system lupus erythematosus syndromes), cardio-cerebrovascular disease and palsy, to discover protein/polypeptide profiling and biomarker profiling as well as the single biomarker in the serum of sufferers of these diseases, making breakthroughs and providing a new tool for the researches of disease pathogeny, drug

The research of serum peptidomics involves in sample detection and data analysis. Fig.7 is the work flow of serum peptidomics. As shown in Fig.7, blood samples from the

**1.2.4 Comparison of serum peptidomics research platform** 

serum peptidomics research platform are compared in Table 2.

**1.3 Main application areas of serum peptidomics** 

**2. Research method of serum peptidomics** 

Fig. 7. Technical Process for Serum Peptidomics

target, diagnosis and treatment.

pathological group and the healthy control group are collected and then the serum is separated from the blood. The serum is mixed with magnetic beads to extract serum polypeptides for detection with the mass spectrometry. The spectra obtained can produce characteristic spectrum peaks. The meaningful characteristic spectrum peaks are screened out with the statistical method. A prediction model is built with the pattern recognition method and validated with test data. After continuous optimization, a disease diagnosis model and a group of characteristic spectrum peaks can be obtained.

Briefly, the research includes magnetic beads, detection instrument, analysis software, polypeptide identification and clinical model.
