Using Weighted Sparse Representation Model Combined with Discrete Cosine Transformation to Predict Protein-Protein Interactions from Protein Sequence
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Zhu-Hong You | Lirong Wang | Xin Gao | Yu-An Huang | Leon Wong | Xin Gao | Yu-An Huang | Zhuhong You | Leon Wong | Lirong Wang | Lirong Wang
[1] Shuai Li,et al. A MapReduce based parallel SVM for large-scale predicting protein-protein interactions , 2014, Neurocomputing.
[2] Gabriele Ausiello,et al. MINT: the Molecular INTeraction database , 2006, Nucleic Acids Res..
[3] Jean-Loup Faulon,et al. Predicting protein-protein interactions using signature products , 2005, Bioinform..
[4] Gary D Bader,et al. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry , 2002, Nature.
[5] André Fujita. Identification of altered gene regulatory networks in gliomas by using Gene Network Entropy Analysis , 2014 .
[6] T E Karakasidis,et al. Fuzzy polynucleotide spaces and metrics , 2006, Bulletin of mathematical biology.
[7] Jianqin Zhou,et al. On discrete cosine transform , 2011, ArXiv.
[8] James G. Lyons,et al. Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou׳s general PseAAC. , 2015, Journal of theoretical biology.
[9] Kuldip K. Paliwal,et al. A deterministic approach to regularized linear discriminant analysis , 2015, Neurocomputing.
[10] Kuldip K. Paliwal,et al. A mixture of physicochemical and evolutionary-based feature extraction approaches for protein fold recognition , 2015, Int. J. Data Min. Bioinform..
[11] Shuai Li,et al. Detection of Protein-Protein Interactions from Amino Acid Sequences Using a Rotation Forest Model with a Novel PR-LPQ Descriptor , 2015, ICIC.
[12] 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.
[13] Zihan Zhou,et al. Demo: Robust face recognition via sparse representation , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.
[14] A. Valencia,et al. In silico two‐hybrid system for the selection of physically interacting protein pairs , 2002, Proteins.
[15] Xiaobo Zhou,et al. A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network , 2010, BMC Bioinformatics.
[16] Loris Nanni,et al. Hyperplanes for predicting protein-protein interactions , 2005, Neurocomputing.
[17] Xiaolong Wang,et al. Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection , 2013, Bioinform..
[18] Ioannis Xenarios,et al. DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions , 2002, Nucleic Acids Res..
[19] P. Bork,et al. Functional organization of the yeast proteome by systematic analysis of protein complexes , 2002, Nature.
[20] Zhen Ji,et al. Prediction of protein-protein interactions from amino acid sequences using a novel multi-scale continuous and discontinuous feature set , 2014, BMC Bioinformatics.
[21] Yihong Gong,et al. Locality-constrained Linear Coding for image classification , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[22] T E Karakasidis,et al. A study of entropy/clarity of genetic sequences using metric spaces and fuzzy sets. , 2010, Journal of theoretical biology.
[23] Xiaolong Wang,et al. Using distances between Top-n-gram and residue pairs for protein remote homology detection , 2014, BMC Bioinformatics.
[24] Bin Liu,et al. QChIPat: a quantitative method to identify distinct binding patterns for two biological ChIP-seq samples in different experimental conditions , 2013, BMC Genomics.
[25] Hareton K. N. Leung,et al. A Highly Efficient Approach to Protein Interactome Mapping Based on Collaborative Filtering Framework , 2015, Scientific Reports.
[26] James G. Lyons,et al. Probabilistic expression of spatially varied amino acid dimers into general form of Chou׳s pseudo amino acid composition for protein fold recognition. , 2015, Journal of theoretical biology.
[27] R. Ozawa,et al. A comprehensive two-hybrid analysis to explore the yeast protein interactome , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[28] B. Liu,et al. Identification of Real MicroRNA Precursors with a Pseudo Structure Status Composition Approach , 2015, PloS one.
[29] S T Roweis,et al. Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.
[30] Jason Weston,et al. Mismatch string kernels for discriminative protein classification , 2004, Bioinform..
[31] Yun Gao,et al. Prediction of Protein-Protein Interactions Using Local Description of Amino Acid Sequence , 2011 .
[32] David Haussler,et al. Using the Fisher Kernel Method to Detect Remote Protein Homologies , 1999, ISMB.
[33] Ying-Ke Lei,et al. Face recognition via Weighted Sparse Representation , 2013, J. Vis. Commun. Image Represent..
[34] Yanzhi Guo,et al. Using support vector machine combined with auto covariance to predict protein–protein interactions from protein sequences , 2008, Nucleic acids research.
[35] David A. Gough,et al. Whole-proteome interaction mining , 2003, Bioinform..
[36] Jie Gui,et al. Prediction of protein-protein interactions from protein sequence using local descriptors. , 2010, Protein and peptide letters.
[37] Zhu-Hong You,et al. Using manifold embedding for assessing and predicting protein interactions from high-throughput experimental data , 2010, Bioinform..
[38] Xiaolong Wang,et al. repDNA: a Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects , 2015, Bioinform..
[39] Zhu-Hong You,et al. Increasing the reliability of protein-protein interaction networks via non-convex semantic embedding , 2013, Neurocomputing.
[40] Zhu-Hong You,et al. Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis , 2013, BMC Bioinformatics.
[41] Wen Zhu,et al. Learning a Weighted Meta-Sample Based Parameter Free Sparse Representation Classification for Microarray Data , 2014, PloS one.
[42] N. Ahmed,et al. Discrete Cosine Transform , 1996 .
[43] Ian M. Donaldson,et al. BIND: the Biomolecular Interaction Network Database , 2001, Nucleic Acids Res..
[44] Loris Nanni,et al. An ensemble of K-local hyperplanes for predicting protein-protein interactions , 2006, Bioinform..
[45] James G. Lyons,et al. Advancing the Accuracy of Protein Fold Recognition by Utilizing Profiles From Hidden Markov Models , 2015, IEEE Transactions on NanoBioscience.
[46] Eleazar Eskin,et al. The Spectrum Kernel: A String Kernel for SVM Protein Classification , 2001, Pacific Symposium on Biocomputing.