Sequence-based prediction of protein-protein interactions using pseudo substitution matrix representation features and ensemble rotation forest classifier in HIV (human immunodeficiency virus)

[1]  Zhu-Hong You,et al.  An improved sequence-based prediction protocol for protein-protein interactions using amino acids substitution matrix and rotation forest ensemble classifiers , 2017, Neurocomputing.

[2]  Xing Chen,et al.  Construction of reliable protein-protein interaction networks using weighted sparse representation based classifier with pseudo substitution matrix representation features , 2016, Neurocomputing.

[3]  Loris Nanni,et al.  Prediction of protein structure classes by incorporating different protein descriptors into general Chou's pseudo amino acid composition. , 2014, Journal of theoretical biology.

[4]  Z. Debyser,et al.  A new potential approach to block HIV-1 replication via protein-protein interaction and strand-transfer inhibition. , 2014, Bioorganic & medicinal chemistry.

[5]  Xiaoqi Zheng,et al.  Predicting subcellular location of apoptosis proteins with pseudo amino acid composition: approach from amino acid substitution matrix and auto covariance transformation , 2012, Amino Acids.

[6]  Jason Weston,et al.  Semi-supervised multi-task learning for predicting interactions between HIV-1 and human proteins , 2010, Bioinform..

[7]  Donna R. Maglott,et al.  Human immunodeficiency virus type 1, human protein interaction database at NCBI , 2008, Nucleic Acids Res..

[8]  R. König,et al.  Global Analysis of Host-Pathogen Interactions that Regulate Early-Stage HIV-1 Replication , 2008, Cell.

[9]  De-Shuang Huang,et al.  Cancer classification using Rotation Forest , 2008, Comput. Biol. Medicine.

[10]  R. Hartmann,et al.  Prediction of protein-protein interaction inhibitors by chemoinformatics and machine learning methods. , 2007, Journal of medicinal chemistry.

[11]  Juan José Rodríguez Diez,et al.  Rotation Forest: A New Classifier Ensemble Method , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  I. Mushahwar.  Human Immunodeficiency Viruses: Molecular Virology, Pathogenesis, Diagnosis and Treatment , 2006 .

[13]  Sean R Eddy,et al.  Where did the BLOSUM62 alignment score matrix come from? , 2004, Nature Biotechnology.

[14]  Thomas G. Dietterich.  An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization , 2000, Machine Learning.

[15]  M. Gerstein,et al.  A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data , 2003, Science.

[16]  Thomas G. Dietterich.  Ensemble Methods in Machine Learning , 2000, Multiple Classifier Systems.

[17]  S. Henikoff,et al.  Amino acid substitution matrices from protein blocks. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[18]  W. Greene,et al.  Molecular insights into human immunodeficiency virus type 1 pathogenesis , 1992, Current Biology.