Protein-protein interactions(PPIs)are of great importance to understand genetic mechanisms,delineate disease pathogenesis,and guide drug design.With the increase of PPI data and development of machine learning technol...Protein-protein interactions(PPIs)are of great importance to understand genetic mechanisms,delineate disease pathogenesis,and guide drug design.With the increase of PPI data and development of machine learning technologies,prediction and identification of PPIs have become a research hotspot in proteomics.In this study,we propose a new prediction pipeline for PPIs based on gradient tree boosting(GTB).First,the initial feature vector is extracted by fusing pseudo amino acid composition(Pse AAC),pseudo position-specific scoring matrix(Pse PSSM),reduced sequence and index-vectors(RSIV),and autocorrelation descriptor(AD).Second,to remove redundancy and noise,we employ L1-regularized logistic regression(L1-RLR)to select an optimal feature subset.Finally,GTB-PPI model is constructed.Five-fold cross-validation showed that GTB-PPI achieved the accuracies of 95.15% and 90.47% on Saccharomyces cerevisiae and Helicobacter pylori datasets,respectively.In addition,GTB-PPI could be applied to predict the independent test datasets for Caenorhabditis elegans,Escherichia coli,Homo sapiens,and Mus musculus,the one-core PPI network for CD9,and the crossover PPI network for the Wnt-related signaling pathways.The results show that GTB-PPI can significantly improve accuracy of PPI prediction.The code and datasets of GTB-PPI can be downloaded from https://github.com/QUST-AIBBDRC/GTB-PPI/.展开更多
In the paper,we consider the l_(1)-regularized least square problem which has been intensively involved in the fields of signal processing,compressive sensing,linear inverse problems and statistical inference.The cons...In the paper,we consider the l_(1)-regularized least square problem which has been intensively involved in the fields of signal processing,compressive sensing,linear inverse problems and statistical inference.The considered problem has been proved recently to be equivalent to a nonnegatively constrained quadratic programming(QP).In this paper,we use a recently developed active conjugate gradient method to solve the resulting QP problem.To improve the algorithm’s performance,we design a subspace exact steplength as well as a precondition technique.The performance comparisons illustrate that the proposed algorithm is competitive and even performs little better than several state-of-the-art algorithms.展开更多
In this paper,we prove that the generator of any bounded analytic semigroup in(θ,1)-type real interpolation of its domain and underlying Banach space has maximal L^(1)-regularity,using a duality argument combined wit...In this paper,we prove that the generator of any bounded analytic semigroup in(θ,1)-type real interpolation of its domain and underlying Banach space has maximal L^(1)-regularity,using a duality argument combined with the result of maximal continuous regularity.As an application,we consider maximal L^(1)-regularity of the Dirichlet-Laplacian and the Stokes operator in inhomogeneous B_(q),^(s),1-type Besov spaces on domains of R^(n),n≥2.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.61863010)the Key Research and Development Program of Shandong Province of China(Grant No.2019GGX101001)the Natural Science Foundation of Shandong Province of China(Grant No.ZR2018MC007)。
文摘Protein-protein interactions(PPIs)are of great importance to understand genetic mechanisms,delineate disease pathogenesis,and guide drug design.With the increase of PPI data and development of machine learning technologies,prediction and identification of PPIs have become a research hotspot in proteomics.In this study,we propose a new prediction pipeline for PPIs based on gradient tree boosting(GTB).First,the initial feature vector is extracted by fusing pseudo amino acid composition(Pse AAC),pseudo position-specific scoring matrix(Pse PSSM),reduced sequence and index-vectors(RSIV),and autocorrelation descriptor(AD).Second,to remove redundancy and noise,we employ L1-regularized logistic regression(L1-RLR)to select an optimal feature subset.Finally,GTB-PPI model is constructed.Five-fold cross-validation showed that GTB-PPI achieved the accuracies of 95.15% and 90.47% on Saccharomyces cerevisiae and Helicobacter pylori datasets,respectively.In addition,GTB-PPI could be applied to predict the independent test datasets for Caenorhabditis elegans,Escherichia coli,Homo sapiens,and Mus musculus,the one-core PPI network for CD9,and the crossover PPI network for the Wnt-related signaling pathways.The results show that GTB-PPI can significantly improve accuracy of PPI prediction.The code and datasets of GTB-PPI can be downloaded from https://github.com/QUST-AIBBDRC/GTB-PPI/.
基金This work is supported by the National Natural Science Foundation of China(No.11371154)the Foundation for Distinguished Young Talents in Higher Education of Guangdong(No.3XZ150603)Characteristic innovation project of Guangdong(No.2015KTSCX1).
文摘In the paper,we consider the l_(1)-regularized least square problem which has been intensively involved in the fields of signal processing,compressive sensing,linear inverse problems and statistical inference.The considered problem has been proved recently to be equivalent to a nonnegatively constrained quadratic programming(QP).In this paper,we use a recently developed active conjugate gradient method to solve the resulting QP problem.To improve the algorithm’s performance,we design a subspace exact steplength as well as a precondition technique.The performance comparisons illustrate that the proposed algorithm is competitive and even performs little better than several state-of-the-art algorithms.
文摘In this paper,we prove that the generator of any bounded analytic semigroup in(θ,1)-type real interpolation of its domain and underlying Banach space has maximal L^(1)-regularity,using a duality argument combined with the result of maximal continuous regularity.As an application,we consider maximal L^(1)-regularity of the Dirichlet-Laplacian and the Stokes operator in inhomogeneous B_(q),^(s),1-type Besov spaces on domains of R^(n),n≥2.
