Objective: To develop a new bioinformatic tool based on a data-mining approach for extraction of the most infor- mative proteins that could be used to find the potential biomarkers for the detection of cancer. Methods...Objective: To develop a new bioinformatic tool based on a data-mining approach for extraction of the most infor- mative proteins that could be used to find the potential biomarkers for the detection of cancer. Methods: Two independent datasets from serum samples of 253 ovarian cancer and 167 breast cancer patients were used. The samples were examined by surface- enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). The datasets were used to extract the informative proteins using a data-mining method in the discrete stationary wavelet transform domain. As a dimensionality re- duction procedure, the hard thresholding method was applied to reduce the number of wavelet coefficients. Also, a distance measure was used to select the most discriminative coefficients. To find the potential biomarkers using the selected wavelet coefficients, we applied the inverse discrete stationary wavelet transform combined with a two-sided t-test. Results: From the ovarian cancer dataset, a set of five proteins were detected as potential biomarkers that could be used to identify the cancer patients from the healthy cases with accuracy, sensitivity, and specificity of 100%. Also, from the breast cancer dataset, a set of eight proteins were found as the potential biomarkers that could separate the healthy cases from the cancer patients with accuracy of 98.26%, sensitivity of 100%, and specificity of 95.6%. Conclusion: The results have shown that the new bioinformatic tool can be used in combination with the high-throughput proteomic data such as SELDI-TOF MS to find the potential biomarkers with high discriminative power.展开更多
To screen out the rapeseed(Brassica napus) combinations that are suitable for the production of both oilseed and vegetable, we carried out a field experiment for 11 new combinations(hybrids) of rapeseed and then perfo...To screen out the rapeseed(Brassica napus) combinations that are suitable for the production of both oilseed and vegetable, we carried out a field experiment for 11 new combinations(hybrids) of rapeseed and then performed grey relation analysis and cluster analysis on 12 traits including the yield and quality of young stem,seed yield, and several agronomic traits after harvesting of young stem. The results showed that A11, A7, and A4 had higher main stalk yield than other combinations.The young stem/leaf ratios of A11, A5, A7, A4, A3, and A1 were in line with the quality requirements for young stem commodity. The soluble sugar content of A2,A8, and A10 was higher than that of CK(Fengyou 737), and the seed yields of A4,A3, A2, A1, A5, and A6 were higher than that of CK. The 11 rapeseed combinations were classified into 3 grades by grey relation analysis and cluster analysis. Two combinations, A4(Y20A×95C4R) and A11(3194A×09-5R), showed the weighted relation degrees higher than 0.95, which were clustered into grade I by cluster analysis. They had good agronomic traits and good performance as both oilseed and vegetable. A8, A5, A3, A7, A2, A10, A6, and A1 were clustered into grade Ⅱ and A9 into grade Ⅲ. In this study, the oilseed and vegetable dual-purpose rapeseed combinations were screened out based on grey relation analysis and cluster analysis,which can provide reference for the breeding of oilseed and vegetable dual-purpose rapeseed combinations.展开更多
文摘Objective: To develop a new bioinformatic tool based on a data-mining approach for extraction of the most infor- mative proteins that could be used to find the potential biomarkers for the detection of cancer. Methods: Two independent datasets from serum samples of 253 ovarian cancer and 167 breast cancer patients were used. The samples were examined by surface- enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). The datasets were used to extract the informative proteins using a data-mining method in the discrete stationary wavelet transform domain. As a dimensionality re- duction procedure, the hard thresholding method was applied to reduce the number of wavelet coefficients. Also, a distance measure was used to select the most discriminative coefficients. To find the potential biomarkers using the selected wavelet coefficients, we applied the inverse discrete stationary wavelet transform combined with a two-sided t-test. Results: From the ovarian cancer dataset, a set of five proteins were detected as potential biomarkers that could be used to identify the cancer patients from the healthy cases with accuracy, sensitivity, and specificity of 100%. Also, from the breast cancer dataset, a set of eight proteins were found as the potential biomarkers that could separate the healthy cases from the cancer patients with accuracy of 98.26%, sensitivity of 100%, and specificity of 95.6%. Conclusion: The results have shown that the new bioinformatic tool can be used in combination with the high-throughput proteomic data such as SELDI-TOF MS to find the potential biomarkers with high discriminative power.
文摘To screen out the rapeseed(Brassica napus) combinations that are suitable for the production of both oilseed and vegetable, we carried out a field experiment for 11 new combinations(hybrids) of rapeseed and then performed grey relation analysis and cluster analysis on 12 traits including the yield and quality of young stem,seed yield, and several agronomic traits after harvesting of young stem. The results showed that A11, A7, and A4 had higher main stalk yield than other combinations.The young stem/leaf ratios of A11, A5, A7, A4, A3, and A1 were in line with the quality requirements for young stem commodity. The soluble sugar content of A2,A8, and A10 was higher than that of CK(Fengyou 737), and the seed yields of A4,A3, A2, A1, A5, and A6 were higher than that of CK. The 11 rapeseed combinations were classified into 3 grades by grey relation analysis and cluster analysis. Two combinations, A4(Y20A×95C4R) and A11(3194A×09-5R), showed the weighted relation degrees higher than 0.95, which were clustered into grade I by cluster analysis. They had good agronomic traits and good performance as both oilseed and vegetable. A8, A5, A3, A7, A2, A10, A6, and A1 were clustered into grade Ⅱ and A9 into grade Ⅲ. In this study, the oilseed and vegetable dual-purpose rapeseed combinations were screened out based on grey relation analysis and cluster analysis,which can provide reference for the breeding of oilseed and vegetable dual-purpose rapeseed combinations.
