Essential proteins are inseparable in cell growth and survival. The study of essential proteins is important for understanding cellular functions and biological mechanisms. Therefore, various computable methods have b...Essential proteins are inseparable in cell growth and survival. The study of essential proteins is important for understanding cellular functions and biological mechanisms. Therefore, various computable methods have been proposed to identify essential proteins. Unfortunately, most methods based on network topology only consider the interactions between a protein and its neighboring proteins, and not the interactions with its higher-order distance proteins. In this paper, we propose the DSEP algorithm in which we integrated network topology properties and subcellular localization information in protein–protein interaction(PPI) networks based on four-order distances, and then used random walks to identify the essential proteins. We also propose a method to calculate the finite-order distance of the network, which can greatly reduce the time complexity of our algorithm. We conducted a comprehensive comparison of the DSEP algorithm with 11 existing classical algorithms to identify essential proteins with multiple evaluation methods. The results show that DSEP is superior to these 11 methods.展开更多
The concentrations of 18 elements in subcellular fractions of human liver were determined by combining differential centrifugation and INAA. Samples of human liver were homogenized in a buffer. The homogenate was sepa...The concentrations of 18 elements in subcellular fractions of human liver were determined by combining differential centrifugation and INAA. Samples of human liver were homogenized in a buffer. The homogenate was separated into nuclei, mitochondrial, lysosomal, microsomal and cytosol fractions by successive differential centrifugation. Biological standard reference materials were used to evaluate the accuracy of the INAA method, and the results agree with the certified values. Element levels in subcellular fractions of human liver were discussed.展开更多
Apoptosis proteins play an important role in the development and homeostasis of an organism. The elucidation of the subcellular locations and functions of these proteins is helpful for understanding the mechanism of p...Apoptosis proteins play an important role in the development and homeostasis of an organism. The elucidation of the subcellular locations and functions of these proteins is helpful for understanding the mechanism of programmed cell death. In this paper, the recurrent quantification analysis, Hilbert-Huang transform methods, the maximum relevance and minimum redundancy method and support vector machine are used to predict the subcellular location of apoptosis proteins. The validation of the jackknife test suggests that the proposed method can improve the prediction accuracy of the subcellular location of apoptosis proteins and its application may be promising in other fields.展开更多
The early risk of internal contaminated accumulation of 147Pm is in blood cells and endothelial cells, especially in red blood cells. Then 147Pm is selectively deposited in ultrastructure of liver cells, such as in nu...The early risk of internal contaminated accumulation of 147Pm is in blood cells and endothelial cells, especially in red blood cells. Then 147Pm is selectively deposited in ultrastructure of liver cells, such as in nucleus, nucleolus, rough endoplasmic reticulum, mitochondria and microbodies. Dense tracks also appear in mitochondria and lysosome of pedal cells within renal corpuscle, and so does in nucleus as well as in mitochondria and microbodies of epicyte of kidney near-convoluted tubule. With the prolongation of observing time, 147Pm is selectively and steadily deposited in subcellular level of organic component for bone. Substantial amount of 147Pm is taken up into the nuclear fraction of osteoclasts and osteoblasts. Particularly, in organelles 147Pm is mainly accumulated in rough endoplasmic reticulum and in mitochondria.Autoradiographic tracks especially localize in combined point between Golgi complex and transitive vesicle of rough endoplasmic reticulum. In addition, numerous 147Pm deposited in collagenous fibre within interstitial of bone cells is hardly excreted.展开更多
The rapidly increasing number of sequences entering into the genome databank has created the need for fully automated methods to analyze them.Knowing the cellular location of a protein is a key step towards understand...The rapidly increasing number of sequences entering into the genome databank has created the need for fully automated methods to analyze them.Knowing the cellular location of a protein is a key step towards understanding its function.The development in statistical prediction of protein attributes generally consists of two cores: one is to construct a training dataset and the other is to formulate a predictive algorithm.The latter can be further separated into two subcores: one is how to give a mathematical expression to effectively represent a protein and the other is how to find a powerful algorithm to accurately perform the prediction.To predict the subcellular location of eukaryotic protein,a systematic prediction approach comprised of a novel feature extraction method,an idea of combining this feature extraction method with support vector machine(SVM) algorithm,and ’one-versus-rest’ & ’all-versus-all’ strategies have been proposed in this paper.Consequently,the total predictive accuracies reach 95.5% for four locations.Compared with existing methods,this new approach provides better predictive performance.For example,it is 13.5%,5.1% higher than Yuan’s and Hua’s methods respectively.These results demonstrate the applicability of this new method and concept and possible improvement of prediction for the protein subcellular location.It is anticipated that the current approach may also have a series of impacts on the prediction of other protein features.展开更多
基金Project supported by the Gansu Province Industrial Support Plan (Grant No.2023CYZC-25)the Natural Science Foundation of Gansu Province (Grant No.23JRRA770)the National Natural Science Foundation of China (Grant No.62162040)。
文摘Essential proteins are inseparable in cell growth and survival. The study of essential proteins is important for understanding cellular functions and biological mechanisms. Therefore, various computable methods have been proposed to identify essential proteins. Unfortunately, most methods based on network topology only consider the interactions between a protein and its neighboring proteins, and not the interactions with its higher-order distance proteins. In this paper, we propose the DSEP algorithm in which we integrated network topology properties and subcellular localization information in protein–protein interaction(PPI) networks based on four-order distances, and then used random walks to identify the essential proteins. We also propose a method to calculate the finite-order distance of the network, which can greatly reduce the time complexity of our algorithm. We conducted a comprehensive comparison of the DSEP algorithm with 11 existing classical algorithms to identify essential proteins with multiple evaluation methods. The results show that DSEP is superior to these 11 methods.
文摘The concentrations of 18 elements in subcellular fractions of human liver were determined by combining differential centrifugation and INAA. Samples of human liver were homogenized in a buffer. The homogenate was separated into nuclei, mitochondrial, lysosomal, microsomal and cytosol fractions by successive differential centrifugation. Biological standard reference materials were used to evaluate the accuracy of the INAA method, and the results agree with the certified values. Element levels in subcellular fractions of human liver were discussed.
基金supported by the National Natural Science Foundation of China (Grant No. 11071282)the Chinese Program for New Century Excellent Talents in University (Grant No. NCET-08-06867)+4 种基金the Natural Science Foundation of Hunan Province of China(Grant No. 10JJ7001)the Lotus Scholars Program of Hunan Province of Chinathe Aid Program for Science and Technology Innovative Research Team in Higher Educational Institutions of Hunan Province of Chinathe Australian Research Council (GrantNo. DP0559807)the Postgraduate Research and Innovation Project of Hunan Province of China (Grant No. CX2010B243)
文摘Apoptosis proteins play an important role in the development and homeostasis of an organism. The elucidation of the subcellular locations and functions of these proteins is helpful for understanding the mechanism of programmed cell death. In this paper, the recurrent quantification analysis, Hilbert-Huang transform methods, the maximum relevance and minimum redundancy method and support vector machine are used to predict the subcellular location of apoptosis proteins. The validation of the jackknife test suggests that the proposed method can improve the prediction accuracy of the subcellular location of apoptosis proteins and its application may be promising in other fields.
文摘The early risk of internal contaminated accumulation of 147Pm is in blood cells and endothelial cells, especially in red blood cells. Then 147Pm is selectively deposited in ultrastructure of liver cells, such as in nucleus, nucleolus, rough endoplasmic reticulum, mitochondria and microbodies. Dense tracks also appear in mitochondria and lysosome of pedal cells within renal corpuscle, and so does in nucleus as well as in mitochondria and microbodies of epicyte of kidney near-convoluted tubule. With the prolongation of observing time, 147Pm is selectively and steadily deposited in subcellular level of organic component for bone. Substantial amount of 147Pm is taken up into the nuclear fraction of osteoclasts and osteoblasts. Particularly, in organelles 147Pm is mainly accumulated in rough endoplasmic reticulum and in mitochondria.Autoradiographic tracks especially localize in combined point between Golgi complex and transitive vesicle of rough endoplasmic reticulum. In addition, numerous 147Pm deposited in collagenous fibre within interstitial of bone cells is hardly excreted.
文摘The rapidly increasing number of sequences entering into the genome databank has created the need for fully automated methods to analyze them.Knowing the cellular location of a protein is a key step towards understanding its function.The development in statistical prediction of protein attributes generally consists of two cores: one is to construct a training dataset and the other is to formulate a predictive algorithm.The latter can be further separated into two subcores: one is how to give a mathematical expression to effectively represent a protein and the other is how to find a powerful algorithm to accurately perform the prediction.To predict the subcellular location of eukaryotic protein,a systematic prediction approach comprised of a novel feature extraction method,an idea of combining this feature extraction method with support vector machine(SVM) algorithm,and ’one-versus-rest’ & ’all-versus-all’ strategies have been proposed in this paper.Consequently,the total predictive accuracies reach 95.5% for four locations.Compared with existing methods,this new approach provides better predictive performance.For example,it is 13.5%,5.1% higher than Yuan’s and Hua’s methods respectively.These results demonstrate the applicability of this new method and concept and possible improvement of prediction for the protein subcellular location.It is anticipated that the current approach may also have a series of impacts on the prediction of other protein features.
