In silico prediction of self-interacting proteins(SIPs)has become an important part of proteomics.There is an urgent need to develop effective and reliable prediction methods to overcome the disadvantage of high cost ...In silico prediction of self-interacting proteins(SIPs)has become an important part of proteomics.There is an urgent need to develop effective and reliable prediction methods to overcome the disadvantage of high cost and labor intensive in traditional biological wet-lab experiments.The goal of our survey is to sum up a comprehensive overview of the recent literature with the computational SIPs prediction,to provide important references for actual work in the future.In this review,we first describe the data required for the task of DTIs prediction.Then,some interesting feature extraction methods and computational models are presented on this topic in a timely manner.Afterwards,an empirical comparison is performed to demonstrate the prediction performance of some classifiers under different feature extraction and encoding schemes.Overall,we conclude and highlight potential methods for further enhancement of SIPs prediction performance as well as related research directions.展开更多
In multi-target tracking,Multiple Hypothesis Tracking (MHT) can effectively solve the data association problem. However,traditional MHT can not make full use of motion information. In this work,we combine MHT with Int...In multi-target tracking,Multiple Hypothesis Tracking (MHT) can effectively solve the data association problem. However,traditional MHT can not make full use of motion information. In this work,we combine MHT with Interactive Multiple Model (IMM) estimator and feature fusion. New algorithm greatly improves the tracking performance due to the fact that IMM estimator provides better estimation and feature information enhances the accuracy of data association. The new algorithm is tested by tracking tropical fish in fish container. Experimental result shows that this algorithm can significantly reduce tracking lost rate and restrain the noises with higher computational effectiveness when compares with traditional MHT.展开更多
基金This work was supported by the National Key R&D Program of China(2020YFA0908700 and 2018AAA0100100)the National Natural Science Foundation of China(Grant Nos.62002297,61902342,U1713212,61836005,and 62073225)+2 种基金the Natural Science Foundation of Guangdong Province-Outstanding Youth Program(2019B151502018)the Technology Research Project of Shenzhen City(JSGG20180507182904693)Public Technology Platform of Shenzhen City(GGFW2018021118145859).
文摘In silico prediction of self-interacting proteins(SIPs)has become an important part of proteomics.There is an urgent need to develop effective and reliable prediction methods to overcome the disadvantage of high cost and labor intensive in traditional biological wet-lab experiments.The goal of our survey is to sum up a comprehensive overview of the recent literature with the computational SIPs prediction,to provide important references for actual work in the future.In this review,we first describe the data required for the task of DTIs prediction.Then,some interesting feature extraction methods and computational models are presented on this topic in a timely manner.Afterwards,an empirical comparison is performed to demonstrate the prediction performance of some classifiers under different feature extraction and encoding schemes.Overall,we conclude and highlight potential methods for further enhancement of SIPs prediction performance as well as related research directions.
基金Supported by the National Natural Science Foundation of China (No. 60772154)the President Foundation of Graduate University of Chinese Academy of Sciences (No. 085102GN00)
文摘In multi-target tracking,Multiple Hypothesis Tracking (MHT) can effectively solve the data association problem. However,traditional MHT can not make full use of motion information. In this work,we combine MHT with Interactive Multiple Model (IMM) estimator and feature fusion. New algorithm greatly improves the tracking performance due to the fact that IMM estimator provides better estimation and feature information enhances the accuracy of data association. The new algorithm is tested by tracking tropical fish in fish container. Experimental result shows that this algorithm can significantly reduce tracking lost rate and restrain the noises with higher computational effectiveness when compares with traditional MHT.