LIF is a cytokine with leiotropic activities. In order to understand better the physiological and patho-physiological role of LIF. we have developed a simple and specific enzyme-linked immunosorbent assay (ELISAI for ...LIF is a cytokine with leiotropic activities. In order to understand better the physiological and patho-physiological role of LIF. we have developed a simple and specific enzyme-linked immunosorbent assay (ELISAI for detecting LIF in human plasma and serum and in tissue culture media. A monoclonal ami-LIF antibody 8B11 (IgGl) produced in our laboratory was coated onto microtiter plates. After block-展开更多
As an emerging research field of brain science,multimodal data fusion analysis has attracted broader attention in the study of complex brain diseases such as Parkinson's disease(PD).However,current studies primari...As an emerging research field of brain science,multimodal data fusion analysis has attracted broader attention in the study of complex brain diseases such as Parkinson's disease(PD).However,current studies primarily lie with detecting the association among different modal data and reducing data attributes.The data mining method after fusion and the overall analysis framework are neglected.In this study,we propose a weighted random forest(WRF)model as the feature screening classifier.The interactions between genes and brain regions are detected as input multimodal fusion features by the correlation analysis method.We implement sample classification and optimal feature selection based on WRF,and construct a multimodal analysis framework for exploring the pathogenic factors of PD.The experimental results in Parkinson's Progression Markers Initiative(PPMI)database show that WRF performs better compared with some advanced methods,and the brain regions and genes related to PD are detected.The fusion of multi-modal data can improve the classification of PD patients and detect the pathogenic factors more comprehensively,which provides a novel perspective for the diagnosis and research of PD.We also show the great potential of WRF to perform the multimodal data fusion analysis of other brain diseases.展开更多
文摘LIF is a cytokine with leiotropic activities. In order to understand better the physiological and patho-physiological role of LIF. we have developed a simple and specific enzyme-linked immunosorbent assay (ELISAI for detecting LIF in human plasma and serum and in tissue culture media. A monoclonal ami-LIF antibody 8B11 (IgGl) produced in our laboratory was coated onto microtiter plates. After block-
基金This work was supported by the National Natural Science Foundation of China under Grant No.62072173the Natural Science Foundation of Hunan Province of China under Grant No.2020JJ4432+3 种基金the Key Scientific Research Projects of Department of Education of Hunan Province under Grant No.20A296the Degree and Postgraduate Education Reform Project of Hunan Province under Grant No.2019JGYB091Hunan Provincial Science and Technology Project Foundation under Grant No.2018TP1018,and the InnovationEntrepreneurship Training Program of Hunan Xiangjiang Artificial Intelligence Academy.
文摘As an emerging research field of brain science,multimodal data fusion analysis has attracted broader attention in the study of complex brain diseases such as Parkinson's disease(PD).However,current studies primarily lie with detecting the association among different modal data and reducing data attributes.The data mining method after fusion and the overall analysis framework are neglected.In this study,we propose a weighted random forest(WRF)model as the feature screening classifier.The interactions between genes and brain regions are detected as input multimodal fusion features by the correlation analysis method.We implement sample classification and optimal feature selection based on WRF,and construct a multimodal analysis framework for exploring the pathogenic factors of PD.The experimental results in Parkinson's Progression Markers Initiative(PPMI)database show that WRF performs better compared with some advanced methods,and the brain regions and genes related to PD are detected.The fusion of multi-modal data can improve the classification of PD patients and detect the pathogenic factors more comprehensively,which provides a novel perspective for the diagnosis and research of PD.We also show the great potential of WRF to perform the multimodal data fusion analysis of other brain diseases.
