The Applications of Clustering Methods in Predicting Protein Functions

[1]  Lusheng Wang,et al.  Predicting Human Protein Subcellular Locations by the Ensemble of Multiple Predictors via Protein-Protein Interaction Network with Edge Clustering Coefficients , 2014, PloS one.

[2]  Johan A. K. Suykens,et al.  Optimized Data Fusion for Kernel k-Means Clustering , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Anton J. Enright,et al.  An efficient algorithm for large-scale detection of protein families. , 2002, Nucleic acids research.

[4]  Abas Md Said,et al.  Efficient Feature Selection and Classification of Protein Sequence Data in Bioinformatics , 2014, TheScientificWorldJournal.

[5]  Xiaoming Liu,et al.  Prediction of hot regions in protein-protein interaction by combining density-based incremental clustering with feature-based classification , 2015, Comput. Biol. Medicine.

[6]  M. Samanta,et al.  Predicting protein functions from redundancies in large-scale protein interaction networks , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Qingyao Wu,et al.  Multi-Instance Metric Transfer Learning for Genome-Wide Protein Function Prediction , 2017, Scientific Reports.

[8]  Changhui Yan,et al.  A new protein graph model for function prediction , 2012, Comput. Biol. Chem..

[9]  Wei Zhu,et al.  Semantic and layered protein function prediction from PPI networks. , 2010, Journal of theoretical biology.

[10]  David F. Gleich,et al.  AptRank: an adaptive PageRank model for protein function prediction on bi‐relational graphs , 2016, Bioinform..

[11]  Sebastian Proost,et al.  Predicting protein-protein interactions in Arabidopsis thaliana through integration of orthology, gene ontology and co-expression , 2009, BMC Genomics.

[12]  Ron Shamir,et al.  A clustering algorithm based on graph connectivity , 2000, Inf. Process. Lett..

[13]  Cathy H. Wu,et al.  Predicting Ligand Binding Residues and Functional Sites Using Multipositional Correlations with Graph Theoretic Clustering and Kernel CCA , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[14]  Lei Chen,et al.  Identification of new candidate drugs for lung cancer using chemical–chemical interactions, chemical–protein interactions and a K-means clustering algorithm , 2016, Journal of biomolecular structure & dynamics.

[15]  Shengrui Wang,et al.  A novel hierarchical clustering algorithm for gene sequences , 2012, BMC Bioinformatics.

[16]  Douglas Steinley,et al.  Stability analysis in K-means clustering. , 2008, The British journal of mathematical and statistical psychology.

[17]  Benjamin A. Shoemaker,et al.  Deciphering Protein–Protein Interactions. Part I. Experimental Techniques and Databases , 2007, PLoS Comput. Biol..

[18]  Limsoon Wong,et al.  Exploiting indirect neighbours and topological weight to predict protein function from protein--protein interactions , 2006 .

[19]  Jingyu Hou,et al.  Progressive Clustering Based Method for Protein Function Prediction , 2013, Bulletin of mathematical biology.

[20]  Krzysztof J. Cios,et al.  CLusFCM: an Algorithm for Predicting protein Functions Using homologies and protein Interactions , 2008, J. Bioinform. Comput. Biol..

[21]  Tomonobu Ozaki,et al.  Interaction site prediction by structural similarity to neighboring clusters in protein-protein interaction networks , 2011, BMC Bioinformatics.

[22]  Geng-Ming Hu,et al.  Visualizing and Clustering Protein Similarity Networks: Sequences, Structures, and Functions. , 2016, Journal of proteome research.

[23]  B. McKinney,et al.  Recursive expectation-maximization clustering: a method for identifying buffering mechanisms composed of phenomic modules. , 2010, Chaos.

[24]  Zhen Liu,et al.  Refined phylogenetic profiles method for predicting protein-protein interactions , 2005, Bioinform..

[25]  Spiridon D. Likothanassis,et al.  Predicting overlapping protein complexes from weighted protein interaction graphs by gradually expanding dense neighborhoods , 2016, Artif. Intell. Medicine.

[26]  Alfredo Benso,et al.  A three-way approach for protein function classification , 2017, PloS one.

[27]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[28]  Xiuzhen Huang,et al.  A practical comparison of two K-Means clustering algorithms , 2008, BMC Bioinformatics.

[29]  Chi-Ming Chen,et al.  Clustering and visualizing similarity networks of membrane proteins , 2015, Proteins.

[30]  Jie Zheng,et al.  Identifying protein complexes from heterogeneous biological data , 2013, Proteins.

[31]  Yi Pan,et al.  A Fast Hierarchical Clustering Algorithm for Functional Modules Discovery in Protein Interaction Networks , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[32]  Igor Jurisica,et al.  Protein complex prediction via cost-based clustering , 2004, Bioinform..

[33]  Björn Wallner,et al.  InterPred: A pipeline to identify and model protein-protein interactions , 2016, bioRxiv.

[34]  Hans-Hermann Bock,et al.  Two-mode clustering methods: astructuredoverview , 2004, Statistical methods in medical research.

[35]  Christine A. Orengo,et al.  Protein function prediction using domain families , 2013, BMC Bioinformatics.

[36]  Gesine Reinert,et al.  Predicting and Validating Protein Interactions Using Network Structure , 2008, PLoS Comput. Biol..

[37]  Fengxia Yan,et al.  A Review of Computational Methods for Predicting Drug Targets. , 2016, Current protein & peptide science.

[38]  Peter Langfelder,et al.  Fast R Functions for Robust Correlations and Hierarchical Clustering. , 2012, Journal of statistical software.

[39]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[40]  Hagit Shatkay,et al.  Protein Function Prediction using Text-based Features extracted from the Biomedical Literature: The CAFA Challenge , 2013, BMC Bioinformatics.

[41]  Yi Pan,et al.  Clustering based on multiple biological information: approach for predicting protein complexes. , 2013, IET systems biology.

[42]  E. Ceulemans,et al.  Subspace K-means clustering , 2013, Behavior Research Methods.

[43]  T N Wang,et al.  An improved K-means clustering method for cDNA microarray image segmentation. , 2015, Genetics and molecular research : GMR.

[44]  Kevin D. Reilly,et al.  SEQOPTICS: a protein sequence clustering system , 2006, BMC Bioinformatics.

[45]  Kadim Tasdemir,et al.  Topology-Based Hierarchical Clustering of Self-Organizing Maps , 2011, IEEE Transactions on Neural Networks.

[46]  Tze-Yun Leong,et al.  Fuzzy K-means clustering with missing values , 2001, AMIA.

[47]  Søren Brunak,et al.  Prediction of human protein function according to Gene Ontology categories , 2003, Bioinform..

[48]  Hiroshi Mamitsuka Essential Latent Knowledge for Protein-Protein Interactions: Analysis by an Unsupervised Learning Approach , 2005, TCBB.

[49]  Ujjwal Maulik,et al.  Gene microarray data analysis using parallel point-symmetry-based clustering , 2015, Int. J. Data Min. Bioinform..

[50]  Sonia M Leach,et al.  The topology of the bacterial co-conserved protein network and its implications for predicting protein function , 2008, BMC Genomics.

[51]  Daniel Jaeger,et al.  pyGCluster, a novel hierarchical clustering approach , 2014, Bioinform..

[52]  Juan Cui,et al.  Recent progresses in the application of machine learning approach for predicting protein functional class independent of sequence similarity , 2006, Proteomics.

[53]  Ljupco Kocarev,et al.  Exploring Function Prediction in Protein Interaction Networks via Clustering Methods , 2014, PloS one.

[54]  Krešimir Šolić,et al.  Cluster analysis as a prediction tool for pregnancy outcomes. , 2015, Collegium antropologicum.

[55]  Changiz Eslahchi,et al.  ProDomAs, protein domain assignment algorithm using center‐based clustering and independent dominating set , 2014, Proteins.

[56]  Arun K. Ramani,et al.  Protein interaction networks from yeast to human. , 2004, Current opinion in structural biology.

[57]  Li Liao,et al.  Phylogenetic tree information aids supervised learning for predicting protein-protein interaction based on distance matrices , 2007, BMC Bioinformatics.

[58]  Jin Xu,et al.  A New Method for the Discovery of Essential Proteins , 2013, PloS one.

[59]  Luhua Lai,et al.  Sequence-based prediction of protein protein interaction using a deep-learning algorithm , 2017, BMC Bioinformatics.

[60]  Yong Zhou,et al.  Prediction of Protein–Protein Interactions with Clustered Amino Acids and Weighted Sparse Representation , 2015, International journal of molecular sciences.

[61]  Yi Pan,et al.  Predicting Essential Proteins Based on Weighted Degree Centrality , 2014, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[62]  David Martin,et al.  Functional classification of proteins for the prediction of cellular function from a protein-protein interaction network , 2003, Genome Biology.

[63]  Douglas Steinley,et al.  K-means clustering: a half-century synthesis. , 2006, The British journal of mathematical and statistical psychology.

[64]  K Faez,et al.  Identifying similar functional modules by a new hybrid spectral clustering method. , 2012, IET systems biology.

[65]  Efstratios F. Georgopoulos,et al.  Predicting protein complexes from weighted protein-protein interaction graphs with a novel unsupervised methodology: Evolutionary enhanced Markov clustering , 2015, Artif. Intell. Medicine.

[66]  Moataz A. Ahmed,et al.  Protein complexes predictions within protein interaction networks using genetic algorithms , 2016, BMC Bioinformatics.

[67]  Ryan T Gill,et al.  Cross-species cluster co-conservation: a new method for generating protein interaction networks , 2007, Genome Biology.

[68]  Alan C. Evans,et al.  Early brain development in infants at high risk for autism spectrum disorder , 2017, Nature.

[69]  Siu-Ming Yiu,et al.  Clustering-Based Approach for Predicting Motif Pairs from protein Interaction Data , 2009, J. Bioinform. Comput. Biol..

[70]  Joshua M. Dudik,et al.  A comparative analysis of DBSCAN, K-means, and quadratic variation algorithms for automatic identification of swallows from swallowing accelerometry signals , 2015, Comput. Biol. Medicine.

[71]  I. Pigeot,et al.  A comparison of heuristic and model-based clustering methods for dietary pattern analysis , 2015, Public Health Nutrition.

[72]  Jianxin Wang,et al.  Computational Methods to Predict Protein Functions from Protein-Protein Interaction Networks. , 2017, Current protein & peptide science.