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Endonuclease-rolling circle amplification-based method for sensitive analysis of DNA-binding protein 认领 引用 被引量：4
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作者 Min Li Li Dong Rui Zhou +2 位作者 Hong Zhao Jin Ke Wang Zu Hong Lu 《Chinese Chemical Letters》 SCIE CAS 2009年第11期1315-1318,共4页
A sensitive approach for the qualitative detection of DNA-binding protein on the microarray was developed. DNA complexes in which a partial duplex region is formed from a biotin-primer and a circle single strand DNA ... A sensitive approach for the qualitative detection of DNA-binding protein on the microarray was developed. DNA complexes in which a partial duplex region is formed from a biotin-primer and a circle single strand DNA （ssDNA） were spotted on a microarray. The endonuclease recognition site （ERS） and the DNA-binding sites （DBS） were arranged side by side within the duplex region. The working principle of the detection system is described as follows： when the DNA-binding protein capture the DBS, the endonuclease could not attach to the ERS, and the immobilized primer in the DNA complex could be extended along the circle ssDNA by rolling circle amplification （RCA）. When no protein protects the DBS, the ERS could be attacked by the endonuclease and subsequently no rolling circle amplification occurs. Thereby we can detect the sequence specific DNA-binding activity with high-sensitivity due to the signal amplification of RCA. 展开更多
关键词 Rolling circle amplification DNA-binding protein Microarray
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Predicting the pathological status of mammographic microcalcifications through a radiomics approach 认领 引用 被引量：1
2
作者 Min Li Liyu Zhu +3 位作者 Guangquan Zhou Jianan He Yanni Jiang Yang Chen 《Intelligent Medicine》 EI 2021年第3期95-103,共9页
Objective The study aimed to develop a machine learning(ML)-coupled interpretable radiomics signature to predict the pathological status of non-palpable suspicious breast microcalcifications(MCs).Methods We enrolled 4... Objective The study aimed to develop a machine learning(ML)-coupled interpretable radiomics signature to predict the pathological status of non-palpable suspicious breast microcalcifications(MCs).Methods We enrolled 463 digital mammographical view images from 260 consecutive patients detected with non-palpable MCs and BI-RADS scored at 4(training cohort,n=428;independent testing cohort,n=35)in the First Affiliated Hospital of Nanjing Medical University between September 2010 and January 2019.Subsequently,837 textures and 9 shape features were subsequently extracted from each view and finally selected by an XGBoostembedded recursive feature elimination technique(RFE),followed by four machine learning-based classifiers to build the radiomics signature.Results Ten radiomic features constituted a malignancy-related signature for breast MCs as logistic regression(LR)and support vector machine(SVM)yielded better positive predictive value(PPV)/sensitivity(SE),0.904(95%CI,0.865–0.949)/0.946(95%CI,0.929–0.977)and 0.891(95%CI,0.822–0.939)/0.939(95%CI,0.907–0.973)respectively,outperforming their negative predictive value(NPV)/specificity(SP)from 10-fold crossvalidation(10FCV)of the training cohort.The optimal prognostic model was obtained by SVM with an area under the curve(AUC)of 0.906(95%CI,0.834–0.969)and accuracy(ACC)0.787(95%CI,0.680–0.855)from 10FCV against AUC 0.810(95%CI,0.760–0.960)and ACC 0.800 from the testing cohort.Conclusion The proposed radiomics signature dependens on a set of ML-based advanced computational algorithms and is expected to identify pathologically cancerous cases from mammographically undecipherable MCs and thus offer prospective clinical diagnostic guidance. 展开更多
关键词 Nonpalpable microcalcifications Radiomics Machine learning
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